Role of Amylase in Ovarian Cancer
Transcript of Role of Amylase in Ovarian Cancer
University of South FloridaScholar Commons
Graduate Theses and Dissertations Graduate School
July 2017
Role of Amylase in Ovarian CancerMai MohamedUniversity of South Florida, [email protected]
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Role of Amylase in Ovarian Cancer
by
Mai Mohamed
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Department of Pathology and Cell Biology
Morsani College of Medicine
University of South Florida
Major Professor: Patricia Kruk, Ph.D.
Paula C. Bickford, Ph.D.
Meera Nanjundan, Ph.D.
Marzenna Wiranowska, Ph.D.
Lauri Wright, Ph.D.
Date of Approval:
June 29, 2017
Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion
Copyright © 2017, Mai Mohamed
Dedication
This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the
importance of education, and, throughout my education, have been my strongest source of
encouragement and support. They always believed in me and I am eternally grateful to them. I
would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also
like to thank my husband, Ahmed. You have all been a bottomless source of strength,
encouragement, and love throughout my Ph.D. Thank you. I love you. I would like to thank Dr.
Kruk. Your continued support and patience are the reasons I was able to get his far. I would like
to thank Stephanie Buttermore, my lab mate, for keeping me sane on my craziest days.
Acknowledgements
It is a pleasure to thank all of those who made this dissertation possible. I want to sincerely thank
Dr. Patricia Kruk for accepting me into her lab and for her guidance, support, and patience over
the years. The lessons and skills I learned from her during my time her lab will be invaluable in
my future endeavors. I would like to thank my committee members, Dr. Paula C. Bickford, Dr.
Meera Nanjundan, Dr. Marzenna Wiranowska, and Dr. Lauri Wright for their time and valuable
insight on my dissertation research. I would also like to thank Dr. Eric Bennett for welcoming me
into the program, as well as the rest of the Medical Sciences program and Pathology and Cell
Biology departmental staff who have provided support for me during my time at the University of
South Florida. Finally, thank you to all of the Kruk Lab members, past and present. Special thanks
to my current fellow lab member: Stephanie Buttermore for her insight and for always making my
time at the lab such a fun and entertaining experience. Finally, I would like to thank my family
and friends for encouraging me over the last four years. It has been a long road that I would not
have been able to travel on my own.
i
Table of Contents
List of Tables v
List of Figures vi
List of Abbreviations viii
Abstract xiii
Chapter 1 Overview of Ovarian Cancer Biomarkers 1
Ovarian Cancer 1
Literature-derived OC protein biomarkers 3
Cytokines and growth factors 5
Interleukin-6 (IL-6) 5
Interleukin-8 (IL-8) 5
Interleukin-10 (IL-10) 6
Prolactin (PRL) 6
Transforming growth factor beta 1 (TGF-β1) 6
Tumor necrosis factor alpha (TNFα) 7
Vascular endothelial growth factor (VEGF) 7
Structural and extracellular matrix proteins 8
Transmembrane proteins 8
Claudin-3 and -4 8
Mucin 1 (MUC1) 9
Mucin 4 (MUC4) 9
Mucin 16 (CA125) 9
Structural and matrix-related proteins 10
Collagen (COL1A1 and COL11A1) 10
Human plasminogen activator inhibitor-1 (PAI-1) 10
Kallikrein (KLK-10,-11) 11
Matrix metalloproteinases (MMP-2 and -9) 12
Osteopontin (SPP1) 12
Stress induced phosphoprotein 1 (STIP1) 13
Metabolic regulators 13
Apolipoprotein E (ApoE) 13
Fatty acid synthase (FASN) 13
Receptors 14
Folate receptor-α (FLOR1) 14
Proteins of unknown function in ovarian cancer 15
ii
Human epididymis protein 4 (HE4) 15
Mesothelin (MSLN) 15
Prostasin (PRSS8) 16
Shared Computational Characteristics 19
Characteristics of secreted proteins 19
Characteristics of stable proteins 20
Identifying additional key regulators of ovarian cancer 24
Rationale 31
Central Hypothesis 31
Aim 1 32
Aim 2 32
Aim 3 32
Chapter 2 Computational Analysis of Amylase Isozymes Contributing to Ovarian
Cancer 34
Introduction 34
History of the amylase genes 34
Evolution and expression of amylase genes 35
Amylase gene regulation 37
Differentiating amylase isozymes 38
Known amylase characteristics 39
Amylase in cancer 40
Objectives 41
Materials and methods 41
Sequences used in computational characterization 41
Amylase isozyme homology 41
Protein biochemical properties databases 41
Protein disorder 42
Secondary structure prediction 42
Posttranslational modification 43
Transcription regulation by promoter analysis 43
Mutational analysis of the amylase isozymes 43
Clinical specimens 44
Western blot 44
Results 45
Amylase isozymes are highly homologous 45
Amylase isozymes have similar molecular weights and isoelectric points 45
Amylases are highly ordered proteins with one predicted protein binding site 45
Amylase isozymes are hydrophilic 50
Amylase is predicted to be glycosylated and phosphorylated 50
The amylase isozymes have common domains, and structural features 51
Amylase isozymes have unique binding regions for chloride, calcium
and glucose 52
The amylase isozymes have similar secondary and tertiary structure profiles 54
Multiple regions of amylase are prone to aggregation 55
iii
The amylase isozymes functionally interact with metabolic proteins 56
Amylase isozyme expression may be driven by differential promotor
activation 59
AMY2B overexpression is the most likely to be associated with
amplification mutations in cancer 60
Clinical validation confirms elevated levels of AMY2B protein in OC 67
Discussion 69
Chapter 3 Amylase Promotes Ovarian Cancer Cell Invasion In Vitro 79
Introduction 79
Materials and methods 80
Tissue culture 80
Transmission Electron microscope (TEM) 80
Quantitative PCR 81
Western blot 82
Amylase ELISA 82
Amylase activity assay 83
Amylase transfection 83
Invasion assay 84
Glycosaminoglycan (GAG)/proteoglycan quantitative assay 84
Immunogold staining 84
Statistical analysis 85
Results 85
Confirmation of yeast-free cultures 85
OC cells overexpress amylase in vitro 87
Amylase secreted by OC cells is metabolically active 91
Inhibiting amylase decreases OC invasion 93
The gylcocalyx of OC cells is thicker than the glycocalyx of IOSE cells 94
Inhibiting amylase increases GAG production 98
Discussion 99
Chapter 4 Regulation of amylase by spirulina 103
Introduction 103
Materials and methods 104
Tissue culture 104
Microarray 105
Quantitative PCR 105
Western blot 106
Amylase ELISA 106
Invasion assay 107
MTS assay 107
Statistical analysis 107
Results 108
Amylase is a downstream target of spirulina 108
Spirulina downregulates amylase mRNA expression in OC cell lines 108
Spirulina downregulates amylase protein expression in OC cell lines 109
iv
Spirulina reduces amylase secretion by OC cell lines 112
Spirulina decreases the invasive capacity of OC cells 113
Spirulina decreased migration of OVCAR5 cells 113
Spirulina does not alter OC proliferation 115
Phycocyanin abrogates amylase RNA expression 116
Spirulina-induced inhibition of OC invasion is driven, in part, by
Phycocyanin 117
Discussion 118
Chapter 5 Concluding Remarks 121
Chapter 6 References 126
Appendix I Potential novel regulators or biomarkers of OC – 683 proteins in total
of non-redundant, secreted, ordered and aggregation-prone
human proteins. 176
v
List of Tables
Table 1.1 Functional and clinical characteristics of OC biomarkers 4
Table 1.2 Computational characteristics of OC biomarkers 17
Table 1.3 Proteins that interact with literature reported ovarian cancer biomarkers 28
Table 2.1 Biochemical properties of amylase determined by computational analyses 47
Table 2.2 Secondary structural and posttranslational modifications among
amylase isozymes 51
Table 2.3 Proteins that functionally interact with amylase isozymes 58
Table 2.4 Distinguishing computational characteristics among amylase isozymes 72
Table 3.1 AMY1 and AMY2B are typically overexpressed in cancer cells 91
Table 4.1 Spirulina transcriptionally targets amylase 108
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List of Figures
Figure 1.1 OC biomarkers have functional interactions 25
Figure 1.2 Summarial schematic identifying additional protein regulators of OC 26
Figure 1.3 Identification of proteins that interact with literature-derived OC biomarkers 30
Figure 2.1 Schematic of mammalian amylase gene distinction 36
Figure 2.2 Amylase proteins are highly homologous 46
Figure 2.3 Amylase isozymes are ordered proteins 48
Figure 2.4 Amylase isozymes are hydrophilic 49
Figure 2.5 Amylase isozymes share structural features 53
Figure 2.6 AMY2A has a unique 3D structure 54
Figure 2.7 Amylase isozymes have multiple aggregation prone regions 55
Figure 2.8 Amylase isozymes form functional interactions with other metabolic enzymes 57
Figure 2.9 AMY2B has unique potential transcription factors 61
Figure 2.10 Mutational events are highest in AMY2B 62
Figure 2.11 AMY2B is altered in most cancer types 63
Figure 2.12 AMY2B is the most expressed amylase isozyme in OC 66
Figure 2.13 Serum levels of AMY2B protein are altered in OC 68
Figure 3.1 Establishing cell cultures are free of yeast 86
Figure 3.2 Amylase AMY1 and AMY2B RNA are typically expressed in OC cell lines 88
Figure 3.3 Most non-OC cell types express AMY1 and AMY2B RNA 88
vii
Figure 3.4 AMY1 and AMY2B protein are overexpressed in OC cell lines 89
Figure 3.5 OC cells produce more amylase than normal cells 92
Figure 3.6 OC cells secrete more metabolically active amylase than IOSE cells 93
Figure 3.7 Abrogation of amylase reduces invasion in OC cells 95
Figure 3.8 The glycocalyx of OC cells is thicker than IOSE cells 96
Figure 3.9 Amylase can be localized to the OC cells glycocalyx/cell surface 97
Figure 3.10 Amylase promotes GAG digestion 98
Figure 4.1 Spirulina downregulates most amylase isozymes in cancer cells 110
Figure 4.2 Spirulina transcriptionally downregulates amylase in OC cell lines 111
Figure 4.3 Spirulina reduces amylase protein levels in OC cell lines 111
Figure 4.4 Spirulina reduces amylase secretion in OC cell lines 112
Figure 4.5 Spirulina decreased invasive capacity in OC cells 113
Figure 4.6 Spirulina reduced migration in OVCAR5 cells 114
Figure 4.7 Spirulina treatment did not alter IOSE or OC cell survival and
Proliferation 115
Figure 4.8 Phycocyanin abrogates amylase expression in OC cells 116
Figure 4.9 Spirulina driven inhibition of OC invasion is partly mediated by
phycocyanin 117
Figure 5.1 Schematic of the possible influence of amylase in altering the glycocalyx
of OC cell and consequently lead to a more malignant phenotype. 124
viii
List of Abbreviations
AGL Glycogen debranching enzyme
AKAP1 Kinase A anchor protein 1
AKAP8 Kinase A anchor protein 8
AMY Amylase
ApoE Apolipoprotein E
ATP1A1 ATPase, Na+/K+ transporting, Alpha 1 polypeptide
ATP1A4 ATPase, Na+/K+ transporting, Alpha 4 polypeptide
BRCA1/2 BReast Cancer gene 1/2
BPI Bacterial/perimeability-increasing protein
CAC Cacodylate
CCM Concentrated conditioned media
CD2 CD molecule
cDNA Complementary DNA
cdx-1 Caudal Type Homeobox (transcription factor) 1
C/EBP CCAAT/enhancer-binding protein
CLDN3 Claudin-3
CLDN4 Claudin-4
COL1A1 Collagen, type I, alpha I
COL10A1 Collagen, type X, alpha 1
ix
COL11A1 Collagen, type XI, alpha 1
CPE Clostridium perfringens enterotoxin
CSSP Consensus Secondary Structure Prediction
CT Computed tomography
Ct Threshold cycle
DEPP Disorder enhanced phosphorylation predictor
ECM Extracellular matrix
ENO1 Enolase 1
EOC Epithelial ovarian cancer
EPD Eukaryotic Promoter Database
ETOH Ethanol
FASN Fatty Acid synthase
FBS fetal bovine serum
FOLR1 Folate receptor-α
FT Fallopian tube
GA Glutaraldehyde
GAG Glycosaminoglycans
GBE1 Glucan (1,4-alpha-) branching enzyme 1
GEMM Genetically engineered mouse models
GP2 Glycoprotein 2
GRAVY Grand average of hydropathy
GRE General transcriptional enhancer
HE4 Human epididymis protein-4
x
HDL high-density lipoprotein
HLDL Very low-density lipoproteins
HOSE Human ovarian surface epithelial
HRP Horseradish peroxidase
IL-6 Interleukin-6
IL-8 Interleukin-8
IL-10 Inerleukin-10
iNOS inducible nitric oxide synthase
IOSE Human ovarian surface epithelial
IP Intraperitoneal
IP3 Inositol trisphosphate
KLK10 Kallikreins-10
KLK11 Kallikreins-11
LCT Lactase
LDL Low-density lipoprotein
LTR Long terminal repeat
MGAM Maltase-glucoamylase
MRI Magnetic resonance imaging
MMP-2 Metalloproteinase-2
MMP-9 Metalloproteinase-9
MSLN Mesothelin
MUC1 Mucin 1
MUC4 Mucin 4
xi
MUC16 Mucin 16
NACB National Academy of Clinical Biochemistry
NADPH Nicotinamide adenine dinucleotide phosphate
NO Nitric oxide
OC Ovarian cancer
OSE Ovarian surface epithelial
OsO4/0.8% K4Fe(CN)6 Osmium tetroxide-potassium ferrocyanide
PAI-1 Human plasminogen activator inhibitor-1
PCR Polymerase chain reaction
PET Positron emission tomography
PFA Paraformaldehyde
PGM1 Phosphoglucomutase 1
pI Isoelectric point
PKC Protein kinase C
PLC Phospholipase C
PLCO Prostate, Lung, Colorectal and Ovarian screening trial
PNLIP Pancreatic lipase
POU1F1 Pituitary-Specific Positive Transcription Factor 1
PRL prolactin
PRSS8 Prostasin
PVDF Polyvinylidene fluoride
PYGB Glycogen phosphorylase, brain form
PYGL Glycogen phosphorylase, liver form
xii
RNA Ribonucleic acid
ROS Reactive oxygen species
SDS-PAGE Sodium dodecyl sulfate polyacrylamide gel electrophoresis
SERPINE1 Human plasminogen activator inhibitor-1
sGAG Sulfated glycosaminoglycans
SI Sucrose-isomaltase
SMAD Mothers against decapentaplegic homolog 3
SPP1 Osteopontin
STAT3 Signal transducer and activator of transcription 3
STAT5A Signal transducer and activator of transcription 5A
STIP1 Tumor stress-induced phosphoprotein
TAS Transabdominal ultrasound
TBST Tween 20-Tris buffered Saline
TG Triglycerides
TGF Transforming growth factor
TGF-β1 Transforming growth factor beta 1
TNFα Tumor necrosis factor alpha
TSHB Thyroid Stimulating Hormone, Beta
TVS Transvaginal ultrasound
VEGF Vascular endothelial growth factor
WFDC2 WAP Four-Disulfide Core Domain 2
HE4 Human epididymis protein-4
Zic1/2 Zic Family Member 1/2
xiii
Abstract
Ovarian cancer (OC) accounts for 4% of all cancer cases and 4.2% of all cancer deaths
worldwide. OC is the most lethal gynecological cancer because it lacks early disease symptoms
and does not have a specific diagnostic marker. As a result, more than 70% of OC patients are
diagnosed in later stages when the disease has already metastasized and the 5-year survival rate
has decreased to less than 20% compared with approximately 90% survival for women diagnosed
with early stage disease. Therefore, I initiated my studies with a computational analysis of the 27
most commonly reported literature-derived ovarian cancer (LDOC) protein biomarkers. I found
that LDOC protein biomarkers share many biochemical features including a preponderance for a
stable protein structure, the ability to be secreted, and functionality related to extracellular matrix
(ECM) modification, immune response and/or energy production. Subsequently, I analyzed the
human proteome to identify proteins that also share these biochemical features. Of the 70,616
proteins in the human proteome, 683 proteins were found to have similar biochemical features to
the 27 LDOC proteins. I also identified a subset of 21 potential additional protein regulators of
ovarian cancer (APROC) that interact with LDOCs. Three of the APROCs identified were amylase
proteins AMY1A, AMY2A, and AMY2B which cleaves alpha 1, 4-glycosidic bonds in
polysaccharides. Amylase is reportedly overexpressed in and secreted by ovarian tumors but its
functional contribution to OC remains unknown[1]. In this thesis, I posit that amylase contributes
xiv
to OC invasion. I initiated my studies by computational characterizing the different amylase
isozymes to predict which amylase isozyme(s) is most likely overexpressed in and contributory to
OC invasion. I found that AMY1 and AMY2B have unique regions of disorder and unique
phosphorylation sites indicating that AMY1 and AMY2B would be more likely to interact with
other proteins, and to be easily secreted. Using OC patient serum samples, I was able to validate
AMY1 and AMY2B overexpression by western immunoblotting.
I then developed an in vitro model system to study the molecular contribution of amylase
to OC invasion using normal ovarian surface epithelial (IOSE) and OC cell lines. I showed that
OC cells generally overexpress and secrete metabolically active amylase isozymes AMY1 and
AMY2B. Abrogating amylase activity using siRNA silencing technology decreased the capacity
of OC cells to invade collagen coated Boyden chambers and increased sulfated
glycosaminoglycans (sGAG) production. Since a survey of OC cell lines indicated that cancer cells
have a bulkier glycocalyx compared to IOSE cells and immunogold labeling studies indicated the
presence of amylase within the immediate OC microenvironment, my data suggest that, by
cleaving alpha 1, 4-glycosidic bonds in glycoconjugates present within ECM, amylase may
remodel the ECM to promote an invasive cancer phenotype. Amylase is therefore a target for
therapeutic intervention in OC patients with hyperamylasemia. I established Spirulina, a dietary
supplement, as a novel transcriptional inhibitor of amylase. Spirulina inhibited amylase expression
in OC cell lines at both the message and protein levels. Spirulina reduced OC cell invasion and
migration in vitro, putatively by decreasing amylase expression.
1
Chapter 1
Overview of Ovarian Cancer Biomarkers
Ovarian Cancer
Ovarian cancer (OC) is the 5th leading cause of cancer death in women in the US after lung,
bronchial, breast, colorectal, and pancreatic cancers [2]. OC represents the most lethal
gynecological cancer [3]. Approximately 22,000 new cases are diagnosed each year and
approximately 14,000 women die from OC each year [2]. While the causes of OC remain
unknown, risk factors for OC include Caucasian race, age, increased number of ovulatory cycles
associated with early menarche, late menopause, nulliparity [4], family history of breast and/or
ovarian cancer, and BReast CAncer genes 1 and 2 (BRCA1/2) mutations [5–7].
A precursor of epithelial OC has not been identified [8,9] and the site of origin of OC remains
unknown [10]. OC is thought to arise from the ovarian surface epithelium [11,12], a specialized
mesothelial layer of cells derived from the coelomic epithelium [10] that covers and protects the
ovary [13]. The coelomic epithelium also gives rise to the epithelia of the peritoneum, the fallopian
tubes (FTs) and the uterus and expresses mesenchymal markers Vimentin and N-Cadherin [14].
Further, the common embryonic origin of the epithelia lining the majority of the female
reproductive tract is reflected in histologic subtypes of OC [15] such that serous, mucinous and
endometrioid OCs histologically mimic the epithelium lining the FT, cervix and uterus,
respectively [16]. Recent studies suggest the FT is an alternate source of OC [10], because serous
2
carcinoma, the most frequent OC subtype, resembles the FT [17]. High grade serous OCs account
for much of the mortality associated with OC due to their aggressive phenotype evidenced by
disease relapse following initial treatment and emergence of drug-resistant disease. [18–20].
Lastly, some OC may arise from extra-ovarian origins, i.e. clear cell OC mimics renal clear cell
carcinoma and mucinous OC bears a resemblance to the gastrointestinal tract [20,21].
Symptoms of OC are generally vague and unspecific; symptoms include bloating, pelvic
discomfort, gas pains and a change in urinary frequency. OC symptoms appear during early disease
stages, but the symptoms are usually mistaken for benign intestinal, musculoskeletal, or
gynecologic conditions. Eighty-nine percent of OC patients experience symptoms during the first
stages of disease and 97% of OC patients experience symptoms in the later stages of disease.
Unfortunately, the non-specific nature of symptoms and a lack of a specific screening test results
in OC generally being diagnosed when it has already metastasized [5].
Current modalities for the detection of OC include pelvic examination, transvaginal ultrasound,
transabdominal ultrasound, and serum CA125 levels (see below). Other imaging methods include
computed tomography, magnetic resonance imaging, and positron emission tomography.
However, none of these modalities is sufficiently sensitive or specific to identify OC, especially
in early stage disease when the 5-year survival rate can be as high as 90% [4,22]. As a result, the
5-year survival for OC patients diagnosed at late stages is generally no better than 30% [23].
Consequently, identifying new screening and detection methods as well as further elucidating the
etiology of OC remains imperative to improving OC patient survival [24].
3
Literature-derived OC protein biomarkers
A number of OC biomarkers has been reported to date and include biomarkers found elevated
in the serum, urine and/or tissues of OC patients [25]. These biomarkers may have not only
diagnostic and prognostic clinical value as indicators of survival time, drug resistance and
recurrent disease, but also serve as possible therapeutic targets. OC biomarkers have been
associated with molecular and cellular processes that include inflammation, cellular movement,
proliferation and cell death. Many OC biomarkers also play a role in cancer development by
regulating metastasis, angiogenesis, immune responses, cell cycle regulation and inflammation
[26].
In order to identify the most prominent protein biomarkers associated with OC, a review of the
literature was carried out and monomer protein OC biomarkers with more than 100 publications
in Pubmed or proteins explored by the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening
trial and/or the National Academy of Clinical Biochemistry (NACB) due to their potential
diagnostic and prognostic utility were selected for further review by computational analyses. The
resulting set of 27 biomarkers identified are typically overexpressed in OC patient serum and tissue
and are associated with poor clinical outcome (Table 1). These biomarkers fell within the following
categories:
(1) Cytokines and growth factors: interleukin-6 (IL-6), interleukin-8 (IL-8/ CXCL8), interleukin-
10 (IL-10), prolactin (PRL), transforming growth factor beta 1 (TGF-β1), tumor necrosis factor
alpha (TNFα) and vascular endothelial growth factor (VEGF);
(2) Structural and Extracellular matrix (ECM) related proteins: claudin 3,4 (CLDN3,4),
collagen1A1, 11A1 (COL1A1 and COL11A1), human plasminogen activator inhibitor-1
(SERPINE1), kallikreins-10, -11 (KLK-10, -11), metalloproteinase-2, -9 (MMP-2, -9), mucin 1
4
(MUC1), mucin 4 (MUC4), mucin 16 (MUC16), osteopontin (SPP1) and phosphoprotein 1
(STIP1);
(3) Metabolic regulators: apolipoprotein E (ApoE) and fatty acid synthase (FASN);
(4) Receptors: folate receptor-α (FOLR1);
(5) Proteins of unknown functions in cancer: human epididymis protein 4 (WFDC2/HE4),
mesothelin (MSLN), and tumor stress-induced prostasin (PRSS8).
Table 1.1: Functional and clinical characteristics of OC biomarkers
Protein
Protein expression Clinical outcomes
Ref
eren
ces
Ser
um
Tis
sue
Cy
stic
flu
id
Asc
ites
OC
cel
ls
Uri
ne
Can
cer
cell
pro
life
rati
on
Met
asta
sis/
inv
asio
n
Dru
g
resi
stan
ce
Red
uce
d
ov
eral
l
surv
ival
An
gio
gen
esis
Po
or
pro
gn
osi
s
Mar
ker
of
dis
ease
recu
rren
ce
Cytokines and growth factors
IL-6 [27–34] IL-8 [35–37] IL-10 [30,38,39] PRL [40–44] TGF-β1 [30,45–48] TNFα [49–53] VEGF [54–57] Adhesion proteins/proteases/ receptors
CLDN3 [58–66] CLDN4 [58–66] COL1A1 [67–69] COL11A1 [69–71] FOLR1 [72]
SERPINE
1
[73–76] KLK10 [77–91]
KLK11 [77–91] MMP2 [92–95] MMP9 [92–95] MUC1 [96–98] MUC4 [99–101] MUC16 [102–104] SPP1 [87,105–109] STIP1 [110–117] Metabolic Regulators
ApoE [89,118]
FASN [119–126] Proteins of unknown function
WFDC2 [87,127,128] MSLN [129–132] PRSS8 [133]
Grey shading indicates notable protein expression or reported clinical outcome.
5
Herein follows a brief review of these 27 proteins describing their diagnostic, prognostic,
and clinical use as well as their potential contribution and function in the etiology of OC.
Cytokines and growth factors
While both cytokines and growth factors regulate the immune response, cytokines are small
polypeptides secreted from cells that stimulate proliferation, survival and differentiation of other
cells through cytokine receptors. Chemokines, a subtype of cytokines, attract cells to sites of
inflammation, thereby influencing cell migration. Cytokines overexpressed in OC enhance cancer
progression through increased cell survival, migration and chemoresistance [134].
Interleukin-6 (IL-6)
IL-6 is predominately a pro-inflammatory cytokine secreted by T-cells and macrophages to
stimulate the immune response [135]. However, in cancer cells, IL-6 may also promote metastasis
through down-regulation of E-cadherin, thereby inducing epithelial-mesenchymal transition
(EMT) [136]. IL-6 overexpression in OC cells results in an upregulation of anti-apoptotic
regulators and matrix metalloproteinase-9 leading to increased cancer cell proliferation and
invasion, respectively [31,34]. Elevated serum IL-6 is also associated with higher OC stage and
reduced chemotherapeutic responsiveness [32,33].
Interleukin-8 (IL-8)
IL-8 is a chemokine produced by macrophages, endothelial cells and a number of epithelial
cells [137]. IL-8 overexpression increases OC cell proliferation [35] by activating the PI3K/Akt
and Raf/MEK/ERK pathways and altering cell cycle regulating proteins. In addition, IL-8
overexpression appears to enhance OC invasion through an upregulation of matrix
metalloproteinase (MMP2/9 – see below) expression and activity [36].
6
Interleukin-10 (IL-10)
IL-10 is an anti-inflammatory cytokine that mediates the immune response through signal
transducer and activator of transcription 3 (STAT3) signaling [138] and effectively inhibits
bacterial-mediated induction of pro-inflammatory cytokines [139,140]. IL-10 levels are elevated
in the serum and ascites fluid of OC patients where it is thought to serve as a useful prognostic
biomarker indicative of disease recurrence [30,39] and worse overall survival [38] by diminishing
the immune response in OC patients.
Prolactin (PRL)
Secreted by the pituitary, PRL is best known for its role to stimulate milk production [141].
However, specific PRL isoforms secreted by monocytes have been shown to enhance the
inflammatory response [142]. Further, by promoting endothelial cell migration and angiogenesis
via enhanced ERK1/2 and STAT5 signaling as well as enhancing breast cancer cell migration
through modulation of the actin cytoskeleton, PRL is believed to promote breast cancer
progression [40,41]. Prolactin is a circulating cytokine/hormone secreted by ovarian follicular cells
[42]. PRL is significantly elevated in OC patient serum [43] where it is believed that increased
PRL expression increases OC PRL receptor expression leading to enhanced OC proliferation via
activation of the MAPK/ERK1 pathway [42]. Further, PRL acts as a tumorigenic agent by
activating Ras in normal ovarian epithelial cells while also protecting these cells from apoptosis
following chemotherapeutic treatment [42]. Lastly, PRL overexpression can enhance OC
migration via reduction of E-cadherin expression [44].
Transforming growth factor beta 1 (TGF-β1)
TGF-β1, a member of the transforming growth factor beta family, is a highly pleiotropic
cytokine involved in cell growth, proliferation, differentiation and apoptosis through Mothers
7
against decapentaplegic homolog 3 (SMAD3) signaling in OC [143–145]. TGF-β1 promotes
tumor cell-mediated ECM remodeling. TGF-β1 increases proteinase and plasmin expression by
tumor cells which creates a positive regulatory feedback loop that increases TGF-β1 activation and
release with subsequent ECM degradation. TGF-β1 expression also upregulates MMP expression
leading to increased ECM remodeling [146]. Elevated TGF-β1 serum levels parallel OC stage and
correlate with metastatic disease [30]. Likewise, OC patients with drug resistant disease also
demonstrate elevated serum TGF-β1 [45] so that OC patients with TGF-β1 overexpression have
poor prognosis [46]. Overexpressed TGF-β1 stimulates tumor growth and angiogenesis in
xenograft OC models [47] and OC metastasis through MMP-2 activation [48].
Tumor necrosis factor alpha (TNFα)
TNFα is typically produced by macrophages and lymphocytes to control immune cells during
the immune response [137]. However, TNFα is a multifunctional cytokine mediating diverse
cellular effects including apoptosis, angiogenesis and cell migration [50,51]. Consequently, TNFα
has been implicated in both anti- and pro-tumorigenic roles [147]. TNFα is overexpressed in OC
tissue [49]. Since TNFα overexpression modulates the expression of other cytokines, including IL-
6 and IL-8, TNFα may mediate inflammatory stimuli that contribute to OC tumorigenesis [52].
TNFα can also act as an endogenous tumor promoter contributing to cellular transformation,
invasion, dissemination and angiogenesis in OC [50,53].
Vascular endothelial growth factor (VEGF)
The VEGF family consists of endothelial cell mitogens that enhance endothelial cell growth
and migration following binding to cell surface receptors that trigger activation of receptor tyrosine
kinases [148]. Secreted VEGF recruits and promotes progenitor endothelial cell growth and
migration that create leaky and permeable cancer vasculature for nutrient transportation and waste
8
removal necessary to the survival, proliferation, invasion and metastasis of cancer cells. VEGF
also acts as an anti-apoptotic factor for recruited endothelial cells [54]. VEGF released by and
sequestered in the ECM can be activated by extracellular cleavage by MMPs. Active VEGF then
binds to ECM collagens and proteoglycans in the ECM that then remodel the ECM to enhance
vascular branching and capillary density [149]. Overexpression of VEGF in OC tissue and patient
serum is associated with poor overall survival and can be indicative of tumor recurrence [55].
Serum VEGF levels have been used to predict patients’ response to anti-angiogenic treatment [56].
VEGF levels in the ovarian cyst fluids are elevated in malignant OC tumors compared with benign
or borderline cysts and tumors and inhibiting VEGF expression can lead to suppression of tumor
growth [57].
Structural and extracellular matrix (ECM) related proteins
Transmembrane proteins
Claudin-3 and -4
Claudins (20-27 kDa), a family of transmembrane proteins, play a central role in tight junction
formation, thereby maintaining epithelial integrity [58]. Both claudin-3 and claudin-4 have been
reported to be overexpressed in OC cells [58–65] and associated with increased cancer cell
survival, invasion and motility which may be mediated, in part, through increased MMP-2 activity
(see below). Claudins also play a role in drug resistance since claudin-4 is overexpressed in
cisplatin-resistant OC cells [66]. Claudin-3 and claudin-4 also act as specific receptors of
Clostridium perfringens enterotoxin (CPE) by mediating endotoxin-dependent cell lysis
[150,151]. Treatment of xenograft ovarian tumors with CPE-based therapies results in inhibited
tumor growth suggesting that claudin-3 and claudin-4 may serve as potential targets for therapeutic
intervention in OC [89,152].
9
Mucin 1 (MUC1)
MUC1, also known as CA15-3, is a highly glycosylated transmembrane glycoprotein that
facilitates invasion, metastasis, immune system evasion, drug resistance, and survival via the ErbB
and β-catenin-Wnt pathways [96–98]. MUC1 is overexpressed on OC cell surfaces and in OC
patient serum. Compared with normal cells, MUC1 expressed by cancer cells is structurally
different – it is attached to shorter and less dense O-glycan moieties, thereby unmasking novel
protein core epitopes that can be exploited to create antibodies differentiating between normal and
diseased cells. MUC1 is overexpressed in over 90% of OC, but only 5% of normal ovarian tissue.
Mucin 4 (MUC4)
MUC4 is a highly glycosylated glycoprotein that lubricates epithelial cell surfaces. MUC4 is
also a signal modulator that can alter tumor phenotype by reorganizing actin indirectly by HER2-
mediated pathways [153]. MUC4 expression was reported to be upregulated in 100% and 88% of
early (stage I and II) and advanced (stage III and IV) OC samples, respectively – with an overall
incidence of 92% in OC samples. Though MUC4 plays a diagnostic role, there is no prognostic
correlation of MUC4 expression with prolonged patient survival [99]. OC cells in effusions have
higher MUC4 expression when compared with solid OC primary tumors and metastases [100].
MUC4-expressing OC cells demonstrate an enhanced motility due to the formation of
lamellipodia, filopodia and microspikes [101].
Mucin 16 (CA125)
Mucin16, a transmembrane mucin known as CA125, is a widely used serum marker of OC
[102]. However, CA125 can be elevated in benign conditions, including menstruation,
endometriosis and pregnancy as well as in malignancies of the liver, lung, bladder and pancreas
contributing to false positive results. In healthy adults, CA125 can be expressed in coelomic and
10
Mullerian epithelium, as well as the pancreas, colon, gall bladder, lung, kidney, and stomach
epithelia. While CA125 values below 35U/L are considered normal, CA125 is only elevated in
50% of stage I OC patients leading to many false negative results. CA125 is elevated in 75-90%
of advanced stage ovarian disease and CA125 is elevated in approximately 80% of epithelial OCs.
[103,104]. CA125 plays a role in OC peritoneal metastasis by interacting with mesothelin. CA125
also plays an immunosuppressive role in OC by inhibiting natural killer cells’ cytotoxic responses
[98].
Structural and matrix-related proteins
Collagen (COL1A1 and COL11A1)
Collagens are abundant fibrous proteins found in the ECM. Collagen I (COL1A1) is the most
abundant structural constituent of the ECM of OC and has been shown to enhance OC adhesion
and proliferation by interacting with integrin receptors α3β1 and α6β1 that then activate the Ras,
Erk and Akt pathways in OC cell lines [154]. Collagen XI (COL11A1), which is absent in most
tissues, is overexpressed in OC ECM and OC patient serum potentially serving as one of the
earliest indications of cancer initiation and progression [67,69]. COL11A1 overexpression, as
triggered by TGF-β1 activation and SMAD2 signaling, promotes MMP3 activation in OC [68].
COL11A1 overexpression enhances OC migration and invasion [70]. The overexpression of
collagen by OC also serves to reduce chemotherapeutic drug efficacy by inhibiting drug
penetration/access to OC cells, therefore, contributing to drug resistance and OC tissue survival
[71].
Human plasminogen activator inhibitor-1 (PAI-1)
PAI-1, also known as SERPINE1, is a serine protease inhibitor that plays a role in tissue
remodeling [75]. Inactive plasminogen is converted to broad spectrum serine protease plasmin by
11
urinary (uPA) or tissue-type (tPA) plasminogen activators (PAs) which then cleave ECM
components such as fibronectin, laminin, vitronectin, proteoglycans, and fibrins. PAs are regulated
by plasminogen activator inhibitors (PAI) which curb plasmin generation [155]. PAIs prevent the
cleavage of plasminogen by binding to the active site of PA [156]. PAI is overexpressed in OC
patient serum [73] and tissue [74]. PAI-1 overexpression is associated with proliferation and PAI-
1 inhibitors can act as anticancer agents. PAI-1 expression is associated with poor prognosis [75]
and lower overall survival [73]. Overexpressed PAI-1 has been correlated with higher grade OC
tumors and a higher probability of disease recurrence [76].
Kallikrein (KLK-10, -11)
Kallikriens (KLKs) comprise a family of 15 secreted serine proteases that are dependent upon
a serine in their catalytic site. Secreted into the tumor microenvironment, KLKs, which are thought
to be stimulated by a steroid hormone–driven cascade [84], are involved in tissue remodeling and
neoplastic disease progression [77–79] by promoting ECM degradation, cellular invasion and
metastasis [80–83]. Kallikrein-4, -5, -6, -7, -8, -10, -11, -13, -14, and -15 are overexpressed in OC
tissues, serum, and cell lines at the mRNA and protein levels. Kallikrein-4, -6, and -10 are highly
expressed in serous epithelial ovarian tumors, and kallikrein-5, -11, and -13 are found in non-
serous ovarian tumors. Kallikrein–8, -10, -11, -13 and -14 have also been found in ascites fluid of
OC patients [84]. Further, kallikrein-10 and -11, which are promising OC serum biomarkers are,
respectively, elevated in approximately 56% and 70% of OC patients serum and tissue [85–88].
Upregulation of KLKs in OC has been associated with worse overall survival, although conflicting
reports indicate that further research is required [89–91].
12
Matrix metalloproteinases (MMP-2 and -9)
MMPs consist of a group of 23 proteins that play an important role in tumor progression by
degrading the basement membrane and ECM components; MMP2 cleaves fibronectin, vitronectin
and collagen I [157] into smaller fragments that enhance adhesion of OC cells via α5β1 (fibronectin
receptor) and αVβ3 (vitronectin receptor) integrin binding [158] and MMP9 degrades E-cadherin
which is involved in cell-cell adhesion and differentiation [93]. MMP-2 and -9 play a role in tumor
metastasis, migration, angiogenesis, cell proliferation and cell–cell interactions [92,93], therefore,
MMP-2 and -9 expression has been associated with poor survival [94]. Differential MMP
expression in OC tissue can be used to distinguish different OC subtypes and determine prognosis;
MMP-2 and -9 are more highly expressed in serous OC than mucinous OC tumors. MMP-2
expression is higher in benign tumors versus borderline and malignant tumors, but MMP-9
expression is higher in malignant tumors versus borderline tumors [95].
Osteopontin (SPP1)
Osteopontin, a glycophosphoprotein found in all body fluids and expressed in the ECM, is
involved in embryonic development, wound healing and tumor development. Osteopontin appears
to promote tumor growth, survival, angiogenesis and metastasis by upregulation of the PI3K/Akt
pathway [105] and MMP-9 activity [87,106,107]. Of clinical significance, osteopontin expression
is higher in OC cells, ovarian tumor tissue and serum of OC patients when compared with healthy
control patients. Osteopontin expression is higher in borderline and invasive tumors than in benign
tumors and normal ovaries, which suggests that osteopontin may be effective to detect early stage
OC [108,109].
13
Stress induced phosphoprotein 1 (STIP1)
Stress induced phosphoprotein 1 (STIP1) forms nuclear scaffolds supporting formation of
protein complexes that participate in transcription, protein folding, signal transduction and cell
cycle regulation [159,160]. STIP1 is elevated in melanoma, glioma, hepatocellular carcinoma and
pancreatic cancer and is believed to induce survival and invasion in cancer by modulating the
SMAD pathway and MMP-2 expression [110–115]. STIP1 is significantly higher in OC patients
tissue and serum versus normal age-matched patients [116,117]. Analysis of STIP1 levels in OC
patients with low CA125 levels revealed that STIP1 levels can be used to detect invasive OC as
an alternative diagnostic marker of OC [161].
Metabolic regulators
Apolipoprotein E (ApoE)
ApoE is an essential component of the plasma lipoproteins responsible for cholesterol
transportation and metabolism. ApoE is mostly found in the liver, brain, adrenal glands, kidney,
and macrophages and is associated with cholesterol-rich proteins [162]. ApoE expression has been
associated with serous OC, but not with borderline serous tumors or normal ovarian surface
epithelial. Inhibiting ApoE expression leads to G2 cell-cycle arrest and apoptosis in ApoE-
expressing OC cells, indicating ApoE is important for OC proliferation and survival [89,118].
Fatty acid synthase (FASN)
FASN, a cytoplasmic enzyme, is involved in de novo fatty acid synthesis via Nicotinamide
adenine dinucleotide phosphate (NADPH)-dependent condensation of malonyl-CoA and acetyl-
CoA into palmitate [119]. Normal adult tissues minimally express FASN, however, FASN
overexpression has been reported in breast, colon, ovarian and prostate cancer [120,163–168] and
is associated with metabolic dysregulation in cancer cells. FASN levels are elevated in OC
14
development, in serous carcinoma [119–121], and especially in recurrent serous carcinomas [122].
Elevated FASN levels parallel cancer aggressiveness, predict poorer overall survival as well as
disease recurrence [119,120,122–126]. FASN-based therapies have been developed for recurrent
OC patients; FASN can be successfully targeted by C93, a FASN inhibitor, to induce apoptosis in
drug resistant OC cells [89,169]. Since FASN is elevated in >75% of OC, the use of therapies
targeting FASN has great potential, but current inhibitors of FASN have limited efficacy due to
cell impermeability and solubility as well as lack of cell selectivity [170,171].
Receptors
Folate receptor-α (FLOR1)
FOLR1, a 38–40 kDa [172] glycosylphosphatidyl-inositol-membrane anchored protein, is
found in the kidneys where it reabsorbs folate (vitamin B9) from the urine filtrate back into the
blood. FOLR1 is normally expressed in the apical surface of epithelial cells [72], especially in the
lung and kidney [172]. FOLR1 is overexpressed in epithelial cancer, including ovarian, renal, lung,
and breast cancers, though its function in cancer is unknown. Overexpressed FOLR1 can be used
to target cancer with therapeutic drugs or imaging agents. FOLR1 is overexpressed in 72% in
primary OC and 81.5% in recurrent tumors and FOLR1 expression is equal in primary, metastatic
and recurrent tumors. Over 90% of non-mucinous OCs overexpress FOLR1, and the level of
FOLR1 correlates with aggressiveness of the malignancy [72]. Secreted functional FOLR1 in the
blood serves as an OC biomarker [172]. While FOLR1 is not a prognostic marker since levels of
FOLR1 do not correlate with survival or tumor response to treatment [173], FOLR1 could deliver
therapeutic drugs to OC tumors. For example, BGC 945, a thymidylate synthase inhibitor, is
transported into the cell via FOLR1 and, therefore, targets cells that overexpress FOLR1 [89,174].
15
Proteins of unknown function in OC
Human epididymis protein 4 (HE4)
The WAP Four-Disulfide Core Domain 2 (WFDC2) gene encodes HE4, a secreted “four-
disulfide core” protein of unknown function. The four-disulfide core protein family is a varied
group of acid- and heat-stable proteins that have many proposed functions including aiding in
sperm maturation and storage [127,175]. HE4 is expressed at low levels in the female reproductive
tract, breast and respiratory system. HE4 is a 13KDa protein that is secreted as a 25kDa
glycosylated protein [176]. The WFDC2 gene is amplified in epithelial OC (EOC) tissue and this
is associated with increased HE4 in OC serum while HE4 expression remains low in normal tissue.
In comparison with CA125, serum HE4 levels have greater specificity in discriminating malignant
disease from benign ovarian disease and between early and late stage disease [127]. Likewise,
preoperative HE4 levels may be used to predict the extent and success of cytoreductive surgery
needed for optimal debulking [87,128]. Overexpression of HE4 in OC patients correlates with
lower overall survival and increased cisplatin resistance. Lastly, in vitro and in vivo HE4
overexpression increases tumor growth and cisplatin resistance in OC cells and tumors such that
targeting HE4 suppresses OC cells and tumor growth. HE4 may function by interacting with
EGFR, IGF1R, and TF HIF1α [176].
Mesothelin (MSLN)
The soluble mesothelin-related peptide (SMRP) gene encodes MSLN, a 40 kDa
glycosylphosphatidylinositol-anchored cell surface molecule, found on the cell surface of
mesothelial cells and overexpressed in the serum and urine of patients with mesotheliomas and
OC [129–131]. MSLN is highly expressed in non-mucinous OCs, including endometrioid, clear
cell, and transitional cell carcinoma as well as pancreatic adenocarcinomas, endometrium
16
carcinoma and lung carcinoma [177]. While the function of MSLN has not been fully elucidated,
MSLN binds to CA125 on tumor cell surfaces which may alter cell-to-cell adhesion and increase
metastasis to the peritoneum [178]. The development of m912, a human monoclonal antibody to
MSLN, imparts tumor cell toxicity [179]. In contrast, other reports suggest that presence of MSLN
after treatment in advanced-stage OC disease patients is associated with prolonged survival
[130,132].
Prostasin (PRSS8)
PRSS8, first isolated from seminal fluid, is expressed at low concentrations in a variety of
tissues including salivary gland, lung, bronchus, kidney, liver, pancreas, and colon, but is absent
in normal testis and ovary [180]. PRSS8 is a secreted prostatic serine protease and potential OC
biomarker [181]. The function of PRSS8 is not completely understood, however, it is thought to
play a role in fertilization [133,182]. Elevated PRSS8 levels in the serum of OC patients have been
detected with the highest level of PRSS8 detected in stage II of the disease [133].
17
*The protein sequences for the OC biomarkers isolated from the literature were retrieved from
Uniprot for bioinformatics analysis. The distribution of intrinsic protein disorder was analyzed
using disorder predictors PONDR® VLXT [183] and PONDR® VSL2 [184,185] which identify
regions of protein disorder and hydropathy. Amylpred 2 [186] was used to find protein sequences
prone to form protein aggregates. Uniprot [187] was used to determine regions of protein
Table 1.2. Computational characteristics of OC biomarkers
Proteins
Biochemical
Characteristi
cs
Posttranslational Modifications
Cellular
localization
Hydro
phil
ic
Ord
ered
Aggre
gat
ed
Gly
cosy
lati
on
Phosp
hory
lati
on
Sum
oyla
tion
Ace
tyla
tion
Sulf
atio
n
Ubiq
uit
inat
ion
Met
hyla
tion
Succ
inyla
tion
Pal
mit
oyla
tio
n
Myri
stoyla
tio
n
Pre
nyla
tion
Sec
rete
d
Mem
bra
ne
Cyto
pla
sm
Cytokines and growth factors
IL-6
IL-8
IL-10
PRL
TGF-β1
TNFα
VEGF
Adhesion proteins/proteases/ receptors
CLDN3
CLDN4
COL1A1
COL11A
1
FOLR1
SERPIN
E1
KLK10
KLK11
MMP2
MMP9
MUC1
MUC4
MUC16
SPP1
STIP1
Metabolic Regulators
ApoE
FASN
Proteins of unknown function
WFDC2
MSLN
PRSS8
18
instability, signal localization, and binding sites. The predicted post-translational modifications of
each protein was elucidated using different programs. Phosphorylation was predicted using
PhosphoSitePlus (PSP) which provides phosphorylation, ubiquitination, acetylation and
methylation information pertaining to each protein as curated from literature and low- and high-
throughput data sources [188]. N-linked glycosylation was predicted using NetNGlyc 1.0 Server
which predicts the acceptor sites of N-linked glycosylation at Asn-Xaa-Ser/Thr (where Xaa is not
Pro) residues. NetNGlyc 1.0 Server predicts glycosylation with 76% overall accuracy [189]. O-
linked glycosylation was predicted using NetOGlyc 4.0 Server which predicts the addition of a
glycan moiety on Ser, Thr and Tyr residues. The NetOGlyc 4.0 determined glycosylated proteins
from mapping the proteome of different human cell lines [190]. Sumoylation was predicted by
SUMOplot Analysis Program. Sumoylation is the reversible attachment of a SUMO protein (11
kDa). SUMO protein is added to a peptide sequence of a hydrophobic residue bound to a lysine
bound to any amino acid followed by an acidic residue [191]. Palmitoylation, the reversible
attachment of a 16-carbon saturated fatty acid to a cysteine reside via a thioester linkage was
predicted vis NBA-Palm [192]. Acetylation was predicted to by NetAcet 1.0 Server which predicts
the addition of acetyl moiety by N-acetyltransferase A [193]. Tyrosine sulfation sites were
predicted by the Sulfinator which uses four different models to recognize sulfated tyrosine residues
located in the N- and C- terminals [194]. Myristoylation was predicted using Myristoylator which
looks for sequences that accept the addition of a myristate to a N-terminal glycine [195].
Prenylation was predicted using PrePS which determines protein farnesylation (15 carbon
polyisopren) as carried out by farnesyltransferases and geranylgeranylation (20 carbon
polyisopren) as carried out by geranylgeranyltransferase 1 and Geranylgeranyltransferase 2. PrePS
recognizes the motifs recognized by prenyltransfeases (the CaaX box and Rab escort protein in the
C-terminal of proteins. Therefore, PrePS recognizes CaaX Farnesylation, CaaX
Geranylgeranylation and Rab Geranylgeranylation [196]. Methylation was predicted using MeMo,
the methylation Modification Prediction Server 2.0 which predicts methylation on lysine and
arginine residues [197].
19
Shared computational characteristics among OC protein biomarkers
All these OC biomarkers have limited diagnostic usefulness. While none are effective at
detecting all OCs, these OC biomarkers may contribute to malignant transformation and/or disease
progression in OC. Therefore, in order to better understand the molecular mechanisms involved in
OC it would be useful to identify other proteins that may also contribute to OC based on similar
biochemical protein characteristics. Therefore, to identify common biochemical features among
the 27 OC biomarkers derived from literature review, computational analyses for measurements
of protein order, hydropathy, posttranslational modifications, aggregation prone regions and
subcellular localization were performed (Table 1.2). As outlined below, computational analyses
indicated that these reported 27 OC protein biomarkers share characteristics of secreted (presence
of export signal sequence, hydrophilic) and stable (ordered, aggregation prone, glycosylated,
sumoylated) proteins.
Characteristics of secreted proteins - (presence of export signal sequence, hydrophilic)
The majority (22/27) of OC biomarkers examined (ApoE, COL1A1, COL11A1, FOLR1,
IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, MSLN, MUC1, MUC4, MUC16, PRL,
PRSS8, SERPINE1, SPP1, TGF-β1, VEGF, WFDC2) are secreted proteins based on the presence
of an export signal sequence while 5/27 (CLDN3, CLDN4, FASN, TNF-α) and only 1/27 (STIP1)
were predicted to be localized at cell membranes or within the cytoplasm, respectively.
Likewise, the vast majority (24/27) of these OC biomarkers (ApoE, COL1A1, CLDN3,
CLDN4, FASN, FOLR1, IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, MSLN, MUC1,
MUC4, MUC16, PRL, PRSS8, SERPINE1, TNF-α, TGF-β1, VEGF, WFDC2) were determined
to be hydrophilic in keeping with a secretory protein profile or function within hydrophilic
cytoplasmic environments. Similarly, almost half, 11/27, (COL11A1, IL-10, MUC1, MUC4,
20
MUC16, MMP2, SPP1, STIP1, TGF-β1, TNF-α, VEGF) of the OC biomarkers were predicted to
be sulfated. Sulfation, the enzyme-catalyzed conjugation of a sulfo group, often to tyrosine
residues, is required by many secreted proteins that traverse the Golgi apparatus for export [198].
Consequently, it is not unexpected that many of the OC biomarkers should be sulfated.
In contrast, only 8/27 (ACTH, ALB, COL1A1, COL11A1, FASN, IL-10, MMP9, TGF-β1)
were predicted to be methylated. Since methylation increases protein hydrophobicity [199–201]
and the vast majority of OC biomarkers are hydrophilic, it is not surprising that few are predicted
to be methylated. In addition, only 4/27 biomarkers (ApoE, COL1A1, FASN, STIP1) were
predicted to be succinylated. While protein succinylation at lysine residues plays a significant role
in protein structure, folding and function due to the addition of a large structural moiety,
succinylation also typically changes protein charge from +1 to −1 [202]. Since secreted proteins
contain a positively charged amino terminal necessary for the export process, low levels of OC
biomarker succinylation (4/27) are consistent with characteristics of secreted proteins [203].
Lastly, only 3/27 (CLDN3, CLDN4, PRL) are predicted to be palmitoylated and none of the
OC biomarkers are predicted to be myristoylated or prenylated. Since palmitoylation,
myristoylation and prenylation anchor proteins to the membrane with potential for increased
interaction with the endoplasmic reticulum [204], the few OC biomarkers predicted to undergo
these posttranslational modifications suggests that they mostly operate without membrane
anchorage.
Characteristics of stable proteins - (ordered, aggregation prone, glycosylated, sumoylated)
In addition to a secretory protein profile, the majority (19/27) of reported OC biomarkers
(CLDN3, CLDN4, FASN, FOLR1, IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, MSLN,
PRL, PRSS8, SERPINE1, TNF-α, TGF-β1, VEGF, WFDC2) were determined to be ordered
21
proteins with over 65% ordered content. Since highly ordered proteins have stable secondary
and/or tertiary structures with low physical binding potential to other proteins, these OC
biomarkers are unlikely to form larger multi-protein complexes [205]. Interestingly, 17/27
(CLDN3, CLDN4, FOLR1, IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, PRL, PRSS8,
SERPINE1, TGF-β1, TNF-α, VEGF, WFDC2) of the OC biomarkers contain amino acid
sequences of which, greater than 30% are considered aggregation prone regions (APR). APRs are
more likely to represent conserved regions and occur in ordered regions within close structural
proximity to catalytic residues and have, therefore, been theorized to stabilize proteins and
contribute to protein function [206].
With regards to possible post-translational modifications in support of stable protein structures,
most (23/27) of the OC biomarkers studied (ApoE, COL1A1, COL11A1, CLDN3, FOLR1, IL-
10, KLK10, KLK11, MMP2, MMP9, MSLN, MUC1, MUC4, MUC16, PRL, PRSS8, SERPINE1,
SPP1, STIP1, TNF-α, TGF-β1, VEGF, WFDC2) are predicted to be glycosylated. Glycosylation
can increase protein stability and modify protein function by increasing thermal stability that
destabilizes the unfolded state of proteins [207], thereby playing a role in many cellular functions
including intercellular communication, adhesion and migration, all of which are important in
cancer progression. Consequently, in addition to an ordered and aggregation prone protein
structure imparting a stable protein confirmation, additional protein stability would be conferred
by glycosylation.
Many of the OC biomarkers (20/27) (ApoE, COL1A1, COL11A1, CLDN3, CLDN4, IL-6,
KLK10, KLK11, MMP2, MMP9, MSLN, MUC1, MUC4, MUC16, SERPINE1, SPP1, STIP1,
TNF-α, TGF-β1, VEGF) are predicted to be phosphorylated. Phosphorylation can alter protein
activity, stability, function by changing serine, threonine or tyrosine residues from 0 to −2 [202]
22
as well as protein interactions [208] by activating intracellular signaling cascades [209].
Phosphorylation is predicted to be a common posttranslational modification in OC biomarkers
because it affects many oncogenic processes, including transcriptional regulation, proliferation and
kinase signaling [210].
Further, the vast majority (22/27) of OC biomarkers (ApoE, COL1A1, COL11A1,CLDN3,
CLDN4, FASN, FOLR1, IL-10, KLK11, MMP2, MMP9, MSLN, MUC4, MUC16, PRL, PRSS8,
SERPINE1, STIP1, TNF-α, TGF-β1, VEGF, WFDC2) are also predicted to be sumoylated.
Sumoylation is a posttranslational modification resulting from the conjugation of Small Ubiquitin-
related MOdifiers (SUMO) to proteins; sumoylated proteins appear to take a part in transcription
[211,212], apoptosis [213], and signal transduction [214]. While the mechanisms that regulate
sumoylation are not well understood, conjugation of SUMO to proteins can alter protein
configuration potentially leading to new protein-protein interactions, inhibition of existing protein-
protein interactions and/or changes to existing protein functions and activity [215], thereby
enhancing functional diversity. In this way, sumoylation can increase protein stability by
promoting the formation of multimeric protein complexes as well as by inhibition of ubiquitination
[216].
In contrast, only 12/27 (ApoE, CLDN3, CLDN4, COL1A1, COL11A1, FASN, MMP9,
MUC1, MUC4, MUC16, STIP1, VEGF) of OC biomarkers were predicted to be acetylated. While
certain forms of protein acetylation can increase protein stability by reducing protein degradation
[217], the principal function of acetylation involves regulation of gene expression. Since the group
of OC biomarkers examined do not typically include transcriptional proteins, it is not surprising
that few might be acetylated. Likewise, only 10/27 (ApoE, CLDN3, CLDN4, COL11A1, FASN,
IL-6, KLK11, PRL, STIP1, TGF-β1) of OC biomarkers were predicted to be ubiquitinated.
23
Ubiquitination marks proteins for degradation. Ordered proteins have fewer ubiquitination and
microRNA targeting sites, and lower mRNA decay rates. Therefore, ordered proteins can persist
within cells at higher levels for longer periods before facing decay [218]. This is consistent with
the prediction that most biomarkers (17/27) are not ubiquitinated.
As might be expected for disease biomarkers, computational biochemical analyses indicated
that the set of published OC protein biomarkers share characteristics of secreted (export signal
sequence, high hydrophilic, low hydrophobicity, low palmitoylation, myristoylation and
prenylation) and stable (ordered, aggregation prone, sumoylated, glycosylated, but not acetylated
or ubiquinated) proteins. However, given the functional diversity of these biomarkers as indicated
above, String [219] analysis was also performed to identify a network of functional interactions
among these established OC biomarkers (Figure 1.1). Interestingly, interactions among these
diverse biomarkers appear functionally related to: ECM/microenvironment modifications (CLDN-
3, CLDN-4, COL1A1, COL11A1, KLK10, KLK11, MMP2, MMP9, MUC1, MUC4, MUC16,
SERPINE1, SPP1); regulation/modulation of the immune response (IL-6, IL-8, IL-10, TNFα) and;
energy production (ApoE, FASN). Since the ECM/ tumor microenvironment encompasses many
components, including stromal cells, ECM components (cytokines, MMPs, integrins, etc.) and
exosomes, it plays an important role in OC dissemination and metastasis through a variety of
mechanisms that include the activation of signaling pathways (AKT and FAK) that induce invasion
and metastasis, the initiation of EMT changes, and the release of proteases and angiogenic factors
that remodel ECM and stimulate microvessel formation [220]. While the immune response
recognizes and eliminates tumor cells, in malignant states, immunosuppression and
immunoediting often prevail leading to unhindered tumor growth. Though increased tumor-
infiltrating leukocyte expression is associated with improved survival, OC is associated with an
24
immunosuppressive environment. Therefore, future therapies might aim to reverse
immunosuppression and modulate the immune response to combat OC [221]. Lastly, besides
endogenous energy production, OC cells induce stromal fibroblasts to secrete energy metabolites
lactate and pyruvate that are then shuttled into the tricarboxylic acid (TCA) cycle in OC cell
mitochondria. This shuttling promotes increased energy production contributing to accelerated
tumor growth and angiogenesis [222].
Identifying additional key regulators of OC
The best OC biomarker in clinical use today is CA-125 which has a number of limitations
including expression in less than 50% of stage I disease and expression that is rarely elevated in
mucinous, endometrioid or clear-cell carcinomas. CA-125 is elevated in advanced-stage OC and
in some benign and malignant conditions and is dependent on additional factors including age,
history of hysterectomy and weight. Though many potential OC biomarkers have been identified
in the literature (see above), none have overcome CA-125’s clinical limitations [26]. However, the
potential biomarkers reported in literature highlight different biological pathways of ovarian
tumorigenesis that could increase our understanding of the etiology of OC. Many of the OC
biomarkers identified so far fall loosely within three functional groups - immune response,
ECM/microenvironment modifications, energy production and metabolism. Therefore, these
protein regulators of OC may be relevant for elucidating mechanisms and pathways of disease
initiation and progression.
25
Figure 1.1. OC biomarkers have functional interactions. Analysis of the interactivity of published
OC biomarkers by String produced a network of predicted associations for these proteins. The
network nodes are proteins, whereas the edges represent the predicted or known functional
associations. An edge may be drawn with up to 7 differently colored lines that represent the
existence of the seven types of evidence used in predicting the associations. A red line indicates
the presence of fusion evidence; a green line - neighborhood evidence; a blue line – co-occurrence
evidence; a purple line - experimental evidence; a yellow line – text mining evidence; a light blue
line - database evidence; a black line – co-expression evidence.
26
Figure 1.2. Summarial schematic identifying additional protein regulators of OC. Proteins
(70,616) of the human proteome were subjected to sequential computational analysis to
identify proteins (21) that are secreted, ordered and aggregation-prone and which interact
with previously reported OC biomarkers (27).
70,616 non-redundant proteins
8,059 proteins have signal peptide
1,848 proteins have signal peptides and are secreted
1,578 proteins have signal pepties, are secreted and ordered
758 secreted proteins with signal peptides, are ordered and have
aggregation-prone regions
Literature review of monomer OC
biomarkers with more than 100
publications in PubMed and proteins
explored by PLCO screening trial
and/or the NACB
Secreted proteins with export signal
peptide sequences that are ordered,
hydrophilic, and have aggregation-
prone sequences
21 proteins that
interact with literature
biomarkers
27 literature
derived
proteins
683
computational
derived
proteins
27
Consequently, expanding upon the characteristics of stable and secreted OC biomarkers
noted above (ordered, aggregation-prone, hydrophilic, export signal peptide sequence and secreted
proteins) potentially involved in immune response, ECM/microenvironment modifications, and
energy production and metabolism, I explored the human proteome to identify additional protein
regulators of OC (Figure 1.2). According to the Uniprot Database, the human proteome contains
70,616 non-redundant proteins, of which 8,059 proteins contain an export signal peptide and of
which 1,848 proteins are secreted. Of these 1,848 proteins 1,578 proteins are ordered and 758 of
the non-redundant proteins have aggregation-prone regions that cover 30% of their total amino
acid composition. Of these proteins, 75 proteins are putative or uncharacterized proteins of
unknown function and, therefore, were removed from the proposed list of proteins to identify 683
proteins as potential protein regulators of OC (Appendix I). Interestingly, of those 683 proteins,
243 proteins are functionally related to the regulation/modulation of the immune response, 235
proteins functionally related to ECM/microenvironment modifications, 120 proteins functionally
related to energy production and metabolism and 86 proteins of unknown function. Subsequent
String analysis demonstrated possible interactions between 27 of the functionally known literature-
based OC biomarkers and 21/683 putative biomarkers (Figure 1.3, Table 1.3) including three
isozymes of amylase, a secreted and stable protein involved in carbohydrate metabolism.
28
Table 1.3: Proteins that interact with literature reported ovarian cancer biomarkers
Protein Protein name Function in cancer
CXCL12
Stromal cell-derived factor 1 (SDF-1) (hSDF-1) (C-X-
C motif chemokine 12) (Intercrine reduced in
hepatomas) (IRH) (hIRH) (Pre-B cell growth-
stimulating factor) (PBSF) [Cleaved into: SDF-1-
beta(3-72); SDF-1-alpha(3-67)]
Tumor growth,
angiogenesis,
immune functions,
inflammation,
invasion [26]
IL2 Interleukin-2 (IL-2) (T-cell growth factor) (TCGF)
(Aldesleukin)
Immune response
[223]
GHR Growth hormone receptor (GH receptor)
(Somatotropin receptor) [Cleaved into: Growth
hormone-binding protein (GH-binding protein)
(GHBP) (Serum-binding protein)]
Tumor growth and
metastasis [224]
IFNG Interferon gamma (IFN-gamma) (Immune interferon) Immune functions,
Inflammation [225]
VWF Von Willebrand Factor Angiogenesis [54]
KDR Kinase Insert Domain Receptor/ Vascular endothelial
growth factor receptor 2
Angiogenesis [54]
PLAT Tissue-type plasminogen activator (t-PA) (t-
plasminogen activator) (tPA) (EC 3.4.21.68)
(Alteplase) (Reteplase) [Cleaved into: Tissue-type
plasminogen activator chain A; Tissue-type
plasminogen activator chain B]
Tumor invasion [155]
PLAU Urokinase-type plasminogen activator (U-
plasminogen activator) (uPA) (EC 3.4.21.73) [Cleaved
into: Urokinase-type plasminogen activator long chain
A; Urokinase-type plasminogen activator short chain
A; Urokinase-type plasminogen activator chain B]
Tumor invasion [155]
HPR Heparanase (EC 3.2.1.166) (Endo-glucoronidase)
(Heparanase-1) (Hpa1) [Cleaved into: Heparanase 8
kDa subunit; Heparanase 50 kDa subunit]
Tumor proliferation
and metastasis [226]
TSLP Thymic stromal lymphopoietin Inflammation [227]
CRLF2 Cytokine receptor-like factor 2 (Cytokine receptor-like
2) (IL-XR) (Thymic stromal lymphopoietin protein
receptor) (TSLP receptor)
Inflammation [227]
AMY1A Alpha-amylase 1 (EC 3.2.1.1) (1,4-alpha-D-glucan
glucanohydrolase 1) (Salivary alpha-amylase)
Cleaves alpha 1, 4-
glycosidic bonds in
polysaccharides; role
in cancer unknown
[228]
AMY2A Pancreatic alpha-amylase (PA) (EC 3.2.1.1) (1,4-
alpha-D-glucan glucanohydrolase)
Cleaves alpha 1, 4-
glycosidic bonds in
polysaccharides; role
29
in cancer unknown
[228]
AMY2B Alpha-amylase 2B (EC 3.2.1.1) (1,4-alpha-D-glucan
glucanohydrolase 2B) (Carcinoid alpha-amylase)
Cleaves alpha 1, 4-
glycosidic bonds in
polysaccharides; role
in cancer unknown
[228]
TIMP1 Metalloproteinase inhibitor 1 (Erythroid-potentiating
activity) (EPA) (Fibroblast collagenase inhibitor)
(Collagenase inhibitor) (Tissue inhibitor of
metalloproteinases 1) (TIMP-1)
Invasion/metastases
[26]
TIMP3 Metalloproteinase inhibitor 2 (CSC-21K) (Tissue
inhibitor of metalloproteinases 2) (TIMP-2)
Invasion/metastases
[26]
LPL Lipoprotein lipase (LPL) (EC 3.1.1.34) Lipid metabolism,
inflammation [229]
APOB Apolipoprotein B (Including Ag(X) Antigen) Tumor growth [26]
APOA2 Apolipoprotein A-II (Apo-AII) (ApoA-II)
(Apolipoprotein A2) [Cleaved into: Proapolipoprotein
A-II (ProapoA-II); Truncated apolipoprotein A-II
(Apolipoprotein A-II(1-76))]
Tumor growth [26]
INS Insulin [Cleaved into: Insulin B chain; Insulin A
chain]
Tumor growth,
transformation [26]
LY6G5C Lymphocyte antigen 6 complex locus protein G5c Unknown
30
Figure 1.3. Identification of proteins that interact with literature-derived OC biomarkers. The 27
literature OC biomarkers interact with 21 proteins from the human proteome with similar structural
and functional characteristics, including the amylase isozymes AMY-1A, -2A, and -2B. Analysis
of the interactivity of published OC biomarkers by String produced a network of predicted
associations for these proteins. The network nodes are proteins, whereas the edges represent the
predicted or known functional associations. An edge may be drawn with up to 7 differently colored
lines that represent the existence of the seven types of evidence used in predicting the associations.
A red line indicates the presence of fusion evidence; a green line - neighborhood evidence; a blue
line – co-occurrence evidence; a purple line - experimental evidence; a yellow line – text mining
evidence; a light blue line - database evidence; a black line – co-expression evidence.
31
Rationale
Given the overall poor survival associated with OC, it is imperative to identify and
delineate the role of novel protein regulators of OC in order to understand their role OC progression
and which could also one day serve as potential targets for therapeutic intervention. While
comprising a diverse group of functional proteins, OC biomarkers share several features including
a preponderance for a stable protein structure (ordered, hydrophilic, aggregation-prone,
glycosylated and sumoylated), the ability to be secreted (export signal peptide sequence,
hydrophilic) and functionality related to ECM modification, immune response and/or energy
production. Interestingly, several amylase isozymes are noted among the potential OC protein
biomarkers of interest identified for further investigation (Figure 3, Table 3). Elevated serum levels
of amylase or hyperamylasemia, has been long associated with OC and has been studied as both a
diagnostic and prognostic indicator even though its functional contribution to OC progression
remains unknown. Amylase is also interesting because it can potentially play multiple roles in OC,
including cleaving alpha 1, 4-glycosidic bonds in polysaccharides to initiate carbohydrate
metabolism, thereby contributing to energy production. Amylase may also play a role in ECM
remodeling by cleaving polysaccharide moieties in the tumor microenvironment; amylase has
already been shown to degrade bacterial biofilm in wounds [228]. It has also been proposed that
amylase is released as part of an immune response due to inflammatory microenvironment induced
by OC [230].
Central Hypothesis
The objective of this study is to determine the function of amylase in OC. My central
hypothesis is that increased amylase expression and secretion alters ECM structure and
32
composition to enhance OC cell invasion. This study also proposes that Spirulina is a novel
transcriptional inhibitor of amylase which can decrease OC invasion. Three aims are proposed.
Aim 1: Characterize and predict which amylase isozyme(s) is overexpressed in OC.
Currently, it is unclear which amylase isozyme is overexpressed in OC. Computational
analyses will be performed on the amylase isozyme (AMY1A, -1B, -1C, -2A, and -2B) sequences
to determine the characteristics of each isozyme and predict which isozyme is most likely
overexpressed in OC. Predicted overexpression of specific amylase isozymes will be validated in
serum samples from healthy controls and patients with OC.
Aim 2: Determine the extracellular function of amylase in OC.
In order to begin to understand the role of amylase for OC progression, an in vitro model
system of OC cell lines that overexpress and secrete amylase will be established, then utilized to
show that by remodeling ECM, amylase may contribute to OC cell invasion.
Aim 3: Transcriptionally inhibit amylase expression using a novel agent.
Since hyperamylasemia is associated with poor clinical outcome in cancer [231], amylase may
represent a novel target for therapeutic intervention. Commercially available amylase inhibitors
typically inhibit amylase at the protein level. I propose to employ Spirulina, a dietary supplement,
as a novel transcriptional inhibitor of amylase. Subsequently, the ability of Spirulina to inhibit OC
cell migration and invasion will be determined.
33
Defining the function of amylase in OC will fill a much needed gap about the etiology of OC.
Further, these studies will establish a novel transcriptional inhibitor of amylase that may lead to
more effective treatment options for OC patients with hyperamylasemia.
34
Chapter 2
Computational Analysis of Amylase Isozymes Contributing to Ovarian Cancer
Introduction
Glucose metabolism is the fundamental biochemical reaction providing energy to cells.
Whether by oxidative phosphorylation or glycolysis [232], glucose consumption is typically
greater in cancer cells than their normal counterparts [233,234]. Alpha-amylase (commonly
referred to as amylase) was first characterized as a starch digesting enzyme in 1913 [235]. There
are three main classes of amylase (α, β and γ) that differ in their structure, mechanism of action
and phylogenetic location; α-amylase is found in animals, plants, fungi, and bacteria, β-amylase is
found in plant seeds and γ-amylase is found in yeast and fungi [236]. Amylase cleaves alpha 1, 4-
glycosidic bonds in polysaccharides to initiate carbohydrate metabolism. Consequently, since α-
amylase is restricted to mammalian cells and since starch is the major source of dietary glucose,
changes in the levels of α-amylase could influence cancer cell survival.
History of the amylase genes
In 1988, cloned amylase genes examined by Southern blot indicated there are three distinct
copies of the amylase genes in the human genome (AMY1, amy2 and amy3 ) whereas previously,
it was thought there is only salivary AMY1 and pancreatic AMY2 [237,238]. In 1989, amy2 and
amy3 were dubbed AMY2A and AMY2B, respectively. Previously, amy3 was mostly associated
35
with carcinoid tissue because it was isolated from lung cancer tissue [239]. The AMY2B amylase
gene was cloned and characterized; it was found to consist of ten exons that are highly homologous
to the ten exons that make up AMY1 and AMY2A (amy2). The introns of AMY1, AMY2A, and
AMY2B were also reported to be highly homologous. However, it was found that AMY2B has
two unique untranslated regions in the 5’ region, placing the promoter of the AMY2B gene farther
upstream than the promoters of AMY1 and AMY2A [240]. A later report found AMY1 consists
of 11 exons [241].
Evolution and expression of amylase genes
Amylase consists of a family of isozymes derived from a single ancestral gene. The
evolution of amylase genes started 43 million years ago with the insertion of a γ-actin pseudogene
before the primordial amylase gene constituting the ancestral amylase gene (Figure. 2.1A). The
ancestral gene underwent duplication. One copy of the ancestral amylase genes has maintained its
structural integrity to date and is referred to as AMY-2B (Figure. 2.1A). However, approximately
39 million years ago, a retroviral insertion interrupted the actin pseudogene of the other copy of
the ancestral gene and is dubbed the ‘retroviral’ amylase gene. The ‘retroviral’ amylase gene then
underwent further duplication. One copy of the ‘retroviral’ amylase gene underwent long terminal
repeat (LTR) recombination to become AMY-2A. Approximately a million years ago, the other
‘retroviral’ amylase gene triplicated to become AMY-1A, -1B, and -1C. Consequently,
evolutionary diversification has resulted in five amylase genes (AMY-1A, -1B, -1C, AMY-2A, -
2B) clustered on the short arm of chromosome one at p21 (Figure 2.1B). These genes are
characterized by three distinct gene flanking regions including a retroviral insertion (AMY1A,
36
AMY1B, and AMY1C), a 3/5’LTR recombination (AMY2A) and an actin pseudogene (AMY2B)
(Figure 2.1C) [242].
Figure 2.1. Schematic of mammalian amylase gene distinction. (A) The five amylase genes
evolved from a single primordial gene. Adapted from [244]. (B) Current amylase genes are
clustered on chromosome 1.p21. Figure adapted from [242]. (C) Evolution of amylase resulted
in three distinct regions flanking the amylase genes characterized by a retroviral insertion
(AMY1A, AMY1B, and AMY1C), a 3/5’LTR recombination (AMY2A) and an actin
pseudogene (AMY2B). Figure adapted from [244].
A
B
C
37
The retroviral insertion is thought to have resulted in amylase tissue specificity.
Historically, AMY-1A, AMY-1B, and AMY-1C were thought to encode salivary amylase,
whereas AMY2A and AMY2B encoded pancreatic amylase [243]. However, recently, AMY-2B
protein has been found endogenously in many normal tissues because it does not have tissue
specificity established by the retroviral insertion [243,244]. There are also reports of AMY2B
protein expression in normal tissue such as the liver, gonads, fallopian tubes, ovary, leukocytes
and intestinal tract. There has been evidence that AMY2B’s expression in different tissues is
regulated by unique promoters. The AMY2B’s pancreatic start site differs from its hepatic start
site [243]. AMY1 expression was thought to be exclusively found in salivary glands, however,
AMY1 expression has been reported in two cases of normal thyroid gland tissue, two cases of
cervix tissue, and one case of fallopian tube tissue. A case of normal ovary tissue presented with
AMY1 expression, but a second case of ovarian tissue did not present with AMY1 expression
[245,246]. Both AMY2B and AMY1 expression have been isolated in normal lung tissue [246].
Total amylase levels in the serum of normal healthy human adults are elevated with age; amylase
levels are elevated in women in their 30s and 40s [247].
Amylase gene regulation
The promoter of AMY-2B, the ancestral amylase gene, has not yet been identified. It is
hypothesized that the retroviral insertion either activates a hidden promoter within the actin
pseudogene or acts as an enhancer that drives the endogenous promoter of AMY-2B. Regardless,
the retroviral insertion plays an important regulatory role in the transcription of amylase [248,249].
No work has been done on the regulation of AMY2A and the promoter of AMY2A has not been
validated experimentally.
38
In contrast, it has been assumed that the promoters for AMY-1A, -1B, and -1C reside in the
region flanking these genes. Studies targeting the flanking regions of AMY-1C indicate that AMY-
1C can be activated by integrin and/or growth factor stimulation [248]. Integrin activation leads to
stimulation of downstream signaling cascades and an increase in intracellular calcium levels that
leads to increased amylase expression. Transforming growth factor (TGF)-α activation triggers
phosphatidylinositol phospholipase C (PLC), inositol triphosphate (IP3) and increases intracellular
calcium levels. Downstream protein kinase C (PKC) and ERK1/2 phosphorylation activate the
amylase 1C promoter [249,250]. Both pathways result in increased amylase 1C expression
indicating that the region flanking the amylase gene may play a role in the regulation of amylase
1C expression.
It has been reported that the amylase genes contain general transcriptional enhancer (GRE)
regions in their promoters that respond to glucocorticoid binding. There are two GRE regions in
the promoters of the pancreatic amylase genes and five GRE regions in the promoter of the salivary
amylase genes [238]. The LTRs inserted in the promoters of the salivary AMY1 and AMY2A
genes contain transcriptional control elements including a TATA box, a CCAAT box, a
polyadenylation signal, and GREs [244].
Differentiating amylase isozymes
The amylase enzymes are predominately secreted by the pancreas and salivary glands [251].
Amylase isozymes have been differentiated by digestion of the fluorogenic substrate, FG5P, by
salivary (AMY-1A, -1B, or -1C), pancreatic (AMY-2A) and AMY-2B amylases into FG3 and p-
nitrophenyl α-maltoside or FG4 and p-nitrophenyl α-glucoside. The FG4/FG3 ratios are 1.88 for
AMY2B, 1.08 for AMY-2A and 0.52 AMY-1A, -1B, or -1C [252].
39
Amylase isozymes can also be distinguished by differences in isoelectric points (pI) and
molecular weight. Experimentally, salivary amylase (AMY1A, AMY1B, and AMY1C) has a pI at
6.36 and 6.65 and pancreatic amylase has a pI at 7.4 [253]. On western blots, ovarian tumor
amylase and salivary amylase have a similar migratory pattern and appear as a doublet of
approximately 57 and 60 kDa. The doublet formation is believed to be due to posttranslational
modifications including glycosylation and deamination. In contrast, the pancreatic amylase
isoenzyme migrates as a single band at 55 kDa [253].
Known amylase characteristics
It has been reported that the amylase genes are located on the short arm of chromosome 1 and
amylase proteins have an average molecular weight of 56 kDa [254]. There is also high homology
among the amylase genes. The three AMY1 are more than 99.9% homologous. AMY1 is 93.2%
homologous to AMY2A and 93.6% homologous to AMY2B. AMY2A and AMY2B are 94%
homologous [255]. The 3D structures of the pancreatic [256] and salivary [257] α-amylase were
obtained by X-ray crystallography. The resulting tertiary structures of the salivary and pancreatic
amylase were revealed to be very similar. However, in these studies it was not indicated which
pancreatic amylase (AMY2A or AMY2B) was used to elucidate the tertiary structure. Further,
while calcium (Asn100, Arg158, Asp167, and His201) and chloride ion (Arg195, Asn298,
Arg337) binding sites of the pancreatic amylase protein have been elucidated, all amylase
isozymes have three similar domains: Domain A (residues 1-99, 469-404), Domain B (residues
100-168), and Domain C (residues 405-496) with active sites at residues Asp197, Glu233, and
Asp300.
40
A trans-species comparison of human pancreatic amylase, porcine amylase, Aspergillus oryzae
“Taka” (fungus) amylase and Aspergillus niger (fungus) amylase revealed very little primary
sequence homology among these amylases. Yet, strong homology across species in residues 96-
101, 201, 233-236, and 294-301 confers structural homology of amylases derived from different
animal species [256]. Likewise, amylase isozymes exhibit similar enzyme activity and degrees of
inhibition [253]. Amylase from Aspergillus oryzae has been reported to have a single N-
glycosylation site at Asn 197 [258,259].
In rats, amylase activity is affected significantly by the sex cycle; the change in amylase
expression is not correlated to calcium levels in the ovary, but to sex hormones acting on the ovary.
Both salivary and pancreatic amylases were isolated in rat ovary, but only salivary amylase was
isolated in rat serum [260].
Amylase in cancer
Amylase is overexpressed in and secreted by a number of tumors [261] including ovarian
tumors [262]. However, it remains controversial as to which amylase gene encodes amylase
isoenzymes secreted by tumors. Some suggest that amylase secreted by tumors is encoded by
AMY-2B [237,245,263–265], while others consider salivary AMY-1A, -1B, or -1C [240,246,266–
271] as the source of tumor amylases. A unique amylase isoenzyme secreted by tumors has also
been suggested [253,272–274]. Further, it is unknown whether several amylase isozymes or a
single isoform is secreted by tumors. As a result, disagreement and mischaracterization of tumor
amylase isozyme expression is common [253,271].
41
Objectives
Hyperamylasemia is associated with disease progression although its contribution to OC is
unclear [1]. Although functionally similar, differential expression of amylase isozymes in OC may
be clinically significant for diagnostic and/or prognostic outcome. Since there is a controversy as
to which amylase isozyme(s) is overexpressed in OC, in this chapter I sought to characterize and
predict which amylase isozyme is overexpressed in OC using computational analyses (evolution,
disorder, hydrophobicity, aggregation, structure, structural features, possible binding partners,
mutation profile, and promoter transcription factors) of human amylase isozymes AMY-1A, -1B,
-1C, -2A, and -2B. Lastly, I sought to validate my prediction in clinical serum samples.
Materials and methods
Sequences used in computational characterization
Computational amylase analyses were performed using the human AMY-1A (NCBI Reference
Sequence: NP_001008222.1); AMY-1B (NCBI Reference Sequence: NP_001008219.1); AMY-
1C (NCBI Reference sequence: NP_001008220.1); AMY-2A (NCBI Reference Sequence:
NP_000690.10) and AMY-2B (NCBI Reference Sequence: NP_066188.1) protein sequences.
Amylase isozyme homology
ClustalW2 is a program used to align multiple DNA and protein sequences using pairwise
sequence alignment tools; ClustalW2 [275–277] was used to determine the percentage of
homology between amylase protein sequences.
Protein biochemical properties databases
ProtParam [278] was used to define the molecular weight, theoretical pI, instability index
(distinctive dipeptides that confer instability to a protein), and GRAVY (grand average of
hydropathy—the sum of hydropathy values of the entire protein, divided by the number of residues
42
in the sequence). Uniprot [187] was used to determine regions of protein instability, signal
localization, and binding sites. The information gathered from Uniprot jointly with SignalP 4.1
and SBASE was also used to construct a general structure depicting the common features shared
by the amylase isozymes.
SignalP 4.1 [279] was used to predict the presence and the cleavage site of a possible signal
transport sequence. The protein sequences were run in SBASE [280] to determine which domains
are in each amylase isozyme. Domains were confirmed with pfam [281,282], a large collection of
the protein families.
Protein disorder
The distribution of intrinsic amylase protein disorder was analyzed using disorder
predictors PONDR® VLXT [183] and PONDR® VSL2 [184,185] which identify regions of protein
disorder and hydropathy. PONDR® VLXT is capable of identifying potential molecular
interactions motifs in disordered proteins and disordered protein regions [184] while PONDR®
VSL2 more accurately predicts general protein disorder.
Secondary structure prediction
CSSP (Consensus Secondary Structure Prediction)[283] was used to predict the consensus
secondary structure of the amylase isozymes. I-TASSER was used to predict the tertiary structure
of the amylase isozymes [284–287]. Amylpred 2 [186] was used to identify protein sequences
prone to form protein aggregates.
Potential protein binding sites within amylase isozyme proteins were identified by the
ANCHOR algorithm [288,289] which determines protein binding motifs within regions of
disordered proteins. String [219] analysis was performed to identify potential amylase isozyme
binding partners by depicting a network of proteins that physically and/or functionally interact
43
with amylase. RaptorX-binding [290] analysis was performed to determine ligands that potentially
bind to the amylase isozymes.
Posttranslational modification
HMMpTM [291] was used to predict phosphorylation and glycosylation sites in the
amylase isozymes. Disorder Enhanced Phosphorylation Predictor (DEPP) is a PONDR
phosphorylation predictor that determines possible phosphorylated amino acid residues through
patterns of disorder, hydrophobicity and charge. Sumoylation was predicted by SUMOplot
Analysis Program.
Transcription regulation by promoter analysis
The promoters of the amylase isozymes have not been previously validated by
experimentation. The suspected amylase promoters used were the -600 base pairs flanking the 5'
transcription start site as retrieved from Ensembl genome browser [292]. The promoter regions of
all five amylase isozymes were examined by the Promo database [293,294] to find possible
transcription factor binding sites that could regulate amylase expression. To find other regulatory
regions in the promoter, the Eukaryotic Promoter Database (EPD) [295] was utilized to find the
promoter of the amylase isozymes and then find the TATA box, the GC box and the CCAAT box
sequences within the promoter sequence to better distinguish the core promoter elements.
Mutational analysis of the amylase isozymes
The mutation profiles of AMY-1A, AMY-2A, and AMY-2B were found on cBioPortal
[296,297] for Cancer Genomics, a large scale cancer genomics database that determines the
individual mutations in genes as well as the prevalence of mutations in cancer.
44
Clinical Specimens
With prior University of South Florida Institutional Review Board committee approval
serum samples were collected at the H. Lee Moffitt Cancer Center and at the University of South
Florida. A total of 30 de-identified serum samples were collected from women with benign
gynecologic disease (N = 8) as well as from patients with serous OC (N = 8), endometrial cancer
(N = 5), mucinous OC (N = 5), thecoma (N = 1), teratoma (N=1), ovarian anaplastic metastatic
carcinoma (N =1) and clear cell OC (N = 1). Pooled serum (N = 4) from healthy individuals was
obtained from Valley Biomedical Cat. HS1021, Lot. 7A0032 and Lot. 6K2146 (Winchester, VA)
and Atlanta Biomedical Cat. S40590, Lot. M14161 and Lot. C16037 (Flowery Branch, GA) to be
used as control. All serum samples were kept on ice following collection, centrifuged at 3000 g
for 15 minutes at 4°C and supernatants were then aliquoted and stored at –20°C.
Western blot
Serum samples were diluted in CHAPS buffer and 120ug of serum protein were separated
by a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Proteins were
transferred to Polyvinylidene fluoride (PVDF) membranes, washed in Tween 20-Tris buffered
Saline (TBST), and blocked in 5% milk in TBST. Blots were incubated in their respective primary
antibodies overnight, followed by incubation with a horseradish peroxidase (HRP)-conjugated
secondary antibody (Fisher, Pittsburgh, PA), and developed via FEMTO Cat. 34095 Life
Technologies (Grand Island, NY). Antibodies used were: AMY1A (1:1000) Cat.H00000276-M04
Abnova (Taipei City, Taiwan); AMY2A (1:500) LS-C114393 LSBio (Seattle, WA); AMY2B
(1:2000) Cat. LS-C173958 LSBio (Seattle, WA); Transferrin ab82411 abcam (San Francisco, CA).
Densitometry was performed using ImageJ software to normalize amylase band strength to the
transferrin bands in serum samples.
45
Results:
Amylase isozymes are highly homologous.
Alignment analyses were performed on amylase isozyme protein sequences using
ClustalW2 software. AMY-1A, -1B, and -1C have 100% alignment, whereas they were only 96%
homologous to AMY-2A and 97% homologous to AMY-2B. Amy-2B and AMY-2A are 98%
homologous (Figure 2.2). In short, all the amylase proteins are highly homologous. Henceforth,
AMY-1A, -1B, and -1C are collectively referred to as AMY1.
Amylase isozymes have similar molecular weights and isoelectric points.
While the amylase isozymes contain up to 12 exons (11 exons in AMY1 and 12 exons in
AMY2B), all the amylase isozymes contain 10 coding exons resulting in proteins of 511 amino
acids (Table 2.1). Computational analysis revealed the molecular weight of salivary amylase
(AMY1) and pancreatic amylases (AMY2A and AMY2B) are 57,767.8 Da, 57,706.8 Da and
57,709.8 Da respectively (Table 2.1). Computational pI for AMY1A, AMY2A and AMY2B is
6.47, 6.60 and 6.64, respectively (Table 2.1).
Amylases are highly ordered proteins with one predicted protein binding site.
Protparam predicted that all amylases are stable (ordered) proteins, because they were
beneath the threshold of 40, above of which indicates instability. In mammalian cells, all the
amylase isozymes have an identical half-life of 30 hours in vitro (Table 2.1). PONDR protein
disorder predictor indicated that all amylase isozymes are mostly ordered proteins with small
regions of disorder. While the amylase isozymes have similar regions of disorder, AMY1 and
AMY2B have unique regions of disorder that are not found in AMY2A. AMY1 has two unique
regions of disorder encompassing amino acids 51 through 63 and amino acids 375 through 384;
AMY2B also has two unique regions of disorder encompassing amino acids 51 through 63 and
46
Figure 2.2. Amylase proteins are highly homologous. The protein sequences of the amylase
isozymes were subjected to ClustalW2 for alignment. AMY-1A, -1B, and -1C have 100%
alignment, whereas they were only 96% homologous to AMY-2A. AMY-1A, -1B, and -1C are
97% homologous to AMY-2B. Amy-2B and AMY-2A are 98% homologous. Homologous
amino acids share the same color. Clear amino acids are non-homologous.
47
Table 2.1. Biochemical properties of amylase determined by computational analyses
AMY1—AMY-
1A, AMY-1B,
AMY-1C
AMY-2A AMY-2B Significance
Exons 11 but only 10
coding
10 12 but only
10 coding
All amylase
isozymes have 10
coding exons
Length of α-
amylase (amino
acids)
511 511 511 All amylase
isozymes are the
same length
Theoretical
molecular
weight (Da)
57767.8 57706.8 57709.8 All amylase
isozymes have an
average
computational
molecular weight of
57KDa
Theoretical pI 6.47 6.60 6.64 All amylase
isozymes have an
average
computational
isoelectric point of
6.57.
Instability index 23.58 23.56 25.26 All amylase
isozymes are
ordered
Half-life The estimated half-life is:
>30 hours (mammalian reticulocytes, in
vitro).
>20 hours (yeast, in vivo).
>10 hours (Escherichia coli, in vivo).
All amylase
isozymes are stable
for long periods of
time
Possible protein
binding
(ANCHOR)
aa107-112 aa109-110 aa108-111 All amylase
isozymes have one
possible binding site
GRAVY
(hydrophobicity)
-0.436 -0.396 -0.417 All amylase
isozymes are
hydrophilic
PONDR
hydropathy
0.4516 0.4559 0.4537 All amylase
isozymes are
hydrophilic
48
Figure 2.3. Amylase isozymes are ordered proteins. Amylase isozymes AMY-1A, -1B, -
1C (A), AMY-2A (B) and AMY-2B (C) were subjected to PONDR analyses for protein
disorder. AMY1 has two unique regions of disorder encompassing amino acids 51 through
63 and 375 through 384; AMY2B has two unique regions of disorder encompassing amino
acids 51 through 63 and 117 through 134 (denoted by*).
A
* *
B
C
* *
49
A
C
B
Figure 2.4. Amylase isozymes are hydrophilic. Amylase isozymes AMY-1A, -1B, -1C (A),
AMY-2A (B) and AMY-2B (C) were subjected to PONDR analyses for protein
hydropathy. The amylase isozymes are hydrophilic according to the mean scale
hydropathy. The amylase isozymes also have low absolute mean net charge values
indicating that amylase isozymes are ordered.
50
amino acids 117 through 134 (Figure 2.3; denoted by *).
The ANCHOR program predicted a single potential protein binding site within the ordered
protein regions, unique to each amylase isozyme. AMY1 could potentially bind another protein at
amino acids 108-111; AMY2A at 109-110 and AMY2B at 107-112 (Table 2.1).
Amylase isozymes are hydrophilic.
All amylase isozymes have low negative GRAVY (grand average of hydrophobicity index)
scores: AMY1 is -0.436, AMY2A is -0.396 and AMY2B is -0.417 (Table 2.1) indicating they are
hydrophilic (increasing positive GRAVY values indicate higher hydrophobicity). Likewise,
PONDR predicted hydrophobicity scores of 0.4516 for AMY1, 0.4559 for AMY2A and 0.4537
for AMY2B (Figure 2.4, Table 2.1) suggesting that the amylase isozymes are hydrophilic proteins.
Amylase is predicted to be glycosylated and phosphorylated.
HMMpTM and PONDR Disorder Enhanced Phosphorylation Predictor (DEPP) database
were used to identify potential posttranslational modifications, especially glycosylation and
phosphorylation sites (Table 2.2). According to HMMpTM, the amylase isozymes do not differ
significantly from each other with each isozyme containing 13-15 possible glycosylation and seven
possible phosphorylation sites. DEPP predicted that all amylase isozymes also have two
phosphorylated residues at amino acids 166 and 197. AMY2B appears to have a unique
phosphorylation residue at amino acid 133 (Table 2.2, Figure 2.5). The SUMOplot Analysis
Program predicted that all amylase isozymes were sumoylated at amino acids 2, 50, 275 and 279
(Table 2.2).
51
Table 2.2 – Secondary structural and posttranslational modifications among amylase
isozymes
AMY1—AMY-1A,
AMY-1B, AMY-1C
AMY-2A AMY-2B
Phosphorylation
(DEPP)
166, 197 166, 197 133, 166, 197
Phosphorylation
(HMMpTM)
7; residues 246, 259,
269, 279, 290, 291,
304
7; residues 246, 259,
269, 279, 290, 291,
304
7; residues 246, 259,
269, 279, 290, 291,
304
Glycosylation
(HMMpTM)
14; O-linked: 58,
127, 452, 457, 493
N-linked: 61, 96,
102, 103, 165, 365,
411, 427, 476
13; O-linked: 58,
127, 452, 457, 493
N-linked: 61, 96,
102, 103, 165, 365,
427, 476
15; O-linked: 58, 123,
127, 452, 457, 493
N-linked: 61, 96, 102,
103, 165, 365, 411,
427, 476
Sumoylation
(SUMO plot)
2,50, 275, 279 2,50, 275, 279 2,50, 275, 279
Signal peptide
(SignalP)
Yes; 1-15 Yes; 1-15 Yes; 1-15
Consensus
Secondary
Structure
(CSSP)
Alpha helix: 15.65%
beta strand: 22.30%
Alpha helix: 16.24%
beta strand: 24.07%
Alpha helix: 16.04%
beta strand: 22.11%
Aggregation
prone regions
(Amylpred)
16 aggregation prone
regions
21 aggregation prone
regions
22 aggregation prone
regions
The amylase isozymes have common domains, and structural features.
According to SBASE and pfam, all the amylase isozymes have two main structural domains:
an alpha amylase catalytic domain from amino acids 36 to 351 and a C-terminal all-beta domain
from amino acids 421 to 510 (Figure 2.5). The C-terminal all-beta domain is unique to the glycosyl
hydrolase family members and is important for folding and secretion of amylase [298]. SignalP
analysis predicted a transport signal cleavage site at amino acid 15 in all amylase isozymes (Table
2.2) which is in keeping with secretion of amylase out of the cell [299].
Uniprot determined possible cofactor binding sites in amylase isozymes. All amylase
proteins require calcium and chloride cofactors for enzymatic function [300,301] and have calcium
52
binding sites at amino acids 115, 173, 182, and 216 and chloride binding sites at amino acids 210,
313, and 352. Amino acids 212 and 248 play an essential role in the active site of all amylase
isozymes (Figure 2.5). All amylase isozymes have four disulfide bonds connecting amino acids 43
to 101, amino acids 85 to 130, amino acids 156 to 175, and amino acids 393 to 399. ProtParam
also predicted that amylase requires calcium and chloride as cofactors with calcium and chloride
binding sites at the same residues predicted by Uniprot (Figure 2.5).
Amylase isozymes have unique binding regions for chloride, calcium and glucose.
RaptorX binding analysis suggested that while all the amylase isozymes bind chloride,
calcium, and glucose, individually, they may have additional binding residues for chloride,
calcium and glucose. All amylase isozymes can bind chloride at residues R210, E248, N313, and
R352 (Figure 2.5). However, AMY2A and AMY2B could bind chloride at residue T269 and
AMY2B might additionally bind chloride at residue H216 (Figure 2.5). Likewise, all amylase
isozymes could bind calcium at residues R173 and D182 (Figure 2.5). However, AMY1 can also
bind calcium at residues N115 while AMY2A might bind calcium at residues N115 and H216
(Figure 2.5). All amylase isozymes appear capable of binding glucose at residues W73, W74, Y77,
Q78, H116, L177, L180, R210, D212, A213, H216, E248, I250, H314, D315, and H320 (Figure
2.5). However, AMY2A and AMY2B can also bind glucose at residues T178 and G321 (Figure
2.5). Therefore, AMY2A and AMY2B appear to share the greatest number of unique binding
residues for chloride, calcium, and glucose which further supports the high homology between
these isozymes.
53
Transport signal cleavage site Calcium binding sites Chloride binding sites Glucose binging sites Active sites Phosphorylation sites O-linked glycosylation N-linked glycosylation Disulfide bonds AMY1 calcium binding site AMY2A calcium binding site AMY2B calcium binding site AMY1 anchor possible binding site AMY2A anchor possible binding site AMY2B anchor possible binding sites AMY2A/AMY2B glucose binding site AMY1/AMY2B N-linked glycosylation site AMY2A/AMY2B chloride binding site AMY2B phosphorylation site AMY2B O-linked glycosylation site
Figure 2.5. Amylase isozymes share structural
features. The SBASE, Uniprot, and DEPP database,
Raptor binding, HMMpTM databases were used to
define general structural features of the amylase
isozymes including domains, co-factor binding sites,
post-translational modifications and residues that
may play an important role in amylase catalytic
activity. The amylase isozymes have an amylase
catalytic domain from amino acid 36 to 351, a C-
terminal all-beta domain from amino acid 421 to 510,
calcium binding sites at amino acids 173 and 182,
chloride binding sites at amino acids 210, 248, 313,
and 352, and glucose binding sites at amino acids 73,
74, 77, 78, 116, 117, 180, 210, 212, 213, 2116, 248,
250, 314, 315, and 320. The amylase isozymes also
have four disulfide bonds at amino acids 43 to 101,
amino acids 85 to 130, amino acids 156 to 175, and
amino acids 393 to 399. Uniprot also determined that
amino acids 212 and 248 play an essential role in the
active site. All amylase isozymes have
phosphorylation sites at amino acids 166, 197, 246,
259, 269, 279, 290, 291, and 304. All amylase
isozymes have O-linked glycosylation sites at amino
acids 58, 127, 452, 457, and 493 and N-linked
glycosylation sites at amino acids 61, 96, 102, 103,
165, 365, 427, and 476. B) Unique calcium, chloride,
and glucose binding sites, phosphorylation and
glycosylation sites and anchor potential binding sites
are also depicted on the bottom of the figure.
54
The amylase isozymes have similar secondary and tertiary structure profiles.
The secondary structure of amylase isozymes was examined by Consensus Secondary
Structure (CSSP) (Table 2.2). The predicted secondary structure of all amylase isozymes is
composed of over 50% of coil. The other 50% is composed of alpha helices and beta sheets. AMY1
is predicted to have 15.65% alpha helical structure and 22.30% beta sheet structure. AMY2A is
predicted to have 16.24% alpha helical structure and 24.07% beta sheet structure. AMY2B is
predicted to have 16.04% alpha helical structure and 22.11% beta sheets. Therefore, the predicted
secondary structure makeup of the amylase isozymes is very similar.
The tertiary structure as isolated by I-TASSER analysis shows that the tertiary structure of
amylase isozymes AMY1 and AMY2B is identical (Figure 2.6). Though the structure of AMY2A
was similar to the structure of AMY1 and AMY2B, it has looser, less organized and less defined
coil structures (Figure 2.6, denoted by orange arrows).
B
Figure 2.6. AMY2A has a unique 3D structure. The amylase isozymes
were subjected to RAPTORX to render a 3D structure. The 3D structure of
AMY1 (A), AMY2A (B), and AMY2B (C) determined that the structures
of AMY1 and AMY2B are identical and AMY2A has different coil
structures as denoted by orange arrows.
C A
55
Multiple regions of amylase are prone to aggregation.
The aggregation prone regions of the amylase isozymes were predicted by AmylPred2.
AMY1 has 16 aggregation prone regions. AMY2A has 21 regions of aggregation and AMY2B has
22 regions of aggregation (Table 2.2, Figure 2.7). AMY2A and AMY2B displayed unique
aggregation prone regions (Figure 2.7). Unique aggregation regions within AMY2A were mapped
to amino acids 2, 64-68, 97-98, 116, 118, 197, 279-281, 325-332,340-345, 355-356, 383-386, 407-
415, 439, 456, and 478-480. Likewise, unique aggregation regions within AMY2B mapped to
amino acids 2, 26, 40, 97-98, 116, 176-182, 325-332, 340-345, 355-356, 383-386, 408-409, 413-
414, 439, and 464-466. While aggregation prone regions were found throughout the proteins, the
isozyme unique aggregation prone regions were mostly found at the terminal ends of the protein
isozymes (Figure. 2.7).
Figure 2.7. Amylase
isozymes have multiple
aggregation prone regions.
The amylase isozymes were
subjected to analysis by
AmylPred2 in order to
determine aggregation prone
regions. AMY1 has 16
aggregation prone regions.
AMY2A has 21 regions of
aggregation and AMY2B has
22 regions of aggregation.
AMY2A and AMY2B have
unique regions of aggregation
not found in AMY1 (*).
56
The amylase isozymes functionally interact with metabolic proteins
According to String analysis, amylase interacts functionally with metabolic proteins
(Figure 2.8). Using a medium confidence interval of interaction, I could isolate proteins that most
likely interact with amylase. Proteins that interact with all amylase isozymes are glycogen
phosphorylase, brain form (PYGB), glucan (1,4-alpha-) branching enzyme 1 (GBE1), glycogen
phosphorylase, liver form (PYGL), glycogen debranching enzyme (AGL), and sucrase-isomaltase
(SI). The proteins that interact with all the amylase isozymes have functions associated with
carbohydrate metabolism (Table 2.3, Figure 2.8). Both AMY2A and AMY2B interact with
Thyroid Stimulating Hormone, Beta (TSHB)
However, amylase isozymes also have unique probable interactions. AMY1 interacts with
lactase (LCT), kinase A anchor protein 1 (AKAP1), kinase A anchor protein 8 (AKAP8), collagen,
type X, alpha 1 (COL10A1), and bacterial/permeability-increasing protein (BPI). The unique
proteins associated with AMY1 are connected to carbohydrate metabolism, antibacterial functions,
and extracellular matrix glycoprotein remodeling. AMY2A interacts with maltase-glucoamylase
(MGAM), glycoprotein 2 (GP2), enolase 1 (ENO1), and pancreatic lipase (PNLIP). The unique
proteins associated with AMY2A are connected to carbohydrate and lipid metabolism, and
immune and antibacterial functions. AMY2B interacts with phosphoglucomutase 1 (PGM1), CD
molecule (CD2), ATPase, Na+/K+ transporting, Alpha 4 Polypeptide (ATP1A4), ATPase,
Na+/K+ transporting, and Alpha 1 Polypeptide (ATP1A1). The unique proteins associated with
AMY2B are connected to carbohydrate metabolism, energy production and immune functions.
57
A
B
C
Figure 2.8. Amylase isozymes form functional interactions with other metabolic enzymes.
String database indicate that AMY1 (A), AMY2A (B), and AMY2B (C) have functional
interactions with other starch digesting enzymes. Shown is the interaction network of the
amylase isozymes at medium confidence. Green lines connect proteins which are associated
by recurring conserved genomic neighborhood, red lines indicate gene-fusion events in other
species, blue connections are inferred by phylogenetic co-occurrence derived from similar
patterns of absence and presence of genes, black lines indicate co-expression analysis derived
from similar patterns of mRNA expression, purple lines indicate high-throughput
experimental data, light blue lines indicate associations in curated database, and light green
lines indicate textmining or co-occurrence of gene/protein names in abstracts.
58
Table 2.3. Proteins that functionally interact with amylase isozymes
Proteins that interact with all amylase isozymes
AGL Amylo-alpha-1, 6-glucosidase, 4-alpha-glucanotransferase. Glycogen debrancher
enzyme which is involved in glycogen degradation.
PYGB Phosphorylase, glycogen; brain; Glycogen phosphorylase found predominantly in
the brain as an allosteric enzyme in carbohydrate metabolism.
GBE1 Glucan (1,4-alpha-), branching enzyme 1; Glycogen branching enzyme required
for sufficient glycogen accumulation.
PYGL Phosphorylase, glycogen, liver. This gene encodes a protein that cleaves alpha-1,
4-glucosidic bonds to release glucose-1-phosphate from liver glycogen stores.
SI Sucrose-isomaltase (alpha-glucosidase). This gene encodes a sucrose-isomaltase
complex essential for the final stage of digestion of dietary carbohydrates.
Proteins that interact with both AMY2A and AMY2B
TSHB Thyroid Stimulating Hormone, Beta is indispensable for the control of thyroid
function and metabolism.
Proteins that interact only with AMY1
LCT Lactase-glycosylceramidase is an integral protein in the plasma membrane that has
both lactase activity and phlorizin hydrolase activity.
AKAP1 A kinase (PRKA) anchor protein 1; binds to type I and II regulatory subunits of
protein kinase A and anchors them to the mitochondrion.
AKAP8 A kinase (PRKA) anchor protein 8; anchoring protein that mediates the
compartmentation of PKA and other signaling molecules. AKAP8 may target
PKA and the condensin complex to chromatin during mitosis for chromosome
condensation.
COL10A1 Collagen, type X, alpha 1; type X collagen is a homotrimer product of
hypertrophic chondrocytes during endochondral ossification
BPI Bactericidal/permeability-increasing protein. This gene encodes a
lipopolysaccharide binding protein associated with human neutrophil granules and
has antimicrobial activity against gram-negative organisms.
Proteins that interact with only AMY2A
MGAM Maltase-glucoamylase (alpha-glucosidase) is a brush border membrane enzyme
that plays a role in the final steps of digestion of starch.
GP2 Glycoprotein 2 (zymogen granule membrane) encodes an integral membrane
protein that is secreted from intracellular zymogen granules and associates with
the plasma membrane via glycosylphosphatidylinositol (GPI) linkage. The
encoded protein binds pathogens such as enterobacteria, thereby playing an
important role in the innate immune response.
ENO1 Enolase 1, (alpha). Each isoenzyme is a homodimer composed of 2 alpha, 2
gamma, or 2 beta subunits, and functions as a glycolytic enzyme. Alternative
59
splicing of this gene results in a shorter isoform that has been shown to bind to the
c-myc promoter and function as a tumor suppressor.
PNLIP Pancreatic lipase. It encodes a carboxyl esterase that hydrolyzes insoluble,
emulsified triglycerides, and is essential for the efficient digestion of dietary fats.
Proteins that interact with only AMY2B
PGM1 Phosphoglucomutase 1 belongs to the phosphohexose mutase family. There are
several PGM isozymes, which are encoded by different genes and catalyze the
transfer of phosphate between the 1 and 6 positions of glucose. In most cell types,
this PGM isozyme is predominant, representing about 90% of total PGM activity.
ATP1A4 ATPase, Na+/K+ Transporting, Alpha 4 Polypeptide. The protein encoded is the
catalytic subunit of the subfamily of Na+/K+ -ATPases, an integral membrane
protein responsible for establishing and maintaining the electrochemical gradients
of Na and K ions across the plasma membrane essential for osmoregulation, for
sodium-coupled transport of molecules, and for electrical excitability of nerve and
muscle.
ATP1A1 The catalytic component catalyzes the hydrolysis of ATP coupled with the
exchange of sodium and potassium ions across the plasma membrane. This action
creates the electrochemical gradient of sodium and potassium ions, providing the
energy for active transport of various nutrients.
CD2 CD2 molecule, a surface antigen of the human T-lymphocyte lineage that is
expressed on all peripheral blood T cells. It is one of the earliest T-cell markers,
being present on more than 95% of thymocytes and on some natural killer cells.
* Protein description was retrieved from GeneCards
Amylase isozyme expression may be driven by differential promotor activation
To begin to understand the mechanisms responsible for hyperamylasemia in OC, additional
computational analyses were performed to identify potential transcription factors that might
regulate amylase expression. Promo was used to analyze the probable promoter regions of the
amylase isozymes. Promo predicted probable attractive transcription factor binding sites within
each probable promoter region. It also predicted which transcription factors would be most likely
to bind to these potential transcription factor binding sites (Figure 2.9). The binding sites of these
transcription factors are located at different regions of the isozyme promoter sequences, suggesting
differing patterns of regulation.
60
Transcription factors C/EBP alpha (regulates cell cycle regulation), C/EBP delta (regulates
immune and inflammatory responses), and Zic1/2 (regulates mammalian development) were
predicted to regulate AMY1 and AMY2A promoter activity. STAT5A (signal transduction and
transcription factor) is a unique transcription factor of AMY1. C/EBP delta was also identified as
a candidate transcription factor for AMY2B, however, POU1F1 (regulates mammalian
development) and Cdx-1 (regulates intestine-specific gene expression) were uniquely predicted to
regulate AMY2B (Figure 2.9).
The EPD was utilized to find potential core promoter elements such as the TATA, CG and
CCAAT boxes (Figure 2.9). AMY1 has a TATA box at residues -51 to -33 and a CG box at
residues 40 to 55. AMY2A has TATA boxes at residues -52 to -21 and -17 to -5. AMY2A also has
two CCAAT boxes at residues -598 to -585 and -547 to -534. AMY2B has two TATA boxes at
residues -138 to -101 and -50 to -33 and a CG box at residues 15 to 28.
AMY2B overexpression is the most likely to be associated with amplification mutations in
cancer
cBioPortal for Cancer Genomics database determined the frequency and pattern of mutations in
the amylase isozymes. The number of reported amylase genes’ mutation reflects their evolutionary
history. AMY2B, the most evolutionarily conserved amylase gene, has developed the most
mutations (n = 86), whereas AMY1, the most recently evolved, has developed the least amount of
mutations (n = 4). All the AMY1 mutations are missense mutations; AMY2B mutations are 90.7%
missense mutations, 3.5% nonsense mutations, 4.6% frameshift mutations and 1.1% splice site
mutations. AMY2A has an intermediate number of mutations (n = 51). Mutations in AMY2A are
82.4% missense mutations and 17.6% nonsense mutations (Figure 2.10).
61
Figure 2.9. AMY2B has unique potential transcription factors. The potential promoters of the
amylase isozymes were retrieved from Ensembl genome browser and analyzed by Promo database
to identify candidate transcription factors driving amylase promoter activity.
62
Figure 2.10. Mutational events are highest in AMY2B. cBioPortal for Cancer Genomics
database determined the pattern of mutations in the amylase isozymes AMY1 (A), AMY2A
(B), and AMY2B (C). Mutations are denoted by lollipops – green lollipops denote missense
mutations, red lollipops denote truncating mutations, black lollipops denote inframe deletions
and insertions. Purple lollipops denote residues affected by multiple mutation types. AMY2B,
the most conserved amylase gene, has developed 86 mutations, whereas AMY1, the most
recently evolved, has developed 4 mutations. AMY2A developed 41 mutations.
A
B
C
64
cBioPortal for Cancer Genomics database determinations also revealed a variety of mutations
among the amylase isozymes, including non-specific mutations, deletion mutations and
amplification mutations present in many cancer types including cancers of the lung, colon, uterus
and skin (Figure 2.11). Mutations in AMY1, AMY2A, and AMY2B were found in 21, 24, and 26
cancer types, respectively. AMY1 mutations in cancers other than OC mostly arise from genetic
amplifications and deletions whereas AMY2A mutations in other cancers are comprised of a
C
Figure 2.11. AMY2B is altered in most cancer types. Amplifications comprise the majority
of amylase mutations in OC. cBioPortal for Cancer Genomics database determined the
mutational profiles for amylase; AMY1 (A), AMY2A (B), and AMY2B (C).
65
mixture of deletions, amplifications and missense mutations. Likewise, AMY2B mutations in
other cancers typically occur as missense mutations and amplification mutations (Figure 2.11).
The pattern of amylase isozyme mutations in OC is somewhat different from that in other
cancer types. In OC, the mutational profile of amylase isozyme AMY1 consists mostly of
amplifications and deletions while the mutational profile of the amylase isozyme AMY2A in OC
is mostly amplifications as well as some deletions and nonsense mutations. The pattern genetic
mutation of the amylase isozyme AMY2B in OC is also mostly due to amplifications although
some deletions, truncations and in-frame mutations are also noted. Further, in OC, the mutation
frequency of the amylase isozyme AMY1 was determined to be 2.2% amplifications and 0.3%
deletions. The mutation frequency of AMY2A was 2.1% amplification, 0.3% deletion, 0.1%
missense mutation and 0.1% mixed missense mutation and amplification while the mutation
frequency of AMY2B was 2.2% amplifications and 0.3% deletions (Figure 2.11).
Lastly, cBioPortal for Cancer Genomics database was utilized to determine amylase isozyme
mRNA expression in different cancers (Figure 2.12). AMY1 mRNA expression was low in most
cancers. Only OC, lung squamous carcinoma, thyroid cancer and lung adenocarcinoma expressed
relatively high AMY1 mRNA levels. AMY2A mRNA expression was almost negligible in most
cancers except for its high mRNA expression in pancreatic cancer. AMY2B mRNA expression
was relatively high in all cancers explored by the database. In OC, AMY2B mRNA expression
levels were highest followed by AMY1 and AMY2A mRNA expression levels.
67
Clinical validation confirms elevated levels of AMY2B protein in OC
While my computational analyses predicted elevated AMY2B RNA levels in OC, validation
of elevated AMY2B protein had yet to be determined in OC. Therefore, to validate differential
amylase isozyme protein expression with OC, serum samples from healthy controls, women with
benign gynecologic disease and women with OC and endometrial cancer were subjected to WB
analyses. Using the 95th percentile of amylase values for healthy controls as the threshold for serum
C
Figure 2.12. AMY2B is the most expressed amylase isozyme in OC. cBioPortal for Cancer
Genomics database determined amylase isozyme mRNA expression in cancers at the
transcriptional level; AMY1 (A), AMY2A (B), and AMY2B (C). AMY2B demonstrated the
highest RNA expression in OC when compared with other amylase isozymes (denoted by
boxes). AMY2B RNA is most expressed in OC followed by AMY1 and AMY2A mRNA.
68
amylase positivity, the amount of serum AMY1, AMY2A and AMY2B was generally negligible
in healthy pooled controls and individual serum samples from women with benign gynecologic
disease (Figure 2.13). Likewise, negligible amounts of AMY1A and AMY2A protein were present
in serum samples from endometrial cancer, clear cell carcinoma and mucinous OC. While elevated
levels of AMY2B protein were noted in most endometrial cancers (4/5) and clear cell OC (1/1),
AMY2B protein levels were only elevated in 2/5 mucinous OC. AMY2B levels. In contrast, serum
samples from patients with serous ovarian carcinoma revealed AMY1 (in 3/8 samples) and
AMY2B (in 7/8 samples) protein levels up to 2x greater than healthy controls.
Figure 2.13: Serum levels of AMY2B protein are altered in OC (Continued on Next Page)
69
Discussion: The chromosomal localization of the amylase isozymes on chromosome 1p21 [302,303],
evolutionary development of the amylase isozymes from a single ancestral gene [242–244], and
similar biochemical properties such as the molecular weight, isoelectric point [242,304] and
calcium and chloride cofactor binding features common to the amylase isozymes have been
identified in the literature previously [305,306]. Further, the catalytic domain and the C-terminal
domain of the amylase isozymes [307], and crystalized tertiary structure of the amylase isozymes
[257,308] have also been elucidated.
Figure 2.13. Serum levels of AMY2B protein are altered in OC. Serum samples from normal
controls (*), women with benign gynecologic disease and cancer were subjected to western
immunoblotting for amylase AMY1, AMY2A, AMY2B (upper panel). Amylase band intensity
was quantified and densitometry values are expressed relative to the transferrin control group
(lower panel).
0
0.5
1
1.5
2
2.5
7A
00
32
31
19
-29
31
19
-51
31
19
-53
31
19
-54
31
19
-55
31
19
-56
P2
0A
31
19
-62
6K
21
46
31
19
-12
31
19
-26
31
19
-30
31
19
-40
31
19
-65
31
19
-61
C1
60
37
31
19
-37
31
19
-44
31
19
-63
P8
0
P9
0
31
19
-60
31
19
-48
31
19
-59
M1
41
61
31
19
-43
31
19
-19
31
19
-33
31
19
-15
31
19
-31
31
19
-22
31
19
-35
31
19
-17
Rat
io o
f am
ylas
e to
tra
nsf
erri
nSerum densitometry
AMY1 AMY2A AMY2B
* * *
*
Serous OC
Mucinous OC
Clear cell OC
Ovarian tumor thecoma
Endometrial Cancer
Benign gynecological disease
Ovarian Anaplastic metastatic Carcinoma
Mature cystic Teratoma
70
Given the common evolutionary background and shared sequence homology, it is not
surprising that the amylase isozymes share biochemical and structural features despite differential
tissue expression. Computational analyses indicated that the amylase isozymes are composed of
511 amino acids, have an average computational molecular weight of 57 kDa and an average
computational isoelectric point of 6.57. The amylase isozymes also bind chloride and calcium as
enzymatic cofactors. This, coupled with predictions that the amylase isozymes are hydrophilic,
suggests that amylase is secreted from cells, in keeping with its known digestive function.
Structurally, the amylase isozymes share a common structure dominated by an amino terminal
transport signal site and a catalytic domain. The amylase isozymes also share common
phosphorylation, glycosylation, and calcium, chloride and glucose binding sites.
Computational analysis was not always in agreement with known properties of amylase
reported in literature. Both computational and known literature reported the homology of the
amylase isozymes as being above 90%; however, the amylase isozymes are much more
homologous according to the computational analysis. Literature reported molecular weight and
isoelectric point did not differentiate between the AMY2A and AMY2B making the computational
analysis of the amylase isozymes size more specific. There was no agreement between the results
of the computational analysis and literature reported domains of the amylase isozyme, the position
of the active sites or the calcium and chloride binding sites. This discrepancy may be due to the
different algorisms used by the computational programs and may not accurately depict biological
conditions. It may also be that experimental validation of the new biological knowledge found by
computational programs has not been done.
In order to begin to understand the role of and mechanisms contributing to hyperamylasemia
seen in OC, I performed computational analyses to identify unique features of the amylase
71
isozymes in order to predict which isozyme would most likely be overexpressed in OC-associated
hyperamylasemia. Computational analyses highlighted new features of the amylase isozymes not
previously reported (Table 2.4). They include the following:
First, potential unique post-translational modifications, including novel glycosylation and
phosphorylation sites, were identified among the amylase isozymes. Glycosylation is a regulated
and reversible modification including the covalent bonding of a sugar moiety of variable length
and arrangement to a protein residue [309]. Glycosylation plays a role in folding and structural
stability of protein, protein localization and interactions as well as immune responses and
modifying cell signaling. Glycosylation of proteins confers a more stable configuration that is less
prone to degradation [310–312]. Glycan dysfunction can lead to cancer, diabetes and liver cirrhosis
[209,313,314], therefore, additional glycan sites at residues 411 for AMY1 and residues 423 and
411 for AMY2B may lead to enhanced protein stability.
Phosphorylation is a flexible, simple, reversible regulatory mechanism utilized by 30% of
human proteins via protein kinases. Phosphorylation can alter protein activity, stability, function,
and interactions. Abnormal phosphorylation is often a cause or consequence of human disease
[208,315]. Phosphorylation affects intracellular signaling cascades that regulate metabolism,
enzyme reactions and protein degradation [209] so a unique phosphorylation site at amino acid
residue 133 in AMY2B (according to DEPP) can be responsible for increased protein stability and
possibly more protein-protein interactions. These potential posttranslational modifications may be
key for amylase function. Together, these unique and/or aberrant posttranslational modifications
could alter amylase transcription, secretion or enzymatic activity contributing to overexpression
of amylase in cancer.
72
Table 2.4. Distinguishing computational characteristics among amylase isozymes
AMY1 AMY2A AMY2B
Tissue specificity Salivary gland Pancreas None
Unique regions of
disorder
Amino acid 51-63,
117-134 and 375-384
- Amino acid 51-63 and
117-134
Unique calcium
binding residues
N115 N115, H216 H216
Unique chloride
binding residues
- 269 T269
Unique glucose
binding residues
- T178, G321 V178, G321
Unique 3D structure - + -
Unique regions of
aggregation
- 2, 64-68, 97-98, 116,
118, 197, 279-281,
325-332,340-345,
355-356, 383-386,
407-415, 439, 456,
478-480
2, 26, 40, 97-98, 116,
176-182, 325-332,
340-345, 355-356,
383-386, 408-409,
413-414, 439, 464-466
Unique
phosphorylation
sites
- - Amino acid 133
Unique
glycosylation sites
N-linked: residue 411 - N-linked: residue 411
O-linked: residue 123
Unique interacting
proteins
LCT, BPI, COL10A1,
AKAP1, AKAP8
MGAM, TSHB,
GP2, PNLIP, ENO1
TSHB, ATP1A4,
ATP1A1, CD2, PGM1
Unique transcription
factors
C/EBP alpha, Zic1/2,
STAT5A
C/EBP alpha and
Zic1/2
POU1F1 and Cdx-1
No. of mutations 4 41 86
* Common features of the amylase isozymes are discussed in the results section. Table indicates
the unique characteristics of the different amylase isozymes.
Sumoylation is the reversible attachment of a Small Ubiquitin-related MOdifiers (SUMO) to
proteins. SUMO protein is added to a specific peptide sequence of a hydrophobic residue bound
to a lysine bound to any amino acid followed by an acidic residue [316]. Sumoylated proteins
appear to take a part in transcription [211,212], apoptosis [213], and signal transduction [214].
While the mechanisms that regulate sumoylation are not well understood, conjugation of SUMO
to proteins can alter protein configuration potentially leading to new protein-protein interactions,
73
inhibition of existing protein-protein interactions and/or changes to existing protein functions and
activity [215], thereby enhancing functional diversity. In this way, sumoylation can increase
protein stability by promoting the formation of multimeric protein complexes as well as by
inhibition of ubiquitination [216]. Since all amylase isozymes are predicted to be sumoylated, it is
expected that sumoylation imparts stability and may result in a longer half-life due to lack of
ubiquitination and, subsequently, degradation.
Second, protein function is determined by protein structure which, in turn, is based on protein
sequence. Consequently, evolutionarily related proteins have similar structural motifs and the
structural similarities between the amylase isozymes are indicative of their evolutionary history
[317]. In agreement, I found that all the amylase isozymes are generally highly ordered proteins
with long half-lives. Therefore, the amylase isozymes have a stable secondary and/or tertiary
structure. Since ordered proteins have low binding potential to other proteins and usually do not
form complexes, it is unlikely that amylase forms larger protein complexes [205]. However, I also
found that AMY1 and AMY2B have unique internal regions of protein disorder. AMY1 has unique
disorder regions at amino acids 51 to 63 and 375 to 384 while AMY2B has a unique disorder
profile at amino acids 51 to 63 and 117 to 134. These internal regions of protein disorder in AMY1
and AMY2B represent zones of flexible protein configurations that: (1) are more prone to
posttranslational modifications (such as the unique phosphorylation site at amino acid 133 in
AMY2B) that increase protein functionality; (2) may function as linkers between the two
structured domains of the amylase isozymes or; (3) may confer increased binding potential to the
structured domains of other proteins [317].
Third, amylase function may be related to protein aggregation inherent in amylase protein
structure. Alpha-amylase was found to be more prone to aggregation as compared to β-amylase
74
due to the presence of cysteine residues on the surface of amylase which help form intermolecular
sulfide bonds that covalently-link amylase dimers [318]. Computational analyses revealed that the
amylase isozymes share multiple, common aggregation prone regions, but AMY2A and AMY2B
have additional unique regions of aggregation. Unique aggregation prone regions were mostly
found in AMY2B, indicating it is most likely to aggregate. Aggregation prone regions are more
likely to be in ordered regions and stabilize proteins. Aggregation prone regions are also very likely
to be found in close structural proximity to catalytic residues further indicating that aggregation
prone regions contribute to protein function. Aggregation prone regions can be conserved in
proteins to maintain protein stability and function [206]. AMY2A and AMY2B have unique
regions of aggregation which is in keeping with their evolutionary history. Unlike AMY1, since
AMY2B is the oldest of the amylase isozymes, it has the most conserved ordered protein regions
which is in keeping with the view that intrinsically ordered proteins are associated with high self-
aggregability. This was further supported by analyses of tertiary amylase structure. Specifically,
alpha helixes are the most common secondary structure to transverse the membrane due to their
ability to maintain more polar structures inside the helix [319] while beta sheets play a large part
in amyloid formation [320]. The pancreatic amylase isozyme AMY2A has more alpha helix
structure indicating it may more readily transverse the membrane than AMY1 and AMY2B
amylase isozymes.
The salivary glands and pancreas account for most of the amylase activity in the body.
Increased amylase expression and activity (hyperamylasemia) has been associated with insult to
the pancreas or salivary glands, chronic alcoholism, amylase secreting cancers and anorexia
nervosa or bulimia. Hyperamylasemia is due to an overproduction of amylase that is secreted into
the serum as well as decreased amylase clearance. Decreased amylase clearance may be due to
75
macroamylasemia, an asymptomatic condition wherein amylase aggregates into high-molecular
weight complexes containing immunoglobulins [321]. Reportedly, hyperamylasemia and
macroamylasemia can occur as secondary conditions to cancer [322]. The amylase proteins
displayed different potential aggregation-prone regions. Since AMY2A and AMY2B may be more
prone to self-aggregate because they have more aggregation prone regions, they may contribute to
macroamylasemia [323].
Fourth, while the ordered nature of the amylase isozymes typically precludes their physical
interaction with other proteins, computational analyses predicted several potential functional
amylase-protein interactions, mostly associated with carbohydrate metabolism. However, unique
functional interactions among amylase isozyme family members were also noted and might
represent novel functions limited to specific amylase isozymes. That is, AMY1 has unique
interactions with lactase (LCT), kinase A anchor protein 1 (AKAP1), kinase A anchor protein 8
(AKAP8), collagen, type X, alpha 1 (COL10A1), and bacterial/permeability-increasing protein
(BPI). These unique interactions indicate that AMY1 might participate in a variety of established
and novel functions including carbohydrate metabolism, extracellular matrix remodeling, and cell
immunity. AMY2A has interactions with maltase-glucoamylase (MGAM), glycoprotein 2 (GP2),
enolase 1 (ENO1), pancreatic lipase (PNLIP), and TSHB. These unique interactions indicate that
AMY2A could participate in a variety of functions including carbohydrate and lipid metabolism
and cell immunity. With possible involvement in lipid metabolism, AMY2A may play a role in
alternate energy production. AMY2B has interactions with phosphoglucomutase 1 (PGM1), CD
molecule (CD2), ATPase, Na+/K+ transporting, Alpha 4 Polypeptide (ATP1A4), Alpha 1
Polypeptide (ATP1A1), and TSHB. These unique interactions suggest that AMY2B could also
participate in carbohydrate metabolism as well as ATP production and cell immunity.
76
Interestingly, all amylase isozymes functionally interact with different immunity related proteins,
indicating that amylase may modulate the immune response. These novel interactions point to
potential alternative functions of the amylase isozymes. It is important to note that all the proteins
that interact with amylase do not form physical interactions, but have functional interactions. There
is no indication that amylase forms complexes with other metabolic enzymes. However, it has
been proposed that amylase is sequestered in normal tissue and during inflammatory events,
amylase is released into the blood [324]. Consequently, the high levels of tissue amylase may be
related to an anti-bacterial role [325].
Fifth, unique amylase isozyme expression profiles may arise through differential gene
expression regulated, in part, by different transcription factors. The current study revealed that
AMY1 transcription may be regulated by the C/EBP alpha, C/EBP delta, zic1/2, and STAT5a
while AMY2A is regulated by C/EBP alpha, C/EBP delta and zic1/2 and AMY2B is regulated by
C/EBP delta, POU1F1 and Cdx-1. The function of the differentiation-inducing C/EBP
transcription factor family has been found to be abrogated in hematopoietic, lung and skin cancers
[326]. C/EBP beta is overexpressed in OC epithelial cells in vivo and C/EBP alpha and delta have
constant expression levels regardless of tumor stage/progression [327]. Zic1 plays a role in cancer
progression in medulloblastomas, endometrial cancers, colorectal and mesenchymal neoplasms
such that Zic1 is downregulated during transformation [328]. In breast cancer, loss of STAT5A is
associated with poor clinical outcome and advanced tumor progression [329]. Not much is known
about the role many of these transcription factors play in OC in particular though it is clear that
they play a large role in cancer progression in other cancer types.
In addition to all the above, all three amylase isozymes had TATA boxes in their promoter
regions while only AMY1 and AMY2B have CG boxes in their promoter regions and only
77
AMY2A has CCAAT boxes in its promoter region. Core promoter elements determine
transcriptional regulations by governing which transcription factors are recruited. The TATA box
is a DNA sequence that specifies where transcription initiates by binding transcription factors.
TATA boxes are typically found in highly regulated genes [330]. The GC box is usually found
upstream of the TATA box and acts like a transcriptional enhancer that binds transcription factors
[331]. The CCAAT box also occurs upstream of the transcription start site and is known to be part
of the core promoter. Interestingly, the C/EBP proteins are highly conserved CCAAT enhancer-
binding proteins with multiple functions in proliferation, metabolism, immunity and inflammation
[332–335]. This indicates that the amylase isozymes have different core promoter and enhancer
regions that recruit transcriptional factors by unique methods.
Lastly, computational analyses indicated that the number of potential mutations among
amylase isozymes was greatest in AMY2B (with 86 possible mutations) compared with AMY2A
(with 41 possible mutations) and AMY1 (with 4 possible mutations). It is also interesting that the
AMY1 mutations are located at the end of the alpha-amylase catalytic domain while mutations in
AMY2A are localized mutations mostly in the N-terminal and alpha-amylase catalytic domain.
This suggests that the alpha-amylase catalytic domain may be prone to mutations. In contrast,
AMY2B has mutations uniformly distributed throughout its protein. Nonetheless, mutations of
amylase isozymes often lead to amplification and could explain overexpression leading to
hyperamylasemia in OC.
Despite many similarities among the amylase isozymes, computational analyses revealed
significant differences among these isozymes based on regions of protein disorder, calcium
binding sites, glycosylation sites, phosphorylation sites, regulatory transcription factors, tertiary
structure, aggregation prone regions and susceptibility for mutational events. Consequently,
78
structural, functional and regulatory differences among the amylase isozymes may contribute to
differential tissue expression in disease. The computational differences among amylase isozymes
leads me to believe that AMY2A and AMY2B may be more prone to self-aggregation. AMY1 and
AMY2B have unique regions of disorder indicating these isozymes may mediate more
interactions, and be more susceptible to mis-folding and aggregation. AMY1 and AMY2B have
identical tertiary structures indicating AMY1 and AMY2A may have similar interactions and
patterns of secretion. AMY2B is endogenously expressed in many tissues due to its lack of a
retroviral insertion that induces tissue specificity. During malignant transformation, AMY2B
expression may be exacerbated by genetic deregulation.
Taken together, my computational analyses predict that AMY2B and, to a lesser extent,
AMY1 may be the amylase isozymes overexpressed in OC-associated hyperamylasemia. While
my computational analyses suggest that AMY2B RNA is overexpressed in OC and elevated levels
of AMY2B amylase have been anecdotally reported in OC serum and tissues, information about
the specific amylase isozyme overexpressed at the protein level in OC disease is lacking.
Therefore, to validate my computational predictions, serum samples from normal controls and
from women with benign gynecologic disease and reproductive cancers were assessed by amylase
isozyme protein expression by western immunoblotting. I was able to confirm that, compared to
normal controls and benign disease, AMY2B was preferentially overexpressed at the protein in
serous ovarian cancers, the most frequently occurring ovarian histologic subtype. Consequently,
computational strategies can serve as a first step to help identify novel protein regulators for OC,
such as AMY2B, which can then be expanded in further studies on the molecular mechanism(s)
contributing to OC progression.
C/EBP alpha C/EBP delta STAT5A
vitamin D
C/EBP alpha
79
Chapter 3
Amylase promotes OC cell invasion in vitro
Introduction:
Hyperamylasemia has been reported in OC [262,336,337], specifically in women with serous
ovarian tumors [338] and ovarian cystadenocarcinoma [230]. Zakrezewska et al. found that
amylase is overexpressed in 39% of OC patients [339]. Hyperamylasemia is associated with poor
prognostic outcome including rapid disease progression and increased mortality; it has been
suggested that amylase is a marker of disease progression as amylase activity only decreases in
response to treatment and rapidly increases when diseases relapse [340–343]. However, its
contribution to OC disease etiology remains unknown [1]. Nonetheless, since hyperamylasemia in
OC patients is reduced after ovarian tumor removal or treatment, elevated serum levels of amylase
in patients with hyperamylasemia are thought to result by secretion of amylase by OC cells
[269,336].
My computational analyses predicted AMY1A and AMY2B as the most likely amylase
isozymes overexpressed in OC, and western blot analysis of OC patient serum samples confirmed
high levels of AMY1 and AMY2B in OC (Chapter 2). To begin to determine the contribution of
amylase for OC progression, in this chapter, an in vitro model of amylase secretion by OC cells
will be developed. The functional contribution amylase for OC will be evaluated by measurement
80
of cellular proliferation and invasion as well as the ability for amylase to degrade ECM
components. Lastly, by demonstrating amylase within the cellular glycocalyx and the ECM
environment, I propose a role/mechanism by which amylase may not only drive energy production,
but invasion by OC cells.
Materials and Methods:
Tissue culture
The following human cell lines were used: normal human dermal fibroblasts HDF; SV 40-
Large T-Antigen transfected human ovarian surface epithelial (IOSE) cell lines MCC3, IOSE-80,
IOSE-121, and IOSE-144 derived from normal patients with no family history (NFH) of breast
and/or OC [344,345]; OC cell lines CaOV3, OVCAR5, OV-90, SKOV3, TOV21G and IGROV;
colorectal adenocarcinoma cancer cell lines WiDr and SW626; breast cancer cell lines MDA-MB-
231 and MCF7; head and neck cancer cell line HN5α [346]; cervical cancer cell lines Hela, C13,
OV2008; pancreatic cancer cell line Panc1; lung cancer cell lines NCI-H2009 and H69; and
glioblastoma U373MG cells. Cells were cultured in Medium 199/MCDB 105 (Sigma, St. Louis,
MO) with 5% fetal bovine serum (FBS) and gentamicin and incubated at 37°C with 5% CO2.
Media was concentrated using Centriprep Centrifugal Filter Devices (Ultracel YM-3 Cat.4302
Merck Millipore Ltd, Tullagreen, Ireland).
Transmission Electron Microscopy (TEM)
Cells were grown on routine tissue culture plasticware in a monolayer on a 13mm coverslip in
6 well plates overnight. Adherent cells were fixed with 75 mM lysine in 0.075% (w/v) alcian blue,
2% paraformaldehyde (PFA), 2.5% glutaraldehyde (GA) in 0.1 M cacodylate (CAC) buffer
solution, buffered at pH 7.2, for 0.5 hour at 37°C. The coverslips were then washed for 1.5 hours
at room temperature followed by storage at 4°C overnight. Coverslips were then rinsed three times
81
for five minutes with 0.1 M CAC, pH 7.2 and postfixed with 1% osmium tetroxide-potassium
ferrocyanide (OsO4/0.8% K4Fe(CN)6) in 0.1M CAC, pH 7.2 for 1 hr. Coverslips were then rinsed
for five minutes with 0.1M CAC, pH 7.2, and washed with dH2O twice for five minutes. Coverslips
were dehydrated with a graded ethanol (ETOH) series: 35%, 50%, 70%, and 95% ETOH followed
by three exchanges of anhydrous 100% ETOH prior to critical point drying via three ten minute
washes in 100% acetone. For resin infiltration, cells were incubated in graded acetone/Epon 812
resin solutions: 2:1 acetone to resin solution for ten min, 1:1 acetone to resin solution for 48 hours,
1:2 acetone to resin solution for two hours and pure resin twice for two hours each. The coverslips
were then cut into 2mm x 6.5mm segments and baked on pure resin at 40°C for 2-4 hours and at
70°C overnight to polymerize the resin. The coverslips were then sectioned, examined and
photographed on a Zeiss EM 30 electron microscope. Cell cultures were screened for yeast
contamination and contaminated cultures were replaced.
Quantitative PCR
Cells were treated with TRIzol reagent from Invitrogen (Carlsbad, CA) and ribonucleic acid
(RNA) was isolated according to the manufacturer’s instructions. To generate single-stranded
complementary DNA (cDNA), the Applied Biosystems GeneAmp RNA polymerase chain
reaction (PCR) Core Kit (Foster City, CA) was used with 3 ug/mL total RNA using a Biometra
UNO-thermo-block and Perkin-Elmer-GeneAmp PCR system 9600. qPCR was carried out with
SYBR Green Universal Master Mix (Applied Biosystems), cDNA, and primers. Primers used were
AMY1A (GeneCopoeia: Hs-QRP-21480; Rockville, MD), AMY2A (GeneCopoeia: Hs-QRP-
21450), AMY2B (GeneCopoeia: Hs-QRP-21451; Rockville, MD), and GAPDH (GeneCopoeia:
Hs-QRP-20169; Rockville, MD) as control. Sequences for the amylase primers remain the
proprietary property of GeneCopoeia. Amplification was performed with 40 cycles of denaturation
82
(95°C, 10 sec), annealing (60°C, 20 sec), and extension (72°C, 15 sec) using a Bio-RAD Chromo4
Real Time PCR Detector using Opticon Monitor 3 program. Levels of amylase were normalized
to their respective GAPDH message values and the fold difference was determined by dividing the
threshold cycle (Ct) value by the reference sample.
Western blot
Cells were washed in PBS, trypsinized, pelleted, rewashed with PBS and counted. Cells
were lysed in CHAPS buffer for 30 minutes on ice before being centrifuged at 115,000 x g, at 4°C
for 1h. Twenty ug of protein were separated by a 10% sodium dodecyl sulfate polyacrylamide gel
electrophoresis (SDS-PAGE). Proteins were transferred to Polyvinylidene fluoride (PVDF)
membranes, washed in Tween 20-Tris buffered Saline (TBST), and blocked in 5% milk in TBST.
Blots were incubated in their respective primary antibodies overnight, followed by incubation with
a horseradish peroxidase (HRP)-conjugated secondary antibody (Fisher, Pittsburgh, PA), and
developed via FEMTO Cat. 34095 Life Technologies (Grand Island, NY). Antibodies used were:
AMY1A (1:1000) Cat.H00000276-M04 Abnova (Taipei City, Taiwan); AMY2A (1:500) 15845-
1-AP Proteintech (Chicago, IL); AMY2B (1:2000) Cat. LS-C173958 LSBio (Seattle, WA); Beta
Actin Loading Control Antibody (BA3R) (1:10,000) Cat. # MA5-15739 Thermo Scientific
(Rockford, IL). Densitometry was performed using ImageJ software to normalize amylase band
strength to the actin band.
Amylase ELISA
To measure amylase protein levels in OC samples and/or their conditioned medium, 100ul
of samples corresponding to 104 cells were assayed in triplicate using the quantitative double
sandwich enzyme-linked immunosorbent assay (Amy-p ELISA Kit; Cat. MBS264855;
MyBioSource, San Diego, CA). Absorbance was read on a Microplate Reader BioTek ELx800
83
(Winooski, Vermont) using Gen5 Data Analysis Software (Biotek, Winooski, Vermont). Resultant
values were derived from a standard curve and expressed as the mean amylase concentration of
triplicate samples.
Amylase activity assay
The Amylase Assay Kit (Cat: ab102523 Abcam, Cambridge, MA) was used to measure
amylase activity according to the manufacturer’s instructions from concentrated conditioned
media (CCM). The CCM corresponding to 104 cells was incubated with ethylidene-pNP-G7, a
substrate that is cleaved by amylase into smaller fragments targeted by α-glucosidase to release a
chromophore whose concentration was measured at 405 nm using μQuant plate reader from Bio-
tek instruments, Inc. (Winooski, VT).
Amylase transfection
For silencing experiments, IOSE and OC cells were split into four groups, including a
control group supplemented with only the transfection reagent, a negative control group
transfected with a non-targeting control scramble siRNA and the transfection reagent, a third group
supplemented separately with the three different amylase siRNAs and the transfection reagents
and, lastly, a fourth group transfected with GFP to monitor transfection efficiency. Cells were
transfected with ON-TARGETplus Human AMY1A siRNA-SMARTpool (Cat. L-012691-01-
0010), ON-TARGETplus Human AMY2B siRNA-SMARTpool (Cat. L-015880-01-0010), and
ON-TARGETplus Human AMY2A siRNA-SMARTpool (Cat. L-016978-01-0010) or ON-
TARGETplus Non-targeting scramble siRNAs (Cat. D-001810-01-0010) GE Healthcare
Dharmacon (Lafayette, CO). The SMARTpool is a mix of four siRNAs provided as a single
reagent. Cells were plated in 6 well plates, serum starved overnight then treated with 145 pmol
siRNA per well incubated with Lipofectamine 3000 Transfection Reagent (Invitrogen, Madison,
84
WI, United States) according to the manufacturer’s instructions. Transfection efficiency was
determined by transfecting cells with pmaxGFP™ Control Vector (Cat. VCA-1003) Lonza (Basel,
Switzerland). Transfected cells were maintained for 48 h before further experiments, unless
otherwise described.
Invasion assay
Cells were incubated on type I collagen coated transwells (CytoSelect 96-Well Cell Invasion
Assay, catalog number: CBA-112-COL (San Diego, CA)) for 24 hours. Following incubation,
cells that had invaded through the collagen gel and onto the transwell membrane insert were
stained with 0.1% crystal violet solution and photographed using an Olympus DP20 digital camera
(Burnaby, BC, Canada) under a Leica DMIRE2 microscope. The number of cells that invaded the
transwell was then counted and subjected to statistical analysis.
Glycosaminoglycan (GAG)/proteoglycan quantification assay
GAG content was determined using a Blyscan colorimetric assay (Biocolor Assays, County
Antrim, BT38 8YF, United Kingdom). After transfection with amylase siRNA, and scramble
RNA, 7x106 IOSE-121 and OVCAR5 cells were digested in a papain extraction reagent. The GAG
content in each sample was detected with spectrometry at 656 nm after precipitation and dye
binding of GAG according to the manufacturer’s instructions. A standard curve was created using
controls according to the manufacturer’s instructions.
Immunogold staining
For immunogold staining, cells were grown on coverslips and prepared for TEM analysis
as described earlier. Sectioned grids of cells were incubated in 4% w/v aqueous sodium
metaperiodate for two minutes, washed multiple times in water, blocked in PBSG (PBS containing
0.001% (v/v) Tween 20 and Triton X-100 and 1% (w/v) gelatin from cold water fish) for 10
85
minutes before incubation overnight in 6ug/ml anti-salivary alpha amylase antibody (Cat. ab54765
Abcam (Cambridge, MA)). The grids were then washed twice in PBS then eight times in water
before a two-hour incubation in goat-anti-mouse IgG (H&L) conjugated to 15nm colloidal gold
particles 1:40 (Cat. 25133 Electron Microscopy Sciences (Hatfield, PA)). The grids were rinsed in
H2O for 30-40 sec then counterstained in uranyl acetate (saturated) in 50% methanol for 2 minutes.
To confirm the specificity of the primary antibody, non-immune goat IgG were used as negative
control in place of primary antibody. The cells were then viewed by TEM.
Statistical analyses
For real time PCR, error bars illustrate RQmin and RQmax, which were calculated as: RQave
divided by (standard deviation^ student’s t value at the 95% confidence interval, for 5 degrees
freedom) and RQave times (standard deviation^ student’s t value at the 95% confidence interval,
for 5 degrees of freedom), respectively. This range represents the 95% confidence level. For
amylase activity assay, amylase ELISA, GAG assay and invasion assay, student’s t test was
performed to assess statistical difference between means of triplicates ± standard error. P-values ≤
0.05 were considered statistically significant.
Results:
Confirmation of yeast-free cell cultures.
Since cell culture contaminants, including commensal yeast, can produce amylase, it was
necessary to ensure that cell lines used for this study were free of microbial contaminants.
Examination of cell cultures by light microscopy did not detect presence of yeast in any cultures.
However, for more stringent examination, IOSE and OC cell lines were examined by TEM for
possible yeast contamination. IOSE-121, HIO118, SKOV3, OV-90 and OVCAR5 were found to
86
be free of yeast, while yeast was found in CaOV3 and IGROV (Figure 3.1). I replaced the yeast-
infected cell lines with yeast-free cell lines as validated by TEM visualization.
A. CaOV3:
B. IGROV:
Figure 3.1. Establishing cell cultures are free of yeast. Cells cultures were examined by TEM
to detect microbial contamination, including yeast. Representative photographs illustrate
endogenous yeast (arrows) in CaOV3 (A) and IGROV (B) ovarian cancer cell lines.
87
OC cells overexpress amylase in vitro.
Computational analyses predicted that AMY1 and AMY2B would be overexpressed in OC
(Chapter 2). A panel of cell lines was used to confirm which amylase isozymes are overexpressed
in established OC cell lines as well as from other cancer types at the message level using qPCR
(Figures 3.2 and 3.3) and at the protein level using western immunoblotting (Figure 3.4). At the
message level, there were variable levels of amylase expression in OC and normal cell lines (Figure
3.2). Moderate levels of AMY1 and AMY2B RNA were expressed in MCC3, while IOSE-121
essentially only expressed AMY1 RNA. As defined by the average signal ratio of
amylase/GAPDH in IOSE cells, only 2/5 (40%) of OC cell lines, OVCAR5 and IGROV,
overexpressed amylase AMY1 and AMY2B to a greater extent than IOSE cells. Though
expressing small levels of amylase message, SKOV3, OV-90, and TOV21G were shown to
express AMY1 and AMY2B RNA. Likewise, there was no clear pattern of which amylase isozyme
is overexpressed in other cancer types at the RNA level (Figure 3.3). Whereas human dermal
fibroblast (HDF) most notably expresses AMY1 RNA, lung cancer cells H2009 displayed an
overexpression of amylase AMY1 and AMY2A while amylase RNA was not detected in H69
cells. Likewise, colorectal cancer WiDr cells displayed an overexpression of amylase AMY1 and
AMY2B while SW626 cells expressed negligible amounts of AMY1. Similarly, cervical cancer
cells (Hela, C13) expressed small amounts of AMY1 and AMY2B. Cells lines representing breast
cancer, head and neck cancer, glioblastoma and pancreatic cancer expressed negligible amounts
of amylase although when detected, these cell lines preferentially expressed AMY1 RNA and,
occasionally, AMY2B (Figure 3.3).
88
At the protein level, all OC cell lines examined, TOV21G (8x for AMY1 and 4x for AMY2B),
OV-90 (13x for AMY1 and 8x for AMY2B), OVCAR5 (10x for AMY1 and 20x for AMY2B),
SKOV3 (5x for AMY1 and 16x for AMY2B) and IGROV (3x for AMY1 and 9x for AMY2B),
Figure 3.2. Amylase AMY1 and AMY2B RNA are typically expressed in OC cell lines.
Cellular RNA was collected from IOSE and OC cells and analyzed by qPCR to determine
AMY1, AMY2A, and AMY2B message expression. Data are expressed as the average ratio of
amylase isozyme expression to control GAPDH mRNA from triplicate samples.
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0.001
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
IOSE-121 MCC3 OVCAR5 SKOV3 OV-90 TOV21G IGROV
HOSE Cells OC
Rat
io o
f A
MY/
GA
PD
H m
RN
AAmylase messenger level in ovarian cells
Figure 3.3. Most non-OC cell types express AMY1 and AMY2B RNA. Cellular RNA was
collected from HDF and representative cancer cells and analyzed by qPCR to determine AMY1,
AMY2A, and AMY2B message expression. Data are expressed as the average ratio of amylase
isozyme expression to control GAPDH mRNA from triplicate samples.
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
0.0035
0.004
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
HDF H2009 H69 WIDR SW626 MDA231 MCF7 HN5A U373MG HELA OV2008 C13 PANC1
Fibroblast Lung Ca Colorectal Ca Breast Ca H&N Ca Glioma Cervical Pancreatic Ca
Rat
io o
f A
MY/
GA
PD
H m
RN
A Amylase messenger level in other cancer types
89
expressed 3x-20x more amylase AMY1 and AMY2B protein relative to IOSE-121 and IOSE-144
cells (Figure 3.4, Table 3.1). With the exception of U373MG, MDA231 and H2009 cells, cells
representing other cancer types, also expressed more AMY1 and/or AMY2B protein relative to
HDF. That is, breast cancer MCF7 (7x), colorectal cancer WIDR (2x) and SW626 (12x), pancreatic
cancer PANC1 (9x) and cervical cancer Hela (3x) overexpress AMY1; colon cancer cell line
SW626 (114x), and pancreatic cancer cell line PANC1 (137x) cells overexpress AMY2A. Breast
cancer cell line MCF7 (214x), colorectal cancer cell lines WIDR (74x) and SW626 (475x),
glioblastoma cell line U373MG (20x), pancreatic cancer cell line PANC1 (412x), and cervical
cancer cell line HELA (200x) overexpressed AMY2B (Figure 3.4, Table 3.1). Overall, protein
levels of the amylase isozymes were greater in cancer cell lines than normal cells and the amylase
isozymes most typically expressed are AMY2B and AMY1. Analysis of amylase at the protein
level showed that AMY1 and AMY2B are consistently elevated in OC cells, whereas, other cancer
types don’t show consistent amylase overexpression.
Figure 3.4: AMY1 and AMY2B protein are overexpressed in OC cell lines (Continued on Next
Page)
IOSE
-12
1
IOSE
-14
4
TOV
21
G
OV
-90
O
VC
AR
5
SKO
V3
IGR
OV
AMY1A
AMY2A
AMY2B
ACTIN
HD
F M
DA
23
1
MC
F7
WID
R
SW6
26
U3
73
MG
H2
00
9
PAN
C1
HEL
A
90
Figure 3.4. AMY1 and AMY2B protein are overexpressed in OC cell lines. HDF, IOSE and a
panel of cancer cells were subjected to western blot to determine the expression of amylase
isozymes at the protein level (upper panel). Densitometry analysis of was used to determine
the intensity ratio of amylase isozyme band signal to respective loading control actin bands
and are shown in corresponding lower panels.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
IOSE-121 IOSE-144 TOV21G OV-90 OVCAR5 SKOV3 IGROV
Rat
io o
f am
ylas
e to
act
inDensitometry of amylase in OSE and OC cells
AMY1 AMY2A AMY2B
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
HDF MDA231 MCF7 WIDR SW626 U373MG H2009 PANC1 HELA
Rat
io o
f am
ylas
e to
act
in
Densitometry of amylase in other cancer types
AMY1 AMY2A AMY2B
91
Table 3.1: AMY1 and AMY2B are typically overexpressed in cancer cells
Cancer type Cell line Amylase isozyme overexpressed
Ovarian TOV21G AMY2B
Ovarian OV-90 AMY1, AMY2A and AMY2B
Ovarian OVCAR5 AMY2B
Ovarian IGROV AMY2B
Breast MCF7 AMY1 and AMY2B
Colorectal SW626 AMY1 and AMY2B
Glioblastoma U373MG AMY2A
Lung H2009 AMY1 and AMY2A
Pancreas Panc1 AMY1 and AMY2B
Cervical Hela AMY1, AMY2A and AMY2B
Amylase secreted by OC cells is metabolically active.
To determine the proportion of amylase that remains in cell lysates and the proportion that
is secreted into the media, cytoplasmic lysates from IOSE-121, IOSE-144, OVCAR5 and SKOV3
cells and their respective CCM of equivalent 104 cells were subjected to an amylase ELISA.
Normal cells IOSE-121 (total 3 ng/ml) and IOSE-144 (total 2.26 ng/ml) produced negligible
amounts of amylase protein in cell lysate and CCM, whereas OVCAR5 (total 47.9 ng/ml) and
SKOV3 (total 8.9 ng/ml) produced 3x to 16x more amylase than normal cells with the majority of
amylase produced by OC cells secreted into their conditioned medium (Figure 3.5)
92
To determine if secreted amylase was metabolically active, CCM from a panel of cancer
cell lines was subjected to the amylase activity assay. All cancer cell lines secreted active amylase
ranging from 0.7 mU/ml to 10 mU/ml. Cervical cancer cell lines secreted active amylase ranging
from 0.7 mU/ml to 10.5 mU/ml, averaging 4.9 mU/ml. Lung cancer cell lines secreted active
amylase ranging from 2.6 mU/ml to 3.7 mU/ml, averaging 3.2 mU/ml (Figure 3.6). Levels of
amylase activity by IOSE cells ranged from 1.5 to 3.7, averaging 2.4 mU/ml. OC cells secreted
metabolically active amylase to a greater extent than IOSE cells, ranging from 5.1 mU/ml to 34.2
mU/ml, averaging 11.2 mU/ml so that OC cells secreted up to 4x more active amylase compared
to normal OSE cells (Figure 3.6). Interestingly, only the glioblastoma (U373MG) and a single
cervical cancer cell line (C13) secreted greater amounts of metabolically active amylase (6.5 &
10.5 mU/ML) than IOSE cells. All other remaining cancer cell lines examined secreted amounts
of metabolically active amylase within a similar range seen in IOSE cells.
Figure 3.5. OC cells produce more amylase than normal cells. Cell lysates and their CCM were
subjected to amylase elisa to determine the amounts of amylase in cell lysates and secreted into
the cell media. Data are expressed as the average of triplicates ± standard deviation (*P < 0.05).
0
10
20
30
40
50
60
IOSE-122 IOSE-144 OVCAR5 SKOV3
ng/
ml a
myl
ase
pro
tein
Cell lysate CCM
*
*
93
Inhibiting amylase decreases OC invasion.
To begin to understand how amylase promotes OC progression, I investigated whether
abrogation of amylase affected the invasive capacity of OC cells using collagen coated Boyden
chambers. Amylase knockdown was performed using pooled amylase siRNAs targeting all
amylase isozymes. Amylase knockdown was validated by qPCR for amylase mRNA levels
(Figure 3.7A). Compared to untreated cells, siAMY abrogated AMY1 and AMY2B RNA levels
in OVCAR5 cells up to 3.7x and 7.8x, respectively. While scramble RNA inhibited AMY2B to
a lesser extent than siAMY, it did not alter AMY1 expression. As determined by parallel GFP
signals, transfection efficiency in OVCAR5 and IOSE 121 was determined to be 80% and 76%,
respectively. Amylase knockdown reduced OC cell invasion especially in SiAMY-treated OC
0
5
10
15
20
25
30
35
40m
U/m
l (*
10
-7)
amyl
ase
Lung
cancer
Cervical
cancer
Normal HOSE
OC
Figure 3.6. OC cells secrete more metabolically active amylase than IOSE cells. CCM,
equivalent to 104 cells, was subjected to the amylase activity assay. Results are expressed as
the average amylase activity in mU/ml, normalized for cell number from triplicate samples
± SE.
94
cell line OVCAR5 by 32% (Figure 3.7B). Scramble RNA did not alter invasive capacities of
OC cells. Since IOSE121 cells have little amylase expression, reduction of their amylase
expression did not significantly affect IOSE121 invasion (Figure 3.7). After transfection, cell
numbers did not change, therefore, amylase does not affect cell proliferation. Cells were
counted after transfection with scramble RNA and amylase SiRNA for 48 hours and an
insignificant percent change ranging from 0.5% to 5.3% was noted.
The glycocalyx of OC cells is thicker than the glycocalyx of IOSE cells:
Given that amylase appears to mediate OC cell invasion and that amylase is an extracellular
metabolically active enzyme, I proceeded to examine whether amylase could modulate the
ECM/tumor microenvironment. To begin, IOSE121 cells and OC cells were examined by TEM
to determine the thickness of their respective glycocalyces. As shown in Figure 3.8, the average
glycocalyx thickness measured from 10 fields of IOSE-121, IGROV, and CAOV3, OVCAR5 and
SKOV3 was 83.7 nm, 207.4 nm, 264.8 nm, 257.2 nm, and 263 nm, respectively. The glycocalyces
of OC cells, then, were 2.4x to 3.2x thicker than the glycocalyx of IOSE121 cells (Figure 3.8).
In order to localize amylase to the OC cell surface and/or its immediate ECM, immunogold
labeling for amylase was performed. Immunogold labeling illustrated the presence of anti-amylase
gold particles in OVCAR5 cells (Figure 3.9). Immunogold particles appeared localized at the
external cell membrane and within the immediate tumor microenvironment. Control sections failed
to demonstrated presence of immunogold particles.
95
Figure 3.7. Abrogation of amylase reduces invasion in OC cells. A) IOSE121 and OVCAR5
cells were treated with siRNA targeting amylase and scramble RNA as control. Verification
of siRNA efficiency was validated by qPCR. B) Treated cells were then seeded into invasion
assay chambers and number of invasive cells were evaluated after 24-hour incubation. Results
are expressed as the average relative fluorescence (a measure of the number of invasive cells)
from triplicate samples ± SE (*P < 0.05).
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
Untreated scramble RNA AmylaseSiRNA
Untreated scramble RNA AmylaseSiRNA
IOSE-121 OVCAR5
Rat
io o
f am
ylas
e/G
AP
DH
mR
NA
A
0
5000
10000
15000
20000
25000
30000
35000
40000
Untreated Scramble RNA AmylaseSiRNA
Untreated Scramble RNA AmylaseSiRNA
IOSE-121 OVCAR5
Inva
sio
n (
rela
tive
flu
ore
sce
nce
)
Abrogating amylase reduces invasion
*
B
96
Fig
ure
3.8
. T
he
gly
coca
lyx
of
OC
cel
ls i
s th
icker
than
IO
SE
cel
ls.
IOS
E a
nd O
C c
ells
wer
e
exam
ined
b
y
TE
M
for
vis
ual
izat
ion
of
thei
r re
spec
tive
gly
coca
lyce
s.
Rep
rese
nta
tive
photo
gra
phs
IOS
E a
nd O
C m
orp
holo
gy a
t th
e ult
rast
ruct
ura
l le
vel
(upper
pan
el)
and r
espec
tive
gly
coca
lyce
s as
dep
icte
d w
ith l
ines
(lo
wer
pan
el).
8
3.7
207.4
264.8
2
57.2
263
Av
erag
e gly
coca
lyx
thic
knes
s (n
m)
mea
sure
d f
rom
10
fie
lds
OS
E-1
21
IG
RO
V
C
AO
V3
O
VC
AR
5
SK
OV
3
120K x magnification
5K
x
1
0K
x
6K
x
1
5K
x
8K
x
97
Figure 3.9 Amylase can be localized to the OC cells glycocalyx/cell surface. OVCAR5 cells
were examined by TEM following immunogold labeling with anti-AMY antibodies (upper
panel) or serum controls (lower panel).
98
Inhibiting amylase increases GAG production.
In order to better understand how extracellular amylase could promote OC cell invasion,
IOSE-121 and OVCAR5 cells were treated with scramble RNA and siRNA targeting amylase and
subjected to the Blyscan Sulfated Glycosaminoglycan Assay to measure sulfated glycan
(proteoglycans and glycosaminoglycans) content at the same time as my Boyden chamber
experiments. Successful validation of transfection is shown in Figure 3.7A. Abrogating amylase
in IOSE-121 cells slightly increased cellular GAG content over untreated IOSE-121 cells from
0.11 ug GAG to 0.33 ug GAG. In contrast, abrogating amylase increased cellular GAG levels in
OVCAR5 over untreated or scramble RNA treated cells from 1.382 ug to 3.787 ug (Figure 3.10).
0
0.5
1
1.5
2
2.5
3
3.5
4
Control Scramble RNA Amylase SiRNA Control Scramble RNA Amylase SiRNA
IOSE-121 OVCAR5
Tota
l ug
sGA
G
sGAG increases after abrogating amylase expression
Cell lysate
Figure 3.10 Amylase promotes GAG digestion. IOSE 121 and OVCAR5 cells were treated
with scramble RNA and amylase siRNA and subjected to the sulfated glycosaminoglycan
assay. Results are expressed as the total ug of GAGs from triplicate samples ± SE (*P < 0.05).
*
99
Discussion:
Amylase (originally known as ptyalin) was discovered in 1831 [347] and has been
extensively studied since its isolation in 1862 from pancreatic trypsin [348]. Since Weiss et al.
(1950s) reported a case of an amylase-producing tumor, multiple cases of cancer-associated
hyperamylasemia have been reported although hyperamylasemia appears limited to few types of
cancers [273] Nonetheless, hyperamylasemia has been strongly associated with lung cancer,
ovarian cancer, multiple myeloma and pheochromocytoma cancers [349]. Hyperamylasemia is
typically asymptomatic and its cause, isozyme profile and contribution to cancer progression are
still unknown. Salivary amylase (AMY1) and pancreatic amylase (AMY2B) have both been
proposed to be overexpressed in cancer. AMY2A has also been proposed to be a tumor suppressor
gene; mutation, deletion, or silencing of AMY2A may lead to malignant transformation in gastric
cancer [350]. Since OC remains the most lethal gynecologic cancer, studies on the contribution of
hyperamylasemia to OC are warranted, but require the establishment of an appropriate model
system.
Potentially owing to differences in reproductive physiology, humans are the only animals in
which OC occurs with regular frequency. Laying hens are the only animals that spontaneously
develop OC, therefore, a laying hen model of OC was characterized to determine its validity as an
in vivo model of OC [351]. Laying hens spontaneously developed serous, endometrioid, mucinous
and clear cell ovarian carcinomas with staging, histology and metastatic characteristics similar to
human OC conditions. However, laying hens only have one ovary, making staging ovarian cancer
more challenging in hens. Also, lymph nodes in chickens are not well organized so OC metastasis
in laying hens is not reflective of OC in humans [351]. Rabbit models for papillogenesis may be
informative for the very early stages of ovarian tumor growth, but these benign conditions
100
indicative of hyper-proliferative events do not progress to full malignant transformation [352].
Current mouse models of OC are also of limited use because they either employ intraperitoneal
(IP) or subcutaneous xenografts or are of genetic backgrounds that do not produce ovarian tumors
that histologically reflect serous adenocarcinoma of the human ovary. Of note, the most common
mouse models of OC employ xenograft model types which are effective in mimicking different
aspects of OC growth and metastasis. However, xenograft models do not reflect the initial
transformation events that lead to OC tumorigenesis. Genetically engineered mouse models
(GEMM) are important for investigating early changes that lead to OC; however, GEMMs are
complicated to generate due to lack of knowledge on OC etiology [353]. Therefore, I chose to use
an in vitro model system using established human cells lines to study the contribution of amylase
to OC progression. While a number of OC cell lines have been established and many are
commercially available, an important advantage for using an in vitro model for studies on the role
of amylase for OC progression is that normal control cell lines derived from pure human ovarian
surface epithelium and transfected with SV-40 large T antigen for enhanced lifespan are also
available. As a result, studies using IOSE cell lines have shown the capacity of OSE cells to
produce, lyse and reconstruct extracellular matrix components [344,345,354] and a demonstration
that OSE has the capacity to undergo epithelial-mesenchymal conversions [355].
As predicted in chapter 2, using cell lines devoid of yeast contamination, I found increased
protein levels of amylase AMY1 and AMY2B in OC cell lines. In addition, since the majority of
amylase produced by OC cells was both secreted and metabolically active, so these cell lines
represent a valid model system to pursue studies on the biological and molecular contribution of
amylase to OC disease.
101
The glycocalyx is the outermost layer of the cell composed of a negatively charged web of
enzymes, proteins, and glycoconjugates—one or more glycan units covalently bound to non-
carbohydrate units. Glycoconjugates include glycosaminoglycans (GAGs), glycoproteins,
glycolipids, and proteoglycans [356]. The glycocalyx is associated with many functions including
mediating cell-to-cell and cell-to-matrix interactions [357]. It also mediates ligand-receptor
interactions and other biological processes including specific recognition events. During malignant
transformation, atypical glycocalyx component expression and structure influences tumor
progression [356] by enhancing tumor growth, adhesion, motility, and invasion [358]. The
glycocalyx is also proposed to facilitate cancer progression [356] and cancer metastasis [359].
There is a correlation between glycoconjugates present at the cell membrane and the metastatic
potential of a cell [360]. A denser glycocalyx may promote more receptor interaction and lead to
more adhesion and migration of tumor cells [358]. It is also believed that the bulky glycocalyx of
malignant cells may serve a drug-resistant function by acting as a barrier against therapeutic agents
[358]. My survey of OC cell lines indicated that cancer cell lines have a bulkier glycocalyx
compared to normal IOSE cells.
Quintarelli et al. found that amylase degraded the glycan moieties that crosslink collagen
fibrils, therefore disassociating collagen fibrils and allowing for collagen degradation [361,362].
Inhibition of amylase decreased the ability of OC cells to invade collagen coated Boyden
chambers, thereby identifying a functional behavior of amylase to promote OC progression.
Amylase secreted by OC cells may, therefore, degrade the glycosylation links that play a role in
linking collagen fibrils to facilitate OC cell invasion. The role of amylase in the glycocalyx is
further supported by the presence of amylase in the glycocalyx and ECM as indicated with
immunogold studies. Amylase in the glycocalyx and ECM suggests that amylase may modify the
102
ECM to promote invasion. Amylase, which cleaves alpha 1, 4-glycosidic bonds in polysaccharides
to initiate carbohydrate metabolism and contribute to energy production, can also play a role in
ECM remodeling by cleaving polysaccharide moieties in the tumor microenvironment. Amylase
has already been shown to degrade connective tissue in bacterial biofilm in wounds [228].
In summary, to test the hypothesis that secreted amylase may modify the cancer cell glycocalyx
by digesting α1, 4-glycosidic bonds [235] found in glycosaminoglycans, which in turn leads to
altered downstream signaling that promotes proliferation, survival or migration/invasion, an in
vitro model to explore the contribution of hyperamylasemia to OC was established by analyzing
amylase message and protein levels from a panel of established cell lines. The activity of amylase
in cell lysates and conditioned concentrated media was also investigated. Lastly, to begin to
explore the contribution of hyperamylasemia to OC, the in-vitro model established was used to
determine how amylase contributes to OC cell invasion.
103
Chapter 4
Regulation of Amylase by Spirulina
Introduction
Using an in vitro model system, I have been able to demonstrate that elevated levels of
amylase in OC cells lines, representing hyperamylasemia, may drive disease progression, at least
in part, by promoting cellular invasion related to alterations in the tumor microenvironment
(Chapter 3). Amylase, then, may serve as a novel target for therapeutic intervention in OC patients
with hyperamylasemia. However, commercially available amylase inhibitors typically inhibit
amylase activity at the protein level and produce a variety of adverse gastrointestinal side effects
such as flatulence, abdominal distension, and diarrhea [363]. Therefore, there is a need for the
development of new amylase inhibitors. Spirulina, a dietary supplement, has recently gained
popularity with many health claims including that it enhances immunity, controls high blood
pressure and serum cholesterol levels and has anti-tumorigenic, anti-inflammatory and anti-
oxidant properties [364,365]. Spirulina is also associated with neuro-protective properties, as has
been shown by reduced ischemic brain damage in rats, improved post-stroke locomotor activity
and reduced levels of pro-inflammatory cytokines in the brain [366–368].
There are several species of the microalgae spirulina: Spirulina platensis, Spirulina
fusiforme, and Spirulina maxima. Spirulina has been used as a food source for centuries; with the
104
earliest recorded human consumption of spirulina by the Chadiansa of Africa and Aztecs of
Mexico [369]. Spirulina is a good nutritional source because of its high protein content; 60-70%
and 5-10% of spirulina’s dry weight is protein and lipid, respectively. Further, the protein in
spirulina is complete, meaning it contains all the essential amino acids. Spirulina also contains
many essential fatty acids (gamma-linolenic, linoleic and oleic acids) and, lastly, spirulina contains
high levels of vitamin B12, beta-carotene, phycocyanin, superoxide dismutase, iron, calcium and
phosphorous [369,370].
In this chapter, I propose employing spirulina or phycocyanin, one of the active ingredients
in spirulina with high anti-inflammatory potential [371], as novel transcriptional inhibitors of
amylase. By determining whether spirulina can inhibit OC cell invasion without cytotoxicity to
normal cells, a novel clinical use for spirulina will be established.
Materials and Methods
Tissue Culture
The following human cell lines were used: normal human dermal fibroblasts HDF; SV 40-
Large T-Antigen transfected human ovarian surface epithelial (IOSE) cell lines MCC3, IOSE-80,
IOSE-121, and IOSE-144 [344,345]; OC cell lines CaOV3, OVCAR5, OV-90, SKOV3, TOV21G
and IGROV; colorectal cancer cell lines WiDr and SW626; breast cancer cell lines MDA-MB-
231, MDA-MB-231, MCF7; head and neck cancer cell line HN5α [346]; cervical cancer cell line
Hela; pancreatic cancer cell line Panc1; lung cancer cell lines NCI-H2009 and H69; and
glioblastoma U373MG cells. Cells were cultured in Medium 199/MCDB 105 (Sigma, St. Louis,
MO) with 5% fetal bovine serum (FBS) and gentamicin and incubated at 37°C with 5% CO2.
Conditioned media was concentrated (CCM) using Centriprep Centrifugal Filter Devices (Ultracel
105
YM-3 Cat.4302 Merck Millipore Ltd, Tullagreen, Ireland). Cell lines were treated with ± 0-150
ug/ml spirulina (kindly provided by Dr. P Bickford, USF) or 1-100 ug/ml phycocyanin (Catalog
number: P2172-25MG; Sigma Aldrich; St. Louis, MO) for 0-72 h.
Microarray
HN5α cells were treated for 24h ± 100ug/ml spirulina, RNA collected, reverse transcribed,
amplified and labeled with biotin at the H. Lee Moffitt Cancer Center Microarray core facility.
Biotin-labeled DNA was applied to Human Genome U133A Affymetrix chips and analyzed using
Affymetrix Microarray MAS 5.0 software to identify increased and decreased gene expression at
p < 0.005.
Quantitative PCR
Cells were treated with TRIzol reagent from Invitrogen (Carlsbad, CA) and ribonucleic acid
(RNA) was isolated according to the manufacturer’s instructions. To generate single-stranded
complementary DNA (cDNA), the Applied Biosystems GeneAmp RNA polymerase chain
reaction (PCR) Core Kit (Foster City, CA) was used with 3 ug/mL total RNA using a Biometra
UNO-thermo-block and Perkin-Elmer-GeneAmp PCR system 9600. qPCR was carried out with
SYBR Green Universal Master Mix (Applied Biosystems), cDNA, and primers. Primers used were
AMY1A (GeneCopoeia: Hs-QRP-21480), AMY2A (GeneCopoeia: Hs-QRP-21450), AMY2B
(GeneCopoeia: Hs-QRP-21451), and GAPDH (GeneCopoeia: Hs-QRP-20169) as control.
Sequences for the amylase primers remain the proprietary property of GeneCopoeia. Amplification
was performed with 40 cycles of denaturation (95°C, 10 sec), annealing (60°C, 20 sec), and
extension (72°C, 15 sec) using a Bio-RAD Chromo4 Real Time PCR Detector using Opticon
Monitor 3 program. Levels of amylase was normalized to the GAPDH message values and the
fold difference was determined by dividing the threshold cycle (Ct) value by the reference sample.
106
Western blot
Cells were washed in PBS, trypsinized, pelleted, rewashed with PBS and counted. Cells were
lysed in CHAPS buffer for 30 minutes on ice before being centrifuged at 115,000 x g, at 4°C for
1h. Twenty ug of protein were separated by a 10% sodium dodecyl sulfate polyacrylamide gel
electrophoresis (SDS-PAGE). Proteins were transferred to Polyvinylidene fluoride (PVDF)
membranes, washed in Tween 20-Tris buffered Saline (TBST), and blocked in 5% milk in TBST
or PBST. Blots were incubated in their respective primary antibodies overnight, followed by
incubation with a horseradish peroxidase (HRP)-conjugated secondary antibody (Fisher,
Pittsburgh, PA), and developed via FEMTO Cat. 34095 Life Technologies (Grand Island, NY).
Antibodies used were: AMY1A (1:1000) Cat.H00000276-M04 Abnova (Taipei City, Taiwan);
AMY2A (1:500) 15845-1-AP Proteintech (Chicago, IL); AMY2B (1:2000) Cat. LS-C173958
LSBio (Seattle, WA); Beta Actin Loading Control Antibody (BA3R) (1:10,000) Cat. # MA5-
15739 Thermo Scientific (Rockford, IL). Densitometry was performed using ImageJ software to
normalize amylase band strength to their respective actin band.
Amylase ELISA
To measure amylase protein levels, 100ul of cytoplasmic lysate and CCM of equivalent cell
number (104/well) were assayed in triplicate using the quantitative double sandwich enzyme-
linked immunosorbant assay (Amy-p ELISA Kit; catalog number: MBS264855; MyBioSource,
San Diego, CA) according to the manufacturer's instructions. Absorbance was read on a Microplate
Reader BioTek ELx800 (Winooski, Vermont) using Gen5 Data Analysis Software (Biotek,
Winooski, Vermont). Resultant values were derived from a standard curve and expressed as the
mean amylase concentration of triplicate samples.
107
Invasion assay
Spirulina treated cells were incubated on type I collagen coated transwells (CytoSelect 96-
Well Cell Invasion Assay, catalog number: CBA-112-COL (San Diego, CA)) for 24 hours.
Following incubation, transwell filters were stained with a 0.1% crystal violet solution and
photographed using an Olympus DP20 digital camera (Burnaby, BC, Canada) under a Leica
DMIRE2 microscope. The number of cells that had migrated through the collagen cell and across
the transwell membrane was then counted.
MTS assay
Cell growth and cytotoxicity were determined by the MTS assay (Promega, Madison, WI)
according to the manufacturer's directions. Cells (1 × 103) were seeded in 96-well plates and grown
in Medium 199/MCDB 105 (Sigma, St. Louis, MO) containing 0.1% FBS. Cells were treated with
varying concentrations of spirulina (0ug/ml, 50ug/ml, 100ug/ml, 150ug/ml). Absorbance was read
nm using a multi-well plate reader (BioTek, Winooski, VT, USA) every 24 hours for 72 hours.
The samples were assayed in two separate experiments, each in triplicate and the results were
expressed as the mean ± standard error.
Statistical Analyses
For real time PCR, error bars illustrate RQmin and RQmax, which were calculated as: RQave
divided by (standard deviation^ student’s t value at the 95% confidence interval, for 5 degrees
freedom) and RQave times (standard deviation^ student’s t value at the 95% confidence interval,
for 5 degrees of freedom), respectively. This range represents the 95% confidence level. For
amylase activity assays, ELISA, MTS, and invasion student’s t test was performed to assess
statistical difference between means of triplicates ± standard error.
108
Results
Amylase is a downstream target of spirulina.
In order to screen for downstream targets of spirulina, an Affymetrix microarray was
performed on spirulina treated HN5α cells. The results indicated that spirulina downregulated all
amylase isozyme expression at least 2.5 fold. Spirulina also suppressed expression of multiple
transcription factors predicted to regulate amylase gene expression (Chapter 2) including C/EBP
alpha, C/EBP delta, Zic1 and POU1F1 (Table 4.1).
Table 4.1: Spirulina transcriptionally targets amylase
Gene Fold change
AMY1A, AMY1B, AMY1C, AMY2A, AMY2B, ACTG1P4, AMYP1 -2.62
AMY1A, AMY1B, AMY1C, AMY2A, AMY2B -2.5
AMY1A, AMY1B, AMY1C, AMY2A, AMY2B, ACTG1P4, AMYP1 -2.5
CCAAT/enhancer binding protein (C/EBP), alpha -1.18
CCAAT/enhancer binding protein (C/EBP), delta -1.15
Zic1 -1.05
POU1F1 -1.19
Spirulina downregulates amylase mRNA expression in OC cell lines.
To confirm that spirulina inhibits amylase expression, a panel of cancer cell lines was
treated ± 100ug/ml spirulina and amylase expression was measured using qPCR. While spirulina
downregulated amylase expression in normal human dermal fibroblasts between 43% (AMY2B)
to 95% (AMY2A) among the amylase isozymes, the pattern of spirulina-mediated inhibition of
amylase expression varied among cancer cell types (Figure 4.1).
Spirulina significantly downregulated the expression of all amylase isozymes by 46% to
99% in colon cancer cells (WIDR and SW626) and by 57% to 77% in breast cancer cells MDA231,
but it actually increased amylase isozyme expression by 1302% for AMY1, 79418% for AMY2A,
109
and 588% for AMY2B in MCF7 breast cancer cells. Similarly, spirulina increased amylase
isozyme expression by 511% to 1875% in glioblastoma cells U373MG. Spirulina also
downregulated amylase isozyme expression by approximately 80% in cervical cancer cells Hela.
Spirulina downregulated AMY1 (43%) and AMY2A (82%) expression in pancreatic cancer cells
PANC1, but increased AMY2B (200%) expression. In addition, spirulina downregulated at least
two amylase isozymes in lung cancer cells (H2009) by 54%.
In contrast, spirulina consistently downregulated amylase expression in amylase positive OC
cells with spirulina-mediated inhibition of AMY1 and AMY2B, ranging from 6% to 99% (Figure
4.2). However, spirulina did not significantly decrease small endogenous levels of amylase
expression in IOSE-121 (Figure 4.2).
Spirulina downregulates amylase protein expression in OC cell lines.
Since RNA levels do not always accurately reflect target protein levels, to determine if
spirulina-mediated inhibition of amylase could be detected at the protein level, cells were treated
with 100ug/ml spirulina for 24 hours and protein amylase isozyme expression was determined
using western blot. While there was no significant change in amylase protein levels in IOSE-121
cells after spirulina treatment (Figure 4.3), spirulina reduced AMY1 protein levels in OVCAR5
and OV-90 cells by 23% and 26%, respectively; spirulina reduced AMY2A protein levels in
OVCAR5 and OV-90 cells by 51% and77%, respectively and spirulina reduced AMY2B protein
levels in OVCAR5 and OV-90 cells by 25% and 43%, respectively.
110
Fig
ure
4.1
. S
pir
uli
na
dow
nre
gula
tes
most
am
yla
se i
sozy
mes
in c
ance
r ce
lls.
Cel
ls t
reat
ed ±
100 u
g/m
l sp
iruli
na
wer
e su
bje
cted
to
qP
CR
to d
eter
min
e am
yla
se i
sozy
me
exp
ress
ion.
Dat
a ar
e ex
pre
ssed
as
the
rati
o o
f am
yla
se i
sozy
me
signal
to G
AP
DH
.
111
Figure 4.2. Spirulina transcriptionally downregulates amylase in OC cell lines. Cells treated
± 100 ug/ml spirulina treatment were subjected to qPCR to determine amylase isozyme
expression. Data are expressed as the ratio of amylase isozyme signal to GAPDH.
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0.001
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
IOSE-121 IOSE-121 +100ug/mlspirulina
OV-90 OV-90 +100ug/mlspirulina
TOV21G TOV21G +100ug/mlspirulina
IGROV IGROV +100ug/mlspirulina
OVCAR5 OVCAR5 +SPIR
Rat
io o
f A
MY/
GA
PD
H m
RN
A
Figure 4.3. Spirulina reduces amylase protein levels in OC cell lines. Cells treated ± 100
ug/ml spirulina were subjected to western blot analysis to determine amylase isozyme protein
expression (left panel). Densitometric analyses representing ratio of amylase isozyme band
signal to respective actin loading control band signals are shown in the right panel.
IOSE
-1
21
IOSE
-12
1 +
spir
ulin
a O
VC
AR
5
OV
CA
R5
+ s
pir
ulin
a
OV
-90
OV
-90
+ s
pir
ulin
a
AMY1A
AMY2A
AMY2B
ACTIN
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
IOSE-121 IOSE-121+ 100ug/ml
spirulina
OVCAR5 OVCAR5 +100ug/mlspirulina
OV-90 OV-90 +100ug/mlspirulina
Rat
io o
f am
ylas
e/ac
tin
pro
tein
AMY1 AMY2A AMY2B
112
Spirulina reduces amylase secretion by OC cell lines.
To determine if spirulina inhibited amylase secretion, cell lysates and CCM collected from
IOSE-121, IOSE-144, OVCAR5 and SKOV3 cells treated with 100ug/ml spirulina for 24 h and
then subjected to an amylase ELISA (Figure 4.4). In agreement with figure 4.3, I found 12%
reduction in cellular amylase in IOSE-121 and -144, respectively. In addition, levels of secreted
amylase in IOSE-121 and -144 were reduced by 28% and 18% following spirulina treatment,
respectively. Also in agreement with figure 4.3, I found 72% and 16% reduction in cellular amylase
in OVCAR5 and SKOV3 cells, respectively. More notably, levels of secreted amylase in OVCAR5
and SKOV3 cells were reduced by 90% and 6% following spirulina treatment, respectively.
Figure 4.4. Spirulina reduces amylase secretion in OC cell lines. Cell lysates and CCM from
cells treated ± spirulina were subjected to amylase ELISA to determine relative amounts of
cellular and secreted amylase. Data are presented as the mean of triplicates ± SE (*P<0.05).
*
0
10
20
30
40
50
60
IOSE-122 IOSE-122 +100ug/mlspirulina
IOSE-144 IOSE-144 +100 ug/mlspirulina
OVCAR5 OVCAR5 +100 ug/mlspirulina
SKOV3 SKOV3 +100 ug/mlspirulina
ng/
ml a
myl
ase
Cell lysate CCM
113
Spirulina decreases the invasive capacity of OC cells.
Using Boyden chamber assays, I found that spirulina did not significantly reduce invasive
capacity in IOSE-121 cells (Figure 4.5). However, spirulina significantly reduced invasive
capacity of OVCAR5 by 51% compared to untreated cells.
Spirulina decreased migration of OVCAR5 cells.
To confirm the inhibitory effect of spirulina for OC cell motility, OVCAR5 cells were
visualized in a scratch assay for 0-24 hours ± 100 ug/ml spirulina. Untreated cells migrated to fill
the scratch gap within 24 h (Figure 4.6) However, spirulina-treated cells did not migrate as quickly
and had only filled in 64% of their scratch gap at 24h.
Figure 4.5. Spirulina decreased invasive capacity in OC cells. IOSE-121 and OVCAR5 cells
were treated ± 100 ug/ml spirulina and assayed in Boyden chambers. Data are presented as
the mean of triplicates ± S.E. (*P<0.05).
0
20
40
60
80
100
120
untreated 100ug/mlspirulina
untreated 100ug/mlspirulina
IOSE-121 OVCAR5
Nu
mb
er o
f in
vasi
ve c
ells
Spirulina invasion assay
*
114
Day 0 Day 1
OVCAR5 control
OVCAR5 + 100 ug/ml spirulina
Figure 4.6. Spirulina reduced migration in OVCAR5 cells. OVCAR5
cells were visualized in a scratch assay for 0-24h ± 100 ug/ml spirulina.
Experiment was performed in triplicate. Representative scratch assay is
illustrated in the upper panel while lower panel represents percent wound
closure.
0
20
40
60
80
100
120
DAY 0 DAY 1
Per
cen
t cl
osu
re
Time
Spirulina decreased migration in OVCAR5 cells
OVCAR5 control OVCAR5 + 100 ug/ml spirulina
115
Spirulina does not alter OC proliferation.
To determine whether spirulina is potentially cytotoxic for normal or OC cells, IOSE and
OC cell survival and growth were measured by MTS assay for 0-72 hours when treated with a
range of concentrations of spirulina (Figure 4.7). Spirulina did not significantly alter or affect
proliferation of either OSE or OC cells.
Figure 4.7. Spirulina did not alter IOSE or OC cell survival and proliferation. IOSE-121, IOSE-
144, OVCAR5 and OV-90 cells were treated with varying concentrations—0ug/ml, 50ug/ml,
100ug/ml, 150ug/ml— of spirulina and proliferation was measured every 24 hours for 72 hours.
Data are presented as the mean of two biological experiments, each performed in triplicates ±
SE.
0
0.2
0.4
0.6
0 24 48 72
Ab
sorb
ance
Time (hrs)
IOSE-121
0ug/mL50ug/mL100ug/mL150ug/mL
0
0.1
0.2
0.3
0.4
0 24 48 72
Ab
sorb
ance
Time (hrs)
IOSE-144
0ug/mL
50ug/mL
100ug/mL
150ug/mL
0
0.1
0.2
0.3
0 24 48 72
Ab
sorb
ance
Time (hrs)
OV-90
0ug/mL
50ug/mL
100ug/mL
150ug/mL
0
0.1
0.2
0.3
0.4
0 24 48 72
Ab
sorb
ance
Time (hrs)
OVCAR5
0ug/mL
50ug/mL
100ug/mL
150ug/mL
116
Phycocyanin abrogates amylase RNA expression.
IOSE-121 and OVCAR5 cells were treated with spirulina or phycocyanin in order to
determine if transcriptional downregulation of amylase by spirulina is due to one of its major active
ingredients, phycocyanin. In agreement with my earlier data (Figure 4.2), spirulina failed to
suppress amylase isozyme RNA expression in IOSE-121 (Figure 4.8), but phycocyanin increased
AMY1 and AMY2B RNA expression levels in a dose related manner. In contrast, but also in
agreement with my earlier data (Figure 4.2), spirulina completely inhibited AMY1 and AMY2B
RNA levels in OVCAR cells. Likewise, phycocyanin completely abrogated AMY1 and AMY2B
RNA levels in OC cells.
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0.001
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
AM
Y1
AM
Y2A
AM
Y2B
Control 100ug/mlSpirulina
1ug/mlphycocyanin
100ug/mlphycocyanin
Control 100ug/mlSpirulina
1ug/mlphycocyanin
100ug/mlphycocyanin
IOSE-121 OVCAR5
Rat
io o
f am
ylas
e/G
AP
DH
mR
NA
Figure 4.8. Phycocyanin abrogates amylase expression in OC cells. IOSE-121 and OVCAR5
cells were treated with 100 ug/ml spirulina, 1ug/ml phycocyanin or 100 ug/ml phycocyanin and
subjected to qPCR for amylase isozyme RNA expression. Data are expressed as the ratio of
amylase isozyme signal to GAPDH.
117
Spirulina-induced inhibition of OC invasion is driven, in part, by phycocyanin.
To determine whether spirulina-mediated inhibition of OC invasive capacity was driven by
phycocyanin, using Boyden chamber assays, I found that neither spirulina nor phycocyanin
significantly reduced invasive capacity in IOSE-121 cells (Figure 4.9). In agreement with my
earlier data (Figure 4.5), spirulina reduced the invasive capacity of OVCAR5 cells by
approximately 51%. However, phycocyanin was only able to reduce the invasive capacity of
OVCAR5 cells by approximately 32%, suggesting that spirulina-induced inhibition of OC
invasion is driven only in part by phycocyanin.
0
20
40
60
80
100
120
untreated 100ug/mlspirulina
1ug/ml c-phycocyanin
untreated 100ug/mlspirulina
1ug/ml c-phycocyanin
IOSE-121 OVCAR5
Nu
mb
er o
f in
vasi
ve c
ells
Number of invasive cells
Figure 4.9. Spirulina driven inhibition of OC invasion is partly mediated by phycocyanin.
IOSE-121 and OVCAR5 cells were treated with 100 ug/ml spirulina or 1ug/ml phycocyanin
and assayed in collagen-coated Boyden chambers. Data are presented as the mean of
triplicates (*P<0.05).
*
*
118
Discussion
Dietary supplements are widely recognized for their medicinal benefits. Many are or
contain free radical scavengers that promote improved health through anti-inflammatory activity
and clinically manifest as reduced incidence of disease, reduced damage associated with disease
and/or accelerated healing from disease. For example, curcumin, an active component of the spice
turmeric is widely used traditional Chinese herbal medicine. It has anti-cancer properties related
to suppression of the NF-κB signaling pathway which inhibits cellular proliferation and metastasis
[372]. Further, treatment of endometrial carcinoma cells, HEC-1B, with curcumin results in
decreased migration and invasion by reducing the expression and activity of MMP-2/9 via
suppression of the ERK signaling pathway [372,373].
Likewise, the clinical benefits of spirulina have also been investigated with regards to its
anti-oxidant, anti-inflammatory and anti-cancer properties. Several preclinical and clinical studies
have investigated the hypolipidemic effects of spirulina, that is, its ability to reduce cholesterol by
decreasing low-density lipoproteins (LDL), very low-density lipoproteins (HLDL), and
triglycerides (TG) and increase protective high-density lipoproteins (HDL). The cumulative data
indicates that spirulina has hypolipidemic activity in healthy young [374] and elderly volunteers,
patients with type 2 diabetes [375], patients with nephrotic syndrome [376] and patients with
ischaemic heart disease [377]. However, the exact molecular mechanism by which spirulina
promotes its hypolipidemic effects has not yet been elucidated [364].
The antioxidant and anti-inflammatory effects of spirulina were examined in in-vitro, pre-
clinical and some clinical studies. Spirulina can decrease the inflammatory reaction induced by
toxic, oxidative and physical damaging agents [378,379]. Spirulina’s antioxidant and anti-
inflammatory effects have been attributed to phycocyanin and β-carotene. Phycocyanin is an
119
antioxidant that can scavenge free radicals and lower lipid peroxidation [371]. Phycocyanin has
also been reported to inhibit TNF alpha and nitrile production [380]. On the other hand, the anti-
oxidant properties of β-carotene are related to its ability to decrease production of nitric oxide
(NO) and PGE(2), inducible NO synthase (iNOS), cyclooxygenase-2, TNF-alpha, and IL-1beta
[381].
Lastly, the anti-cancer properties of spirulina have also been studied. Spirulina and
compounds isolated from spirulina, including phycocyanobilin and chlorophyllin, tetrapyrrolic,
significantly decrease proliferation of human pancreatic cancer cells in xenotransplanted nude
mice [382]. Spirulina has been reported to down-regulate inflammation and carcinogenesis
induced by UVB irradiation in the skin [383]. Additionally, spirulina exhibited anti-proliferative
activity with low cytotoxicity in five cancer cell lines – HepG2 (liver cancer cell line), MCF-7
(breast cancer cell line), SGC-7901 (gastric cancer cell line), A549 (non-small cell lung cancer),
and HT-29 (colon colorectal cancer cell line) [384]. Finally, colon colorectal cells (Caco-2) treated
with spirulina display decreased proliferation, migration and increased apoptosis that may be
related to increased intracellular reactive oxygen species (ROS) and nitric oxide (NO) levels [385].
In contrast, there have only been two reports investigating the role of spirulina in OC and
both studies were limited to the effect of phycocyanin in a single OC cell line. By targeting
mitochondrial proteins, phycocyanin was reported to inhibit cellular proliferation and induce
apoptosis by increased ROS production and activation of caspase-3, -8, and -9 in SKOV3 cells.
[386]. Ying et al. (2016) suggested that the anti-proliferative action of phycocyanin in SKOV3
cells could be mediated through 18 cellular pathways [387].
In the present study, I showed that amylase is a downstream target of spirulina. Spirulina
consistently downregulated amylase isozyme expression at the message and protein levels. As a
120
result, spirulina, and to a lesser degree, phycocyanin appear to abrogate OC invasion by inhibiting
amylase-mediated OC cell migration and invasion. Importantly, spirulina did not appear to
adversely affect normal OSE cells. Given that the high mortality associated with OC is due, to a
large extent, by its propensity to metastasize early, the subset of patients whose cancers
overexpress amylase may benefit from adjuvant spirulina therapy to minimize burden of disease.
121
Chapter 5
Concluding Remarks
Although its etiology is poorly understood, OC accounts for 4% of all cancer cases and
4.2% of all cancer deaths worldwide. OC is the most lethal gynecological cancer because early
symptoms of the disease are generally lacking. As a result, more than 70% of OC patients are
diagnosed in later stages when the disease has already metastasized. Women diagnosed in late
disease stage have a 5-year survival rate of less than 20% compared with approximately 90%
survival for women diagnosed with early stage disease. The current gold standard for OC
detection, serum CA-125, is only elevated in 50% of stage I OC patients so that CA-125 is more
useful as a prognostic rather than a diagnostic marker [388]. Therefore, there remains an urgent
need to better understand the cellular and molecular regulators that drive OC progression in order
to reduce metastatic spread as well as identify novel diagnostic and therapeutic targets.
A number of potential OC biomarkers have been reported to date. However, their efficacy
for disease detection or functional contribution to OC progression is limited. Therefore, I initiated
my studies with a computational analysis of the 27 most commonly reported literature-derived
ovarian cancer (LDOC) protein biomarkers. I found that LDOC protein biomarkers share many
biochemical features including a preponderance for a stable protein structure (ordered, hydrophilic,
aggregation-prone, glycosylated and sumoylated), the ability to be secreted (export signal peptide
122
sequence, hydrophilic) and functionality related to ECM modification, immune response and/or
energy production. Subsequently, I analyzed the human proteome to identify additional proteins
that also share these biochemical features. Of the 70,616 proteins in the human proteome, 683
proteins were found to have similar biochemical features to the 27 LDOC proteins. With further
computational analyses I identified a subset of 21 potential additional protein regulators of ovarian
cancer (APROC) sharing similar functional and biochemical protein profiles and interactivity with
LDOCs in the humane proteome.
Three of the APROCs identified were amylase (AMY) AMY1A, AMY2A, and AMY2B,
which compromise the members of the human amylase gene family. Amylase cleaves alpha 1, 4-
glycosidic bonds in polysaccharides to initiate carbohydrate metabolism, thereby contributing to
energy production. Amylase is reportedly overexpressed in and secreted by ovarian tumors [262].
Hyperamylasemia has been studied as both a diagnostic and prognostic indicator even though its
functional contribution to OC progression remains unknown [1]. Since amylase can degrade
bacterial biofilms in wounds [228], it is possible that, by cleaving polysaccharide moieties in the
tumor microenvironment, amylase also plays a role in ECM remodeling. Consequently, I sought
to determine whether amylase could promote OC cell migration and invasion.
I initiated my studies on the role of amylase to drive OC invasion with additional
computational studies focused on characterizing the different amylase isozymes in order to predict
which amylase isozyme(s) is most likely overexpressed in and contributory to OC invasion. I found
that there were significant differences among the isozymes. Specifically, AMY1 and AMY2B have
unique regions of disorder, unique regions of aggregation and unique phosphorylation sites
indicating that AMY1 and AMY2B would be more likely to interact with other proteins, to
123
aggregate and to be easily secreted. Using serum samples from patients with OC, I was able to
validate AMY1 and AMY2B overexpression by western immunoblotting.
I then developed an in vitro model system to study the molecular contribution of amylase
to OC using IOSE and OC cell lines. I showed that OC cells generally overexpress and secrete
metabolically active amylase isozymes AMY1 and AMY2B. Abrogating amylase activity using
siRNA silencing technology decreased the capacity of OC cells to invade collagen coated Boyden
chambers and increased sGAG production. Since a survey of OC cell lines indicated that cancer
cells have a bulkier glycocalyx compared to IOSE cells and immunogold labeling studies indicated
the presence of amylase within the immediate OC microenvironment, my data suggest that, by
cleaving alpha 1, 4-glycosidic bonds in glycoconjugates (glycosaminoglycans (GAGs),
glycoproteins, glycolipids, and proteoglycans) present within ECM, amylase may remodel the
ECM to promote an invasive cancer phenotype.
Further studies are needed to validate the hypothesis that amylase may promote OC
invasion by altering the glycocalyx/ECM architecture in OC. Assuming that amylase maintains its
traditional enzymatic activity in the OC ECM, GAGs within the glycocalyx containing α-1, 4-
glycosidic bonds should be susceptible to hydrolysis by amylase enzymatic activity. By cleaving
α-1, 4-glycosidic bonds in negatively charged repellant GAGs [356], amylase may lead to
proteoglycan condensation. Consequently, this proteoglycan condensation could result in integrin
clustering and dimerization, leading to an altered glycocalyx characteristic of malignant cells that
is associated with an invasive phenotype (Figure 5.1). Weaver et al. described metastatic tumors
which overexpress bulky glycan structures and glycoproteins that alter integrin organization by
funneling active integrins together. Glycocalyx –mediated integrin clustering promote phenotypes
associated with cancer [389,390].
124
Amylase could, then, be a target for therapeutic intervention in OC patients with
hyperamylasemia. However, commercially available amylase inhibitors typically inhibit amylase
activity at the protein level and produce a variety of adverse gastrointestinal side effects [363]. I
established Spirulina, a dietary supplement, as a novel transcriptional inhibitor of amylase.
Spirulina inhibited amylase expression in OC cell lines at both the message and protein levels.
Spirulina reduced OC cell invasion and migration in vitro, putatively by decreasing amylase
expression. Phycocyanin, one of the active ingredients of spirulina, was investigated in order to
determine if it was responsible for the downregulation of amylase expression and reduced invasion
induced by spirulina. I found that phycocyanin reduced amylase expression in and invasive
Figure 5.1. Schematic of the possible influence of amylase in altering the glycocalyx of OC
cell and consequently lead to a more malignant phenotype. Amylase may cleave α-1, 4-
glycosidic bonds in OC cell glycocalyx which leads to proteoglycan condensation and integrin
clustering. The resulting altered glycocalyx promotes a malignant phenotype.
125
behavior of OC cells, but the degree to which phycocyanin reduced amylase-mediated activities
was less than that of spirulina. Therefore, multiple components of spirulina are likely responsible
for regulating amylase expression.
Clearly, further studies are warranted to determine the clinical impact of amylase in OC,
and, potentially other cancer types. Since lung cancer is also often clinically associated with
hyperamylasemia, it would be interesting to determine whether amylase similarly mediates cellular
invasion in lung cancer cells. Given the lack of sensitive and specific biomarkers for OC, it might
also be clinically impactful to examine the potential for serum amylase to serve as a biomarker of
disease based on amylase isozyme subtype. Furthermore, spirulina may be an attractive and
effective therapy/therapeutic adjuvant to reduce the migratory and invasive capabilities of OC cells
leading to decreased disease burden. Going forward, it would be important to identify and isolate
the specific components of Spirulina that transcriptionally inhibit amylase as well as examine other
dietary supplements for their ability to inhibit amylase alone or in concert with Spirulina. Lastly,
I have shown the power of computational analyses to provide insight about the different molecular
mechanisms employed by disease regulators with shared protein profiles. I would hope that future
endeavors explore the biochemical features, metabolic pathways and protein-protein networks
among the remaining APROC candidates to provide additional insights about the appearance of
diverse histologic subtypes, the propensity for metastatic spread and the emergence of drug
resistance in OC. This, in turn, could reduce the mortality of a disease that kills thousands of
women annually.
126
Chapter 6
References
1. Srivastava R, Fraser C, Gentleman D, Jamieson LA, Murphy MJ. Hyperamylasaemia: not
the usual suspects. Bmj. 2005; 331(7521):890–891. doi:10.1136/bmj.331.7521.890.
2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA. Cancer J. Clin. 2015;
65(1):5–29. doi:10.3322/caac.21254.
3. Brucks JA. Ovarian cancer. The most lethal gynecologic malignancy. Nurs. Clin. North
Am. 1992; 27(4):835–845.
4. Brown DL, Andreotti RF, Lee SI, Dejesus Allison SO, Bennett GL, Dubinsky T, et al.
ACR appropriateness criteria(c) ovarian cancer screening. Ultrasound Q. 2010;
26(4):219–223. doi:10.1097/RUQ.0b013e3181fdd604.
5. Bast RCJ, Brewer M, Zou C, Hernandez MA, Daley M, Ozols R, et al. Prevention and
early detection of ovarian cancer: mission impossible? Recent results cancer Res.
Fortschritte der Krebsforsch. Prog. dans les Rech. sur le cancer. 2007; 174:91–100.
6. Stirling D, Evans DGR, Pichert G, Shenton A, Kirk EN, Rimmer S, et al. Screening for
familial ovarian cancer: failure of current protocols to detect ovarian cancer at an early
stage according to the international Federation of gynecology and obstetrics system. J.
Clin. Oncol. 2005; 23(24):5588–5596. doi:10.1200/JCO.2005.05.097.
7. Gaarenstroom KN, van der Hiel B, Tollenaar RAEM, Vink GR, Jansen FW, van Asperen
127
CJ, et al. Efficacy of screening women at high risk of hereditary ovarian cancer: results of
an 11-year cohort study. Int. J. Gynecol. Cancer. 2006; 16 Suppl 1:54–59.
doi:10.1111/j.1525-1438.2006.00480.x.
8. Bell DA. Origins and molecular pathology of ovarian cancer. Mod. Pathol. an Off. J.
United States Can. Acad. Pathol. Inc. 2005; 18 Suppl 2:S19-32.
doi:10.1038/modpathol.3800306.
9. Chene G, Penault-Llorca F, Le Bouedec G, Mishellany F, Dauplat M-M, Jaffeux P, et al.
Ovarian epithelial dysplasia and prophylactic oophorectomy for genetic risk. Int. J.
Gynecol. Cancer. 2009; 19(1):65–72. doi:10.1111/IGC.0b013e3181990127.
10. Dietl J. Revisiting the pathogenesis of ovarian cancer: the central role of the fallopian
tube. Arch. Gynecol. Obstet. 2014; 289(2):241–246. doi:10.1007/s00404-013-3041-3.
11. Auersperg N, Wong AS, Choi KC, Kang SK, Leung PC. Ovarian surface epithelium:
biology, endocrinology, and pathology. Endocr. Rev. 2001; 22(2):255–288.
doi:10.1210/edrv.22.2.0422.
12. Dietl J, Marzusch K. Ovarian surface epithelium and human ovarian cancer. Gynecol.
Obstet. Invest. 1993; 35(3):129–135.
13. Robbins SL, Kumar V. Robbins and Cotran Pathologic Basis of Disease. 8th ed.
Philadelphia, PA: Saunders/Elsevier; 2010.
14. Blaustein A. Peritoneal mesothelium and ovarian surface cells--shared characteristics. Int.
J. Gynecol. Pathol. 1984; 3(4):361–375.
15. Dubeau L. The cell of origin of ovarian epithelial tumours. Lancet. Oncol. 2008;
9(12):1191–1197. doi:10.1016/S1470-2045(08)70308-5.
16. Marquez RT, Baggerly KA, Patterson AP, Liu J, Broaddus R, Frumovitz M, et al. Patterns
128
of gene expression in different histotypes of epithelial ovarian cancer correlate with those
in normal fallopian tube, endometrium, and colon. Clin. Cancer Res. 2005; 11(17):6116–
6126. doi:10.1158/1078-0432.CCR-04-2509.
17. Kurman RJ, Ellenson LH, Ronnett BM, editors. Blaustein’s Pathology of the Female
Genital Tract. 6th ed. Springer US; 2011.
18. Shih I-M, Kurman RJ. Ovarian tumorigenesis: a proposed model based on morphological
and molecular genetic analysis. Am. J. Pathol. 2004; 164(5):1511–1518.
19. Kurman RJ, Shih I-M. The origin and pathogenesis of epithelial ovarian cancer: a
proposed unifying theory. Am. J. Surg. Pathol. 2010; 34(3):433–443.
doi:10.1097/PAS.0b013e3181cf3d79.
20. Auersperg N. The origin of ovarian cancers--hypotheses and controversies. Front. Biosci.
(Schol. Ed). 2013; 5:709–719.
21. Kobel M, Kalloger SE, Boyd N, McKinney S, Mehl E, Palmer C, et al. Ovarian carcinoma
subtypes are different diseases: implications for biomarker studies. PLoS Med. 2008;
5(12):e232. doi:10.1371/journal.pmed.0050232.
22. van Nagell JRJ, DePriest PD, Ueland FR, DeSimone CP, Cooper AL, McDonald JM, et
al. Ovarian cancer screening with annual transvaginal sonography: findings of 25,000
women screened. Cancer. 2007; 109(9):1887–1896. doi:10.1002/cncr.22594.
23. Siegel R, Ward E, Brawley O, Jemal A. Cancer statistics, 2011: the impact of eliminating
socioeconomic and racial disparities on premature cancer deaths. CA. Cancer J. Clin.
2011; 61(4):212–236. doi:10.3322/caac.20121.
24. Gloss BS, Samimi G. Epigenetic biomarkers in epithelial ovarian cancer. Cancer Lett.
2014; 342(2):257–263. doi:10.1016/j.canlet.2011.12.036.
129
25. Mills GB, Bast RCJ, Srivastava S. Future for ovarian cancer screening: novel markers
from emerging technologies of transcriptional profiling and proteomics. J. Natl. Cancer
Inst. 2001; 93(19):1437–1439.
26. Nolen BM, Lokshin AE. Protein biomarkers of ovarian cancer: the forest and the trees.
Future Oncol. [Internet]. 2012; 8(1):55–71. doi:10.2217/fon.11.135.
27. Scambia G, Testa U, Panici PB, Martucci R, Foti E, Petrini M, et al. Interleukin-6 serum
levels in patients with gynecological tumors. Int. J. cancer. 1994; 57(3):318–323.
28. Plante M, Rubin SC, Wong GY, Federici MG, Finstad CL, Gastl GA. Interleukin-6 level
in serum and ascites as a prognostic factor in patients with epithelial ovarian cancer.
Cancer. 1994; 73(7):1882–1888.
29. van der Zee AG, de Cuyper EM, Limburg PC, de Bruijn HW, Hollema H, Bijzet J, et al.
Higher levels of interleukin-6 in cystic fluids from patients with malignant versus benign
ovarian tumors correlate with decreased hemoglobin levels and increased platelet counts.
Cancer. 1995; 75(4):1004–1009.
30. Nowak M, Glowacka E, Szpakowski M, Szyllo K, Malinowski A, Kulig A, et al.
Proinflammatory and immunosuppressive serum, ascites and cyst fluid cytokines in
patients with early and advanced ovarian cancer and benign ovarian tumors. Neuro
Endocrinol. Lett. 2010; 31(3):375–383.
31. Rabinovich A, Medina L, Piura B, Segal S, Huleihel M. Regulation of ovarian carcinoma
SKOV-3 cell proliferation and secretion of MMPs by autocrine IL-6. Anticancer Res.
2007; 27(1A):267–272.
32. Macciò A, Madeddu C. The role of interleukin-6 in the evolution of ovarian cancer:
clinical and prognostic implications---a review. J. Mol. Med. [Internet]. 2013;
130
91(12):1355–1368. doi:10.1007/s00109-013-1080-7.
33. Wang Y, Niu XL, Qu Y, Wu J, Zhu YQ, Sun WJ, et al. Autocrine production of
interleukin-6 confers cisplatin and paclitaxel resistance in ovarian cancer cells. Cancer
Lett. 2010; 295(1):110–123. doi:10.1016/j.canlet.2010.02.019.
34. Wang Y, Li L, Guo X, Jin X, Sun W, Zhang X, et al. Interleukin-6 signaling regulates
anchorage-independent growth, proliferation, adhesion and invasion in human ovarian
cancer cells. Cytokine. 2012; 59(2):228–236. doi:10.1016/j.cyto.2012.04.020.
35. Xu L, Fidler IJ. Interleukin 8: an autocrine growth factor for human ovarian cancer.
Oncol. Res. 2000; 12(2):97–106.
36. Wang Y, Xu RC, Zhang XL, Niu XL, Qu Y, Li LZ, et al. Interleukin-8 secretion by
ovarian cancer cells increases anchorage-independent growth, proliferation, angiogenic
potential, adhesion and invasion. Cytokine [Internet]. 2012 [cited 2016 Mar 23];
59(1):145–55. doi:10.1016/j.cyto.2012.04.013.
37. Dobrzycka B, Mackowiak-Matejczyk B, Terlikowska KM, Kulesza-Bronczyk B, Kinalski
M, Terlikowski SJ. Serum levels of IL-6, IL-8 and CRP as prognostic factors in epithelial
ovarian cancer. Eur. Cytokine Netw. 2013; 24(3):106–113. doi:10.1684/ecn.2013.0340.
38. Zhao S, Wu D, Wu P, Wang Z, Huang J. Serum IL-10 Predicts Worse Outcome in Cancer
Patients: A Meta-Analysis. PLoS One [Internet]. 2015; 10(10):e0139598. Available from:
http://dx.doi.org/10.1371%252Fjournal.pone.0139598.
39. Mustea A, Konsgen D, Braicu EI, Pirvulescu C, Sun P, Sofroni D, et al. Expression of IL-
10 in patients with ovarian carcinoma. Anticancer Res. 2006; 26(2C):1715–1718.
40. Reuwer AQ, Nowak-Sliwinska P, Mans LA, van der Loos CM, von der Thüsen JH,
Twickler MTB, et al. Functional consequences of prolactin signalling in endothelial cells:
131
a potential link with angiogenesis in pathophysiology? J. Cell. Mol. Med. [Internet]. 2012;
16(9):2035–2048. doi:10.1111/j.1582-4934.2011.01499.x.
41. da Silva PL, do Amaral VC, Gabrielli V, Montt Guevara MM, Mannella P, Baracat EC, et
al. Prolactin Promotes Breast Cancer Cell Migration through Actin Cytoskeleton
Remodeling. Front. Endocrinol. (Lausanne). 2015; 6:186. doi:10.3389/fendo.2015.00186.
42. Levina V V, Nolen B, Su Y, Godwin AK, Fishman D, Liu J, et al. Biological Significance
of Prolactin in Gynecologic Cancers. Cancer Res. [Internet]. 2009; 69(12):5226–5233.
doi:10.1158/0008-5472.CAN-08-4652.
43. Mor G, Visintin I, Lai Y, Zhao H, Schwartz P, Rutherford T, et al. Serum protein markers
for early detection of ovarian cancer. Proc. Natl. Acad. Sci. U. S. A. 2005; 102(21):7677–
7682. doi:10.1073/pnas.0502178102.
44. Tan D, Chen KE, Khoo T, Walker AM. Prolactin increases survival and migration of
ovarian cancer cells: importance of prolactin receptor type and therapeutic potential of
S179D and G129R receptor antagonists. Cancer Lett. [Internet]. 2011 [cited 2016 Mar
29]; 310(1):101–8. doi:10.1016/j.canlet.2011.06.014.
45. Tas F, Karabulut S, Serilmez M, Ciftci R, Duranyildiz D. Clinical significance of serum
transforming growth factor-beta 1 (TGF-$β$1) levels in patients with epithelial ovarian
cancer. Tumor Biol. [Internet]. 2013; 35(4):3611–3616. doi:10.1007/s13277-013-1476-6.
46. Pasche B. Role of transforming growth factor beta in cancer. J. Cell. Physiol. 2001;
186(2):153–168. doi:10.1002/1097-4652(200002)186:2<153::AID-JCP1016>3.0.CO;2-J.
47. Ikushima H, Miyazono K. TGFβ signalling: a complex web in cancer progression. Nat
Rev Cancer [Internet]. 2010; 10(6):415–424. Available from:
http://dx.doi.org/10.1038/nrc2853.
132
48. Rodriguez GC, Haisley C, Hurteau J, Moser TL, Whitaker R, Bast RCJ, et al. Regulation
of invasion of epithelial ovarian cancer by transforming growth factor-beta. Gynecol.
Oncol. 2001; 80(2):245–253. doi:10.1006/gyno.2000.6042.
49. JAMMAL MP, DA SILVA AA, FILHO AM, DE CASTRO CÔBO E, ADAD SJ,
MURTA EFC, et al. Immunohistochemical staining of tumor necrosis factor-α and
interleukin-10 in benign and malignant ovarian neoplasms. Oncol. Lett. [Internet]. 2015;
9(2):979–983. doi:10.3892/ol.2014.2781.
50. Kulbe H, Thompson R, Wilson JL, Robinson S, Hagemann T, Fatah R, et al. The
Inflammatory Cytokine Tumor Necrosis Factor-α Generates an Autocrine Tumor-
Promoting Network in Epithelial Ovarian Cancer Cells. Cancer Res. [Internet]. 2007;
67(2):585–592. doi:10.1158/0008-5472.CAN-06-2941.
51. Grivennikov SI, Greten FR, Karin M. Immunity, Inflammation, and Cancer. Cell
[Internet]. 2010; 140(6):883–899. doi:10.1016/j.cell.2010.01.025.
52. Szlosarek PW, Grimshaw MJ, Kulbe H, Wilson JL, Wilbanks GD, Burke F, et al.
Expression and regulation of tumor necrosis factor α in normal and malignant ovarian
epithelium. Mol. Cancer Ther. [Internet]. 2006; 5(2):382–390. doi:10.1158/1535-
7163.MCT-05-0303.
53. Sethi G, Sung B, Aggarwal BB. TNF: a master switch for inflammation to cancer. Front.
Biosci. 2008; 13:5094–5107.
54. Bamberger ES, Perrett CW. Angiogenesis in epithelian ovarian cancer. Mol. Pathol.
[Internet]. 2002; 55(6):348–359. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1187269/.
55. Hazelton D, Nicosia RF, Nicosia S V. Vascular endothelial growth factor levels in ovarian
133
cyst fluid correlate with malignancy. Clin. Cancer Res. 1999; 5(4):823–829.
56. Yu L, Deng L, Li J, Zhang Y, Hu L. The prognostic value of vascular endothelial growth
factor in ovarian cancer: a systematic review and meta-analysis. Gynecol. Oncol. 2013;
128(2):391–396. doi:10.1016/j.ygyno.2012.11.002.
57. Kumar R, Yarmand-Bagheri R. The role of HER2 in angiogenesis. Semin. Oncol. 2001;
28(5 Suppl 16):27–32.
58. Agarwal R, D’Souza T, Morin PJ. Claudin-3 and claudin-4 expression in ovarian
epithelial cells enhances invasion and is associated with increased matrix
metalloproteinase-2 activity. Cancer Res. 2005; 65(16):7378–7385. doi:10.1158/0008-
5472.CAN-05-1036.
59. Hough CD, Sherman-Baust CA, Pizer ES, Montz FJ, Im DD, Rosenshein NB, et al.
Large-scale serial analysis of gene expression reveals genes differentially expressed in
ovarian cancer. Cancer Res. 2000; 60(22):6281–6287.
60. Lu KH, Patterson AP, Wang L, Marquez RT, Atkinson EN, Baggerly KA, et al. Selection
of potential markers for epithelial ovarian cancer with gene expression arrays and
recursive descent partition analysis. Clin. Cancer Res. 2004; 10(10):3291–3300.
doi:10.1158/1078-0432.CCR-03-0409.
61. Hibbs K, Skubitz KM, Pambuccian SE, Casey RC, Burleson KM, Oegema TRJ, et al.
Differential gene expression in ovarian carcinoma: identification of potential biomarkers.
Am. J. Pathol. 2004; 165(2):397–414. doi:10.1016/S0002-9440(10)63306-8.
62. Hough CD, Cho KR, Zonderman AB, Schwartz DR, Morin PJ. Coordinately up-regulated
genes in ovarian cancer. Cancer Res. 2001; 61(10):3869–3876.
63. Davidson B, Zhang Z, Kleinberg L, Li M, Florenes VA, Wang T-L, et al. Gene expression
134
signatures differentiate ovarian/peritoneal serous carcinoma from diffuse malignant
peritoneal mesothelioma. Clin. Cancer Res. 2006; 12(20 Pt 1):5944–5950.
doi:10.1158/1078-0432.CCR-06-1059.
64. Rangel LBA, Agarwal R, D’Souza T, Pizer ES, Alo PL, Lancaster WD, et al. Tight
junction proteins claudin-3 and claudin-4 are frequently overexpressed in ovarian cancer
but not in ovarian cystadenomas. Clin. Cancer Res. 2003; 9(7):2567–2575.
65. Bignotti E, Tassi RA, Calza S, Ravaggi A, Bandiera E, Rossi E, et al. Gene expression
profile of ovarian serous papillary carcinomas: identification of metastasis-associated
genes. Am. J. Obstet. Gynecol. 2007; 196(3):245.e1-11. doi:10.1016/j.ajog.2006.10.874.
66. Stewart JJ, White JT, Yan X, Collins S, Drescher CW, Urban ND, et al. Proteins
associated with Cisplatin resistance in ovarian cancer cells identified by quantitative
proteomic technology and integrated with mRNA expression levels. Mol. Cell.
Proteomics. 2006; 5(3):433–443. doi:10.1074/mcp.M500140-MCP200.
67. Cho A, Howell VM, Colvin EK. The Extracellular Matrix in Epithelial Ovarian Cancer –
A Piece of a Puzzle. Front. Oncol. 2015; 5. doi:10.3389/fonc.2015.00245.
68. Wu Y-H, Chang T-H, Huang Y-F, Huang H-D, Chou C-Y. COL11A1 promotes tumor
progression and predicts poor clinical outcome in ovarian cancer. Oncogene. 2014;
33(26):3432–3440. doi:10.1038/onc.2013.307.
69. Bager CL, Willumsen N, Leeming DJ, Smith V, Karsdal MA, Dornan D, et al. Collagen
degradation products measured in serum can separate ovarian and breast cancer patients
from healthy controls: A preliminary study. Cancer Biomark. 2015; 15(6):783–788.
doi:10.3233/CBM-150520.
70. Cheon D-J, Tong Y, Sim M-S, Dering J, Berel D, Cui X, et al. A collagen-remodeling
135
gene signature regulated by TGFβ signaling is associated with metastasis and poor
survival in serous ovarian cancer. Clin. Cancer Res. [Internet]. 2014; 20(3):711–723.
doi:10.1158/1078-0432.CCR-13-1256.
71. Januchowski R, Świerczewska M, Sterzyńska K, Wojtowicz K, Nowicki M, Zabel M.
Increased Expression of Several Collagen Genes is Associated with Drug Resistance in
Ovarian Cancer Cell Lines. J. Cancer [Internet]. 2016; 7(10):1295–1310.
doi:10.7150/jca.15371.
72. Kalli KR, Oberg AL, Keeney GL, Christianson TJH, Low PS, Knutson KL, et al. Folate
receptor alpha as a tumor target in epithelial ovarian cancer. Gynecol. Oncol. 2008;
108(3):619–626. doi:10.1016/j.ygyno.2007.11.020.
73. Chen S, Tai H, Tong X, Wang J, Yang F, Yang Y, et al. Variation and prognostic value of
serum plasminogen activator inhibitor-1 before and after chemotherapy in patients with
epithelial ovarian cancer. J. Obstet. Gynaecol. Res. [Internet]. 2014; 40(9):2058–2065.
doi:10.1111/jog.12474.
74. Koensgen D, Mustea A, Denkert C, Sun PM, Lichtenegger W, Sehouli J. Overexpression
of the plasminogen activator inhibitor type-1 in epithelial ovarian cancer. Anticancer Res.
2006; 26(2C):1683–1689.
75. Mashiko S, Kitatani K, Toyoshima M, Ichimura A, Dan T, Usui T, et al. Inhibition of
plasminogen activator inhibitor-1 is a potential therapeutic strategy in ovarian cancer.
Cancer Biol. Ther. 2015; 16(2):253–260. doi:10.1080/15384047.2014.1001271.
76. Tecimer C, Doering DL, Goldsmith LJ, Meyer JS, Abdulhay G, Wittliff JL. Clinical
relevance of urokinase-type plasminogen activator, its receptor and inhibitor type 1 in
ovarian cancer. Int. J. Gynecol. Cancer. 2000; 10(5):372–381.
136
77. Emami N, Diamandis EP. Utility of kallikrein-related peptidases (KLKs) as cancer
biomarkers. Clin. Chem. 2008; 54(10):1600–1607. doi:10.1373/clinchem.2008.105189.
78. Obiezu C V, Diamandis EP. Human tissue kallikrein gene family: applications in cancer.
Cancer Lett. 2005; 224(1):1–22. doi:10.1016/j.canlet.2004.09.024.
79. Luo L-Y, Katsaros D, Scorilas A, Fracchioli S, Bellino R, van Gramberen M, et al. The
serum concentration of human kallikrein 10 represents a novel biomarker for ovarian
cancer diagnosis and prognosis. Cancer Res. 2003; 63(4):807–811.
80. Borgono CA, Diamandis EP. The emerging roles of human tissue kallikreins in cancer.
Nat. Rev. Cancer. 2004; 4(11):876–890. doi:10.1038/nrc1474.
81. Michael IP, Sotiropoulou G, Pampalakis G, Magklara A, Ghosh M, Wasney G, et al.
Biochemical and enzymatic characterization of human kallikrein 5 (hK5), a novel serine
protease potentially involved in cancer progression. J. Biol. Chem. 2005; 280(15):14628–
14635. doi:10.1074/jbc.M408132200.
82. Kapadia C, Ghosh MC, Grass L, Diamandis EP. Human kallikrein 13 involvement in
extracellular matrix degradation. Biochem. Biophys. Res. Commun. 2004; 323(3):1084–
1090. doi:10.1016/j.bbrc.2004.08.206.
83. Ghosh MC, Grass L, Soosaipillai A, Sotiropoulou G, Diamandis EP. Human kallikrein 6
degrades extracellular matrix proteins and may enhance the metastatic potential of tumour
cells. Tumour Biol. 2004; 25(4):193–199. doi:10.1159/000081102.
84. Dong Y, Loessner D, Irving-Rodgers H, Obermair A, Nicklin JL, Clements JA. Metastasis
of ovarian cancer is mediated by kallikrein related peptidases. Clin. Exp. Metastasis
[Internet]. 2014; 31(1):135–147. doi:10.1007/s10585-013-9615-4.
85. Yousef GM, Diamandis EP. Tissue kallikreins: new players in normal and abnormal cell
137
growth? Thromb. Haemost. 2003; 90(1):7–16. doi:10.1267/THRO03010007.
86. Luo LY, Bunting P, Scorilas A, Diamandis EP. Human kallikrein 10: a novel tumor
marker for ovarian carcinoma? Clin. Chim. Acta. 2001; 306(1–2):111–118.
87. Das PM, Bast RCJ. Early detection of ovarian cancer. Biomark. Med. 2008; 2(3):291–303.
doi:10.2217/17520363.2.3.291.
88. Diamandis EP, Okui A, Mitsui S, Luo L-Y, Soosaipillai A, Grass L, et al. Human
kallikrein 11: a new biomarker of prostate and ovarian carcinoma. Cancer Res. 2002;
62(1):295–300.
89. Shih I-M, Davidson B. Pathogenesis of ovarian cancer: clues from selected overexpressed
genes. Future Oncol. 2009; 5(10):1641–1657. doi:10.2217/fon.09.126.
90. Yousef GM, Diamandis EP. The new human tissue kallikrein gene family: structure,
function, and association to disease. Endocr. Rev. 2001; 22(2):184–204.
doi:10.1210/edrv.22.2.0424.
91. Davidson B, Xi Z, Klokk TI, Trope CG, Dorum A, Scheistroen M, et al. Kallikrein 4
expression is up-regulated in epithelial ovarian carcinoma cells in effusions. Am. J. Clin.
Pathol. 2005; 123(3):360–368. doi:10.1309/PTBB-5BPC-KX8K-9V69.
92. John A, Tuszynski G. The role of matrix metalloproteinases in tumor angiogenesis and
tumor metastasis. Pathol. Oncol. Res. 2001; 7(1):14–23.
93. Kessenbrock K, Plaks V, Werb Z. Matrix Metalloproteinases: Regulators of the Tumor
Microenvironment. Cell [Internet]. 2010; 141(1):52–67. doi:10.1016/j.cell.2010.03.015.
94. Davidson B, Goldberg I, Gotlieb WH, Kopolovic J, Ben-Baruch G, Nesland JM, et al.
High levels of MMP-2, MMP-9, MT1-MMP and TIMP-2 mRNA correlate with poor
survival in ovarian carcinoma. Clin. Exp. Metastasis. 1999; 17(10):799–808.
138
95. Brun J-L, Cortez A, Commo F, Uzan S, Rouzier R, Darai E. Serous and mucinous ovarian
tumors express different profiles of MMP-2, -7, -9, MT1-MMP, and TIMP-1 and -2. Int. J.
Oncol. 2008; 33(6):1239–1246.
96. Deng J, Wang L, Chen H, Li L, Ma Y, Ni J, et al. The role of tumour-associated MUC1 in
epithelial ovarian cancer metastasis and progression. Cancer Metastasis Rev. [Internet].
2013; 32(3):535–551. doi:10.1007/s10555-013-9423-y.
97. Wang L, Ma J, Liu F, Yu Q, Chu G, Perkins AC, et al. Expression of MUC1 in primary
and metastatic human epithelial ovarian cancer and its therapeutic significance. Gynecol.
Oncol. [Internet]. 2007 [cited 2016 Mar 28]; 105(3):695–702.
doi:10.1016/j.ygyno.2007.02.004.
98. Chauhan SC, Kumar D, Jaggi M. Mucins in ovarian cancer diagnosis and therapy. J.
Ovarian Res. [Internet]. 2009; 2:21. doi:10.1186/1757-2215-2-21.
99. Chauhan SC, Singh AP, Ruiz F, Johansson SL, Jain M, Smith LM, et al. Aberrant
expression of MUC4 in ovarian carcinoma: diagnostic significance alone and in
combination with MUC1 and MUC16 (CA125). Mod. Pathol. an Off. J. United States
Can. Acad. Pathol. Inc. 2006; 19(10):1386–1394. doi:10.1038/modpathol.3800646.
100. Davidson B, Baekelandt M, Shih I-M. MUC4 is upregulated in ovarian carcinoma
effusions and differentiates carcinoma cells from mesothelial cells. Diagn. Cytopathol.
2007; 35(12):756–760. doi:10.1002/dc.20771.
101. Ponnusamy MP, Singh AP, Jain M, Chakraborty S, Moniaux N, Batra SK. MUC4
activates HER2 signalling and enhances the motility of human ovarian cancer cells. Br. J.
Cancer. 2008; 99(3):520–526. doi:10.1038/sj.bjc.6604517.
102. Felder M, Kapur A, Gonzalez-Bosquet J, Horibata S, Heintz J, Albrecht R, et al. MUC16
139
(CA125): tumor biomarker to cancer therapy, a work in progress. Mol. Cancer [Internet].
2014; 13:129. doi:10.1186/1476-4598-13-129.
103. Cohen JG, White M, Cruz A, Farias-Eisner R. In 2014, can we do better than CA125 in
the early detection of ovarian cancer? World J. Biol. Chem. [Internet]. 2014; 5(3):286–
300. doi:10.4331/wjbc.v5.i3.286.
104. Sundar S, Neal RD, Kehoe S. Diagnosis of ovarian cancer. BMJ [Internet]. 2015; 351.
Available from: http://www.bmj.com/content/351/bmj.h4443.abstract.
105. Dai J, Peng L, Fan K, Wang H, Wei R, Ji G, et al. Osteopontin induces angiogenesis
through activation of PI3K/AKT and ERK1/2 in endothelial cells. Oncogene. 2009;
28(38):3412–3422. doi:10.1038/onc.2009.189.
106. Song G, Cai Q-F, Mao Y-B, Ming Y-L, Bao S-D, Ouyang G-L. Osteopontin promotes
ovarian cancer progression and cell survival and increases HIF-1alpha expression through
the PI3-K/Akt pathway. Cancer Sci. 2008; 99(10):1901–1907. doi:10.1111/j.1349-
7006.2008.00911.x.
107. Song G, Ouyang G, Mao Y, Ming Y, Bao S, Hu T. Osteopontin promotes gastric cancer
metastasis by augmenting cell survival and invasion through Akt-mediated HIF-1alpha
up-regulation and MMP9 activation. J. Cell. Mol. Med. 2009; 13(8B):1706–1718.
doi:10.1111/j.1582-4934.2008.00540.x.
108. Sarojini S, Tamir A, Lim H, Li S, Zhang S, Goy A, et al. Early detection biomarkers for
ovarian cancer. J. Oncol. 2012; 2012:709049. doi:10.1155/2012/709049.
109. Kim J-H, Skates SJ, Uede T, Wong K, Schorge JO, Feltmate CM, et al. Osteopontin as a
potential diagnostic biomarker for ovarian cancer. JAMA. 2002; 287(13):1671–1679.
110. Sun W, Xing B, Sun Y, Du X, Lu M, Hao C, et al. Proteome analysis of hepatocellular
140
carcinoma by two-dimensional difference gel electrophoresis: novel protein markers in
hepatocellular carcinoma tissues. Mol. Cell. Proteomics. 2007; 6(10):1798–1808.
doi:10.1074/mcp.M600449-MCP200.
111. Erlich RB, Kahn SA, Lima FRS, Muras AG, Martins RAP, Linden R, et al. STI1 promotes
glioma proliferation through MAPK and PI3K pathways. Glia. 2007; 55(16):1690–1698.
doi:10.1002/glia.20579.
112. Carta F, Demuro PP, Zanini C, Santona A, Castiglia D, D’Atri S, et al. Analysis of
candidate genes through a proteomics-based approach in primary cell lines from
malignant melanomas and their metastases. Melanoma Res. 2005; 15(4):235–244.
113. Walsh N, O’Donovan N, Kennedy S, Henry M, Meleady P, Clynes M, et al. Identification
of pancreatic cancer invasion-related proteins by proteomic analysis. Proteome Sci. 2009;
7:3. doi:10.1186/1477-5956-7-3.
114. Walsh N, Larkin A, Swan N, Conlon K, Dowling P, McDermott R, et al. RNAi
knockdown of Hop (Hsp70/Hsp90 organising protein) decreases invasion via MMP-2
down regulation. Cancer Lett. [Internet]. 2011; 306(2):180–189.
doi:http://dx.doi.org/10.1016/j.canlet.2011.03.004.
115. Cho H, Kim S, Shin H-Y, Chung EJ, Kitano H, Hyon Park J, et al. Expression of stress-
induced phosphoprotein1 (STIP1) is associated with tumor progression and poor
prognosis in epithelial ovarian cancer. Genes, Chromosom. Cancer [Internet]. 2014;
53(4):277–288. doi:10.1002/gcc.22136.
116. Wang T-H, Chao A, Tsai C-L, Chang C-L, Chen S-H, Lee Y-S, et al. Stress-induced
phosphoprotein 1 as a secreted biomarker for human ovarian cancer promotes cancer cell
proliferation. Mol. Cell. Proteomics. 2010; 9(9):1873–1884.
141
doi:10.1074/mcp.M110.000802.
117. Kim S, Cho H, Nam EJ, Kim SW, Kim YT, Park YW, et al. Autoantibodies against stress-
induced phosphoprotein-1 as a novel biomarker candidate for ovarian cancer. Genes.
Chromosomes Cancer. 2010; 49(7):585–595. doi:10.1002/gcc.20769.
118. Chen Y-C, Pohl G, Wang T-L, Morin PJ, Risberg B, Kristensen GB, et al. Apolipoprotein
E is required for cell proliferation and survival in ovarian cancer. Cancer Res. 2005;
65(1):331–337.
119. Kuhajda FP. Fatty acid synthase and cancer: new application of an old pathway. Cancer
Res. 2006; 66(12):5977–5980. doi:10.1158/0008-5472.CAN-05-4673.
120. Gansler TS, Hardman W 3rd, Hunt DA, Schaffel S, Hennigar RA. Increased expression of
fatty acid synthase (OA-519) in ovarian neoplasms predicts shorter survival. Hum. Pathol.
1997; 28(6):686–692.
121. Alo PL, Visca P, Framarino ML, Botti C, Monaco S, Sebastiani V, et al.
Immunohistochemical study of fatty acid synthase in ovarian neoplasms. Oncol. Rep.
2000; 7(6):1383–1388.
122. Ueda SM, Yap KL, Davidson B, Tian Y, Murthy V, Wang T-L, et al. Expression of Fatty
Acid Synthase Depends on NAC1 and Is Associated with Recurrent Ovarian Serous
Carcinomas. J. Oncol. 2010; 2010:285191. doi:10.1155/2010/285191.
123. Orita H, Coulter J, Tully E, Kuhajda FP, Gabrielson E. Inhibiting fatty acid synthase for
chemoprevention of chemically induced lung tumors. Clin. Cancer Res. 2008;
14(8):2458–2464. doi:10.1158/1078-0432.CCR-07-4177.
124. Jensen V, Ladekarl M, Holm-Nielsen P, Melsen F, Soerensen FB. The prognostic value of
oncogenic antigen 519 (OA-519) expression and proliferative activity detected by
142
antibody MIB-1 in node-negative breast cancer. J. Pathol. 1995; 176(4):343–352.
doi:10.1002/path.1711760405.
125. Shurbaji MS, Kalbfleisch JH, Thurmond TS. Immunohistochemical detection of a fatty
acid synthase (OA-519) as a predictor of progression of prostate cancer. Hum. Pathol.
1996; 27(9):917–921.
126. Menendez JA, Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer
pathogenesis. Nat. Rev. Cancer. 2007; 7(10):763–777. doi:10.1038/nrc2222.
127. Hellstrom I, Raycraft J, Hayden-Ledbetter M, Ledbetter JA, Schummer M, McIntosh M,
et al. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res. 2003;
63(13):3695–3700.
128. Tang Z, Chang X, Ye X, Li Y, Cheng H, Cui H. Usefulness of human epididymis protein
4 in predicting cytoreductive surgical outcomes for advanced ovarian tubal and peritoneal
carcinoma. Chin. J. Cancer Res. 2015; 27(3):309–317. doi:10.3978/j.issn.1000-
9604.2015.06.01.
129. Chang K, Pastan I. Molecular cloning of mesothelin, a differentiation antigen present on
mesothelium, mesotheliomas, and ovarian cancers. Proc. Natl. Acad. Sci. U. S. A. 1996;
93(1):136–140.
130. Ho M, Hassan R, Zhang J, Wang Q-C, Onda M, Bera T, et al. Humoral immune response
to mesothelin in mesothelioma and ovarian cancer patients. Clin. Cancer Res. 2005;
11(10):3814–3820. doi:10.1158/1078-0432.CCR-04-2304.
131. Badgwell D, Lu Z, Cole L, Fritsche H, Atkinson EN, Somers E, et al. Urinary mesothelin
provides greater sensitivity for early stage ovarian cancer than serum mesothelin, urinary
hCG free beta subunit and urinary hCG beta core fragment. Gynecol. Oncol. 2007;
143
106(3):490–497. doi:10.1016/j.ygyno.2007.04.022.
132. Yen MJ, Hsu C-Y, Mao T-L, Wu T-C, Roden R, Wang T-L, et al. Diffuse mesothelin
expression correlates with prolonged patient survival in ovarian serous carcinoma. Clin.
Cancer Res. 2006; 12(3 Pt 1):827–831. doi:10.1158/1078-0432.CCR-05-1397.
133. Mok SC, Chao J, Skates S, Wong K, Yiu GK, Muto MG, et al. Prostasin, a potential
serum marker for ovarian cancer: identification through microarray technology. J. Natl.
Cancer Inst. 2001; 93(19):1458–1464.
134. Kumar J, Ward AC. Role of the interleukin 6 receptor family in epithelial ovarian cancer
and its clinical implications. Biochim. Biophys. Acta [Internet]. 2014 [cited 2016 Mar 26];
1845(2):117–25. doi:10.1016/j.bbcan.2013.12.003.
135. Scheller J, Chalaris A, Schmidt-Arras D, Rose-John S. The pro- and anti-inflammatory
properties of the cytokine interleukin-6. Biochim. Biophys. Acta - Mol. Cell Res.
[Internet]. 2011; 1813(5):878–888. doi:http://dx.doi.org/10.1016/j.bbamcr.2011.01.034.
136. Miao J-W, Liu L-J, Huang J. Interleukin-6-induced epithelial-mesenchymal transition
through signal transducer and activator of transcription 3 in human cervical carcinoma.
Int. J. Oncol. 2014; 45(1):165–176. doi:10.3892/ijo.2014.2422.
137. Arango Duque G, Descoteaux A. Macrophage Cytokines: Involvement in Immunity and
Infectious Diseases. Front. Immunol. [Internet]. 2014; 5:491.
doi:10.3389/fimmu.2014.00491.
138. El Kasmi KC, Holst J, Coffre M, Mielke L, de Pauw A, Lhocine N, et al. General nature
of the STAT3-activated anti-inflammatory response. J. Immunol. 2006; 177(11):7880–
7888.
139. Varma TK, Toliver-Kinsky TE, Lin CY, Koutrouvelis AP, Nichols JE, Sherwood ER.
144
Cellular mechanisms that cause suppressed gamma interferon secretion in endotoxin-
tolerant mice. Infect. Immun. 2001; 69(9):5249–5263.
140. Aste-Amezaga M, Ma X, Sartori A, Trinchieri G. Molecular mechanisms of the induction
of IL-12 and its inhibition by IL-10. J. Immunol. 1998; 160(12):5936–5944.
141. Sethi BK, Chanukya G V, Nagesh VS. Prolactin and cancer: Has the orphan finally found
a home? Indian J. Endocrinol. Metab. [Internet]. 2012; 16(Suppl 2):S195–S198.
doi:10.4103/2230-8210.104038.
142. López-Rincón G, Pereira-Suárez AL, Del Toro-Arreola S, Sánchez-Hernández PE,
Ochoa-Zarzosa A, Muñoz-Valle JF, et al. Lipopolysaccharide induces the expression of an
autocrine prolactin loop enhancing inflammatory response in monocytes. J. Inflamm.
[Internet]. 2013; 10(1):1–12. doi:10.1186/1476-9255-10-24.
143. Dunfield LD, Dwyer EJC, Nachtigal MW. TGF beta-induced Smad signaling remains
intact in primary human ovarian cancer cells. Endocrinology. 2002; 143(4):1174–1181.
doi:10.1210/endo.143.4.8733.
144. de Caestecker MP, Piek E, Roberts AB. Role of Transforming Growth Factor-β Signaling
in Cancer. J. Natl. Cancer Inst. [Internet]. 2000; 92(17):1388–1402.
doi:10.1093/jnci/92.17.1388.
145. Cheng J-C, Auersperg N, Leung PCK. TGF-Beta Induces Serous Borderline Ovarian
Tumor Cell Invasion by Activating EMT but Triggers Apoptosis in Low-Grade Serous
Ovarian Carcinoma Cells. PLoS One [Internet]. 2012; 7(8):e42436. Available from:
http://dx.doi.org/10.1371%252Fjournal.pone.0042436.
146. Leivonen S-K, Kahari V-M. Transforming growth factor-beta signaling in cancer invasion
and metastasis. Int. J. cancer. 2007; 121(10):2119–2124. doi:10.1002/ijc.23113.
145
147. WANG X, LIN Y. Tumor necrosis factor and cancer, buddies or foes? Acta Pharmacol.
Sin. [Internet]. 2008; 29(11):1275–1288. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2631033/.
148. Koch S, Claesson-Welsh L. Signal Transduction by Vascular Endothelial Growth Factor
Receptors. Cold Spring Harb. Perspect. Med. [Internet]. 2012; 2(7):a006502.
doi:10.1101/cshperspect.a006502.
149. Chen TT, Luque A, Lee S, Anderson SM, Segura T, Iruela-Arispe ML. Anchorage of
VEGF to the extracellular matrix conveys differential signaling responses to endothelial
cells. J. Cell Biol. [Internet]. 2010; 188(4):595 LP-609. Available from:
http://jcb.rupress.org/content/188/4/595.abstract.
150. Katahira J, Inoue N, Horiguchi Y, Matsuda M, Sugimoto N. Molecular cloning and
functional characterization of the receptor for Clostridium perfringens enterotoxin. J. Cell
Biol. 1997; 136(6):1239–1247.
151. McClane BA. An overview of Clostridium perfringens enterotoxin. Toxicon. 1996;
34(11–12):1335–1343.
152. Santin AD, Cane S, Bellone S, Palmieri M, Siegel ER, Thomas M, et al. Treatment of
chemotherapy-resistant human ovarian cancer xenografts in C.B-17/SCID mice by
intraperitoneal administration of Clostridium perfringens enterotoxin. Cancer Res. 2005;
65(10):4334–4342. doi:10.1158/0008-5472.CAN-04-3472.
153. Singh AP, Chaturvedi P, Batra SK. Emerging roles of MUC4 in cancer: a novel target for
diagnosis and therapy. Cancer Res. 2007; 67(2):433–436. doi:10.1158/0008-5472.CAN-
06-3114.
154. Ahmed N, Riley C, Rice G, Quinn M. Role of integrin receptors for fibronectin, collagen
146
and laminin in the regulation of ovarian carcinoma functions in response to a matrix
microenvironment. Clin. Exp. Metastasis. 2005; 22(5):391–402. doi:10.1007/s10585-005-
1262-y.
155. Stoppelli MP. The Plasminogen Activation System in Cell Invasion. Madame Curie
Biosci. Database [Internet]. Landes Bio. Available from:
https://www.ncbi.nlm.nih.gov/books/NBK6146/.
156. Carter JC, Church FC. Obesity and Breast Cancer: The Roles of Peroxisome Proliferator-
Activated Receptor-γ and Plasminogen Activator Inhibitor-1. PPAR Res. [Internet]. 2009;
2009:345320. doi:10.1155/2009/345320.
157. Kenny HA, Lengyel E. MMP-2 functions as an early response protein in ovarian cancer
metastasis. Cell Cycle [Internet]. 2009; 8(5):683–688. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2840625/.
158. Kenny HA, Kaur S, Coussens LM, Lengyel E. The initial steps of ovarian cancer cell
metastasis are mediated by MMP-2 cleavage of vitronectin and fibronectin. J. Clin. Invest.
[Internet]. 2008; 118(4):1367–1379. doi:10.1172/JCI33775.
159. Odunuga OO, Longshaw VM, Blatch GL. Hop: more than an Hsp70/Hsp90 adaptor
protein. Bioessays. 2004; 26(10):1058–1068. doi:10.1002/bies.20107.
160. Longshaw VM, Chapple JP, Balda MS, Cheetham ME, Blatch GL. Nuclear translocation
of the Hsp70/Hsp90 organizing protein mSTI1 is regulated by cell cycle kinases. J. Cell
Sci. 2004; 117(Pt 5):701–710. doi:10.1242/jcs.00905.
161. Chao A, Lee L-Y, Hsueh C, Lin C-Y, Tsai C-L, Chao A-S, et al. Immunohistological
analysis of stress-induced phosphoprotein 1 in ovarian cancer patients with low serum
cancer antigen 125 levels. Taiwan. J. Obstet. Gynecol. 2013; 52(2):185–191.
147
doi:10.1016/j.tjog.2013.04.006.
162. Gliemann J. Receptors of the low density lipoprotein (LDL) receptor family in man.
Multiple functions of the large family members via interaction with complex ligands. Biol.
Chem. 1998; 379(8–9):951–964.
163. Kuhajda FP, Jenner K, Wood FD, Hennigar RA, Jacobs LB, Dick JD, et al. Fatty acid
synthesis: a potential selective target for antineoplastic therapy. Proc. Natl. Acad. Sci. U.
S. A. 1994; 91(14):6379–6383.
164. Epstein JI, Carmichael M, Partin AW. OA-519 (fatty acid synthase) as an independent
predictor of pathologic state in adenocarcinoma of the prostate. Urology. 1995; 45(1):81–
86.
165. Alo’ PL, Visca P, Marci A, Mangoni A, Botti C, Di Tondo U. Expression of fatty acid
synthase (FAS) as a predictor of recurrence in stage I breast carcinoma patients. Cancer.
1996; 77(3):474–482. doi:10.1002/(SICI)1097-0142(19960201)77:3<474::AID-
CNCR8>3.0.CO;2-K.
166. Rashid A, Pizer ES, Moga M, Milgraum LZ, Zahurak M, Pasternack GR, et al. Elevated
expression of fatty acid synthase and fatty acid synthetic activity in colorectal neoplasia.
Am. J. Pathol. 1997; 150(1):201–208.
167. Milgraum LZ, Witters LA, Pasternack GR, Kuhajda FP. Enzymes of the fatty acid
synthesis pathway are highly expressed in in situ breast carcinoma. Clin. Cancer Res.
1997; 3(11):2115–2120.
168. Pizer ES, Wood FD, Heine HS, Romantsev FE, Pasternack GR, Kuhajda FP. Inhibition of
fatty acid synthesis delays disease progression in a xenograft model of ovarian cancer.
Cancer Res. 1996; 56(6):1189–1193.
148
169. Ross J, Najjar AM, Sankaranarayanapillai M, Tong WP, Kaluarachchi K, Ronen SM.
Fatty acid synthase inhibition results in a magnetic resonance-detectable drop in
phosphocholine. Mol. Cancer Ther. 2008; 7(8):2556–2565. doi:10.1158/1535-7163.MCT-
08-0015.
170. Grunt TW, Wagner R, Grusch M, Berger W, Singer CF, Marian B, et al. Interaction
between fatty acid synthase- and ErbB-systems in ovarian cancer cells. Biochem. Biophys.
Res. Commun. [Internet]. 2009; 385(3):454–459.
doi:http://dx.doi.org/10.1016/j.bbrc.2009.05.085.
171. Bauerschlag DO, Maass N, Leonhardt P, Verburg FA, Pecks U, Zeppernick F, et al. Fatty
acid synthase overexpression: target for therapy and reversal of chemoresistance in
ovarian cancer. J. Transl. Med. [Internet]. 2015; 13:146. doi:10.1186/s12967-015-0511-3.
172. Basal E, Eghbali-Fatourechi GZ, Kalli KR, Hartmann LC, Goodman KM, Goode EL, et
al. Functional folate receptor alpha is elevated in the blood of ovarian cancer patients.
PLoS One. 2009; 4(7):e6292. doi:10.1371/journal.pone.0006292.
173. Yuan Y, Nymoen DA, Dong HP, Bjorang O, Shih I-M, Low PS, et al. Expression of the
folate receptor genes FOLR1 and FOLR3 differentiates ovarian carcinoma from breast
carcinoma and malignant mesothelioma in serous effusions. Hum. Pathol. 2009;
40(10):1453–1460. doi:10.1016/j.humpath.2009.02.013.
174. Gibbs DD, Theti DS, Wood N, Green M, Raynaud F, Valenti M, et al. BGC 945, a novel
tumor-selective thymidylate synthase inhibitor targeted to alpha-folate receptor-
overexpressing tumors. Cancer Res. 2005; 65(24):11721–11728. doi:10.1158/0008-
5472.CAN-05-2034.
175. Kirchhoff C. Molecular characterization of epididymal proteins. Rev. Reprod. 1998;
149
3(2):86–95.
176. Moore RG, Hill EK, Horan T, Yano N, Kim K, MacLaughlan S, et al. HE4 (WFDC2)
gene overexpression promotes ovarian tumor growth. Sci. Rep. 2014; 4:3574.
doi:10.1038/srep03574.
177. Ordonez NG. Application of mesothelin immunostaining in tumor diagnosis. Am. J. Surg.
Pathol. 2003; 27(11):1418–1428.
178. Rump A, Morikawa Y, Tanaka M, Minami S, Umesaki N, Takeuchi M, et al. Binding of
ovarian cancer antigen CA125/MUC16 to mesothelin mediates cell adhesion. J. Biol.
Chem. 2004; 279(10):9190–9198. doi:10.1074/jbc.M312372200.
179. Feng Y, Xiao X, Zhu Z, Streaker E, Ho M, Pastan I, et al. A novel human monoclonal
antibody that binds with high affinity to mesothelin-expressing cells and kills them by
antibody-dependent cell-mediated cytotoxicity. Mol. Cancer Ther. 2009; 8(5):1113–1118.
doi:10.1158/1535-7163.MCT-08-0945.
180. Tamir A, Gangadharan A, Balwani S, Tanaka T, Patel U, Hassan A, et al. The serine
protease prostasin (PRSS8) is a potential biomarker for early detection of ovarian cancer.
J. Ovarian Res. [Internet]. 2016; 9:20. doi:10.1186/s13048-016-0228-9.
181. Costa FP, Junior ELB, Zelmanowicz A, Svedman C, Devenz G, Alves S, et al. Prostasin,
A Potential Tumor Marker in Ovarian Cancer- A Pilot Study. Clinics (Sao Paulo).
[Internet]. 2009; 64(7):641–644. doi:10.1590/S1807-59322009000700006.
182. Yu JX, Chao L, Chao J. Molecular cloning, tissue-specific expression, and cellular
localization of human prostasin mRNA. J. Biol. Chem. 1995; 270(22):13483–13489.
183. Romero P, Obradovic Z, Li X, Garner EC, Brown CJ, Dunker AK. Sequence complexity
of disordered protein. Proteins. 2001; 42(1):38–48.
150
184. Obradovic Z, Peng K, Vucetic S, Radivojac P, Dunker AK. Exploiting heterogeneous
sequence properties improves prediction of protein disorder. Proteins. 2005; 61 Suppl
7:176–182. doi:10.1002/prot.20735.
185. Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z. Length-dependent prediction
of protein intrinsic disorder. BMC Bioinformatics. 2006; 7:208. doi:10.1186/1471-2105-7-
208.
186. Tsolis AC, Papandreou NC, Iconomidou VA, Hamodrakas SJ. A consensus method for
the prediction of “aggregation-prone” peptides in globular proteins. PLoS One. 2013;
8(1):e54175. doi:10.1371/journal.pone.0054175.
187. Consortium TU. UniProt: a hub for protein information. Nucleic Acids Res [Internet].
2015; 43(D1):D204–D212. doi:10.1093/nar/gku989.
188. Hornbeck P V, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E.
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015;
43(Database issue):D512-20. doi:10.1093/nar/gku1267.
189. Gupta R, Jung E, Brunak S. Prediction of N-glycosylation sites in human proteins. Prep.
2004;
190. Steentoft C, Vakhrushev SY, Joshi HJ, Kong Y, Vester-Christensen MB, Schjoldager KT-
BG, et al. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell
technology. EMBO J. 2013; 32(10):1478–1488. doi:10.1038/emboj.2013.79.
191. Sekiyama N, Ikegami T, Yamane T, Ikeguchi M, Uchimura Y, Baba D, et al. Structure of
the Small Ubiquitin-like Modifier (SUMO)-interacting Motif of MBD1-containing
Chromatin-associated Factor 1 Bound to SUMO-3. J. Biol. Chem. [Internet]. 2008;
283(51):35966–35975. doi:10.1074/jbc.M802528200 .
151
192. Xue Y, Chen H, Jin C, Sun Z, Yao X. NBA-Palm: prediction of palmitoylation site
implemented in Naive Bayes algorithm. BMC Bioinformatics. 2006; 7:458.
doi:10.1186/1471-2105-7-458.
193. Kiemer L, Bendtsen JD, Blom N. NetAcet: prediction of N-terminal acetylation sites.
Bioinformatics. 2005; 21(7):1269–1270. doi:10.1093/bioinformatics/bti130.
194. Monigatti F, Gasteiger E, Bairoch A, Jung E. The Sulfinator: predicting tyrosine sulfation
sites in protein sequences. Bioinformatics. 2002; 18(5):769–770.
195. Bologna G, Yvon C, Duvaud S, Veuthey A-L. N-Terminal myristoylation predictions by
ensembles of neural networks. Proteomics. 2004; 4(6):1626–1632.
doi:10.1002/pmic.200300783.
196. Maurer-Stroh S, Eisenhaber F. Refinement and prediction of protein prenylation motifs.
Genome Biol. 2005; 6(6):R55. doi:10.1186/gb-2005-6-6-r55.
197. Chen H, Xue Y, Huang N, Yao X, Sun Z. MeMo: a web tool for prediction of protein
methylation modifications. Nucleic Acids Res. 2006; 34(Web Server issue):W249-53.
doi:10.1093/nar/gkl233.
198. Baeuerle PA, Huttner WB. Tyrosine sulfation is a trans-Golgi-specific protein
modification. J. Cell Biol. 1987; 105(6 Pt 1):2655–2664.
199. Aitken A, Learmonth M. The Protein Protocols Handbook [Internet]. In: Walker JM,
editor. . Totowa, NJ: Humana Press; 2002. p. 459–460. doi:10.1385/1-59259-169-8:459.
200. Jensen ON. Modification-specific proteomics: characterization of post-translational
modifications by mass spectrometry. Curr. Opin. Chem. Biol. 2004; 8(1):33–41.
doi:10.1016/j.cbpa.2003.12.009.
201. Yang X-J, Seto E. Lysine acetylation: codified crosstalk with other posttranslational
152
modifications. Mol. Cell. 2008; 31(4):449–461. doi:10.1016/j.molcel.2008.07.002.
202. Zhang Z, Tan M, Xie Z, Dai L, Chen Y, Zhao T. Identification of lysine succinylation as a
new post-translational modification. Nat. Chem. Biol. [Internet]. 2011; 7(1):58–63.
doi:10.1038/nchembio.495.
203. Iino T, Takahashi M, Sako T. Role of amino-terminal positive charge on signal peptide in
staphylokinase export across the cytoplasmic membrane of Escherichia coli. J. Biol.
Chem. 1987; 262(15):7412–7417.
204. Resh MD. Covalent Lipid Modifications of Proteins. Curr. Biol. [Internet]. 2013;
23(10):R431–R435. doi:10.1016/j.cub.2013.04.024.
205. Turoverov KK, Kuznetsova IM, Uversky VN. The Protein Kingdom Extended: Ordered
and Intrinsically Disordered Proteins, Their Folding, Supramolecular Complex Formation,
and Aggregation. Prog. Biophys. Mol. Biol. 2010; 102(2–3):73–84.
doi:10.1016/j.pbiomolbio.2010.01.003.
206. Buck PM, Kumar S, Singh SK. On the role of aggregation prone regions in protein
evolution, stability, and enzymatic catalysis: insights from diverse analyses. PLoS
Comput. Biol. [Internet]. 2013 [cited 2015 Dec 22]; 9(10):e1003291.
doi:10.1371/journal.pcbi.1003291.
207. Shental-Bechor D, Levy Y. Effect of glycosylation on protein folding: A close look at
thermodynamic stabilization. Proc. Natl. Acad. Sci. U. S. A. [Internet]. 2008;
105(24):8256–8261. doi:10.1073/pnas.0801340105.
208. Nestler EJ, Greengard P. Protein Phosphorylation is of Fundamental Importance in
Biological Regulation. 6th ed. Philadelphia: Lippincott-Raven; 1999.
209. Cheema TMK and AK. Small Changes Huge Impact: The Role of Protein
153
Posttranslational Modifications in Cellular Homeostasis and Disease. J. Amino Acids.
2011; 2011.
210. Reimand J, Wagih O, Bader GD. The mutational landscape of phosphorylation signaling
in cancer. Sci. Rep. 2013; 3:2651. doi:10.1038/srep02651.
211. Gregoire S, Yang X-J. Association with class IIa histone deacetylases upregulates the
sumoylation of MEF2 transcription factors. Mol. Cell. Biol. 2005; 25(6):2273–2287.
doi:10.1128/MCB.25.6.2273-2287.2005.
212. Girdwood DWH, Tatham MH, Hay RT. SUMO and transcriptional regulation. Semin.
Cell Dev. Biol. 2004; 15(2):201–210.
213. Besnault-Mascard L, Leprince C, Auffredou MT, Meunier B, Bourgeade MF, Camonis J,
et al. Caspase-8 sumoylation is associated with nuclear localization. Oncogene. 2005;
24(20):3268–3273. doi:10.1038/sj.onc.1208448.
214. Liang M, Melchior F, Feng X-H, Lin X. Regulation of Smad4 sumoylation and
transforming growth factor-beta signaling by protein inhibitor of activated STAT1. J.
Biol. Chem. 2004; 279(22):22857–22865. doi:10.1074/jbc.M401554200.
215. Wilkinson KA, Henley JM. Mechanisms, regulation and consequences of protein
SUMOylation. Biochem. J. 2010; 428(2):133–145. doi:10.1042/BJ20100158.
216. Miteva M, Keusekotten K, Hofmann K, Praefcke GJK, Dohmen RJ. Sumoylation as a
Signal for Polyubiquitylation and Proteasomal Degradation [Internet]. In: Groettrup M,
editor. Conjugation and Deconjugation of Ubiquitin Family Modifiers: Subcellular
Biochemistry. New York, NY: Springer New York; 2010. p. 195–214. doi:10.1007/978-1-
4419-6676-6_16.
217. Hershko A, Heller H, Eytan E, Kaklij G, Rose IA. Role of the alpha-amino group of
154
protein in ubiquitin-mediated protein breakdown. Proc. Natl. Acad. Sci. U. S. A.
[Internet]. 1984; 81(22):7021–7025. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC392068/.
218. Edwards YJK, Lobley AE, Pentony MM, Jones DT. Insights into the regulation of
intrinsically disordered proteins in the human proteome by analyzing sequence and gene
expression data. Genome Biol. [Internet]. 2009; 10(5):1–18. doi:10.1186/gb-2009-10-5-
r50.
219. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8--a global
view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res.
2009; 37(Database issue):D412-6. doi:10.1093/nar/gkn760.
220. Luo Z, Wang Q, Lau WB, Lau B, Xu L, Zhao L, et al. Tumor microenvironment: The
culprit for ovarian cancer metastasis? Cancer Lett. 2016; 377(2):174–182.
doi:10.1016/j.canlet.2016.04.038.
221. Turner TB, Buchsbaum DJ, Straughn JMJ, Randall TD, Arend RC. Ovarian cancer and
the immune system - The role of targeted therapies. Gynecol. Oncol. 2016; 142(2):349–
356. doi:10.1016/j.ygyno.2016.05.007.
222. Suh DH, Kim HS, Kim B, Song YS. Metabolic orchestration between cancer cells and
tumor microenvironment as a co-evolutionary source of chemoresistance in ovarian
cancer: a therapeutic implication. Biochem. Pharmacol. 2014; 92(1):43–54.
doi:10.1016/j.bcp.2014.08.011.
223. Owens OJ, Taggart C, Wilson R, Walker JJ, McKillop JH, Kennedy JH. Interleukin-2
receptor and ovarian cancer. Br. J. Cancer. 1993; 68(2):364–367.
224. Subramani R, Lopez-Valdez R, Salcido A, Boopalan T, Arumugam A, Nandy S, et al.
155
Growth hormone receptor inhibition decreases the growth and metastasis of pancreatic
ductal adenocarcinoma. Exp. Mol. Med. 2014; 46(10):e117-. doi:10.1038/emm.2014.61.
225. Gavalas NG, Karadimou A, Dimopoulos MA, Bamias A. Immune Response in Ovarian
Cancer: How Is the Immune System Involved in Prognosis and Therapy: Potential for
Treatment Utilization. Clin. Dev. Immunol. 2010; 2010. doi:10.1155/2010/791603.
226. Zheng H, Ruan J, Zhao P, Chen S, Pan L, Liu J. Heparanase is involved in proliferation
and invasion of ovarian cancer cells. Cancer Biomark. 2015; 15(5):525–534.
doi:10.3233/CBM-150459.
227. LoKuan E, Ziegler SF. Thymic Stromal Lymphopoietin (TSLP) and Cancer. J. Immunol.
2014; 193(9):4283–4288. doi:10.4049/jimmunol.1400864.
228. Fleming D, Chahin L, Rumbaugh K. Glycoside Hydrolases Degrade Polymicrobial
Bacterial Biofilms in Wounds. Antimicrob. Agents Chemother. 2017; 61(2).
doi:10.1128/AAC.01998-16.
229. Takasu S, Mutoh M, Takahashi M, Nakagama H. Lipoprotein lipase as a candidate target
for cancer prevention/therapy. Biochem. Res. Int. 2012; 2012:398697.
doi:10.1155/2012/398697.
230. D’Souza B, Sreekantha, D’Souza V. Hyperamylasemia in ovarian tumours – serum
amylase as a marker for ovarian cancers. Int. J. Pharma Bio Sci. 2011; 2(1):B445-449.
231. Sosnoff DR, Friend RB, Berkovic M, Rasansky RJ, Hoffman SMJ. Salivary Amylase–
Producing Multiple Myeloma: Case Report and Review of the Current Literature. J. Clin.
Oncol. [Internet]. 2013; 31(19):e309–e311. doi:10.1200/JCO.2012.46.4677.
232. Elf SE, Chen J. Targeting glucose metabolism in patients with cancer. Cancer. 2014;
120(6):774–780. doi:10.1002/cncr.28501.
156
233. Annibaldi A, Widmann C. Glucose metabolism in cancer cells. Curr. Opin. Clin. Nutr.
Metab. Care. 2010; 13(4):466–470. doi:10.1097/MCO.0b013e32833a5577.
234. Warburg O, Wind F, Negelein E. THE METABOLISM OF TUMORS IN THE BODY. J.
Gen. Physiol. 1927; 8(6):519–530.
235. Porter AE. On the Behaviour of Amylase in the Presence of a Specific Precipitate.
Biochem. J. 1913; 7(6):599–603.
236. Azzopardi E, Lloyd C, Teixeira SR, Conlan RS, Whitaker IS. Clinical applications of
amylase: Novel perspectives. Surgery. 2016; 160(1):26–37.
doi:10.1016/j.surg.2016.01.005.
237. Groot PC, Bleeker MJ, Pronk JC, Arwert F, Mager WH, Planta RJ, et al. The human α-
amylase multigene family consists of haplotypes with variable numbers of genes.
Genomics [Internet]. 1989; 5(1):29–42. doi:http://dx.doi.org/10.1016/0888-
7543(89)90083-9.
238. Gumucio DL, Wiebauer K, Caldwell RM, Samuelson LC, Meisler MH. Concerted
evolution of human amylase genes. Mol Cell Biol. 1988; 8(3):1197–1205.
239. Groot PC, Mager WH, Frants RR, Meisler MH, Samuelson LC. The human amylase-
encoding genes amy2 and amy3 are identical to AMY2A and AMY2B. Gene. 1989;
85(2):567–568.
240. Horii A, Emi M, Tomita N, Nishide T, Ogawa M, Mori T, et al. Primary structure of
human pancreatic alpha-amylase gene: its comparison with human salivary alpha-amylase
gene. Gene. 1987; 60(1):57–64.
241. Groot PC, Bleeker MJ, Pronk JC, Arwert F, Mager WH, Planta RJ, et al. Human
pancreatic amylase is encoded by two different genes. Nucleic Acids Res. 1988;
157
16(10):4724.
242. Meisler MH, Ting CN. The remarkable evolutionary history of the human amylase genes.
Crit. Rev. Oral Biol. Med. 1993; 4(3–4):503–509.
243. Samuelson LC, Wiebauer K, Gumucio DL, Meisler MH. Expression of the human
amylase genes: recent origin of a salivary amylase promoter from an actin pseudogene.
Nucleic Acids Res. 1988; 16(17):8261–8276.
244. Samuelson LC, Wiebauer K, Snow CM, Meisler MH. Retroviral and pseudogene insertion
sites reveal the lineage of human salivary and pancreatic amylase genes from a single gene
during primate evolution. Mol Cell Biol. 1990; 10(6):2513–2520.
245. Koyama I, Komine S, Iino N, Hokari S, Igarashi S, Alpers DH, et al. α-Amylase
expressed in human liver is encoded by the AMY-2B gene identified in tumorous tissues.
Clin. Chim. Acta [Internet]. 2001; 309(1):73–83. doi:http://dx.doi.org/10.1016/S0009-
8981(01)00501-0.
246. Seyama K, Nukiwa T, Takahashi K, Takahashi H, Kira S. Amylase mRNA transcripts in
normal tissues and neoplasms: the implication of different expressions of amylase
isogenes. J. Cancer Res. Clin. Oncol. [Internet]. 1994; 120(4):213–220.
doi:10.1007/BF01372559.
247. Ueda M, Araki T, Shiota T, Taketa K. Age and sex-dependent alterations of serum
amylase and isoamylase levels in normal human adults. J. Gastroenterol. 1994; 29(2):189–
191.
248. Zheng C, Hoffman MP, McMillan T, Kleinman HK, O’Connell BC. Growth factor
regulation of the amylase promoter in a differentiating salivary acinar cell line. J Cell
Physiol. 1998; 177(4):628–635. doi:10.1002/(sici)1097-4652(199812)177:4<628::aid-
158
jcp13>3.0.co;2-l.
249. Jung DW, Hecht D, Ho SW, O’Connell BC, Kleinman HK, Hoffman MP. PKC and
ERK1/2 regulate amylase promoter activity during differentiation of a salivary gland cell
line. J Cell Physiol. 2000; 185(2):215–225. doi:10.1002/1097-
4652(200011)185:2<215::aid-jcp6>3.0.co;2-l.
250. Zheng C, Hoque AT, Braddon VR, Baum BJ, O’Connell BC. Evaluation of salivary gland
acinar and ductal cell-specific promoters in vivo with recombinant adenoviral vectors.
Hum. Gene Ther. 2001; 12(18):2215–2223. doi:10.1089/10430340152710559.
251. Nishide T, Emi M, Nakamura Y, Matsubara K. Corrected sequences of cDNAs for human
salivary and pancreatic alpha-amylases [corrected]. Gene. 1984; 28(2):263–270.
252. Omichi K, Hase S. Detection of human urinary alpha-amylase encoded by the AMY2B
gene using a fluorogenic substrate, FG5P. J Biochem. 1992; 112(3):303–305.
253. Zakowski JJ, Gregory MR, Bruns DE. Amylase from human serous ovarian tumors:
purification and characterization. Clin. Chem. 1984; 30(1):62–68.
254. Butterworth PJ, Warren FJ, Ellis PR. Human α-amylase and starch digestion: An
interesting marriage. Starch - Stärke [Internet]. 2011; 63(7):395–405.
doi:10.1002/star.201000150.
255. Carpenter D, Dhar S, Mitchell LM, Fu B, Tyson J, Shwan NAA, et al. Obesity, starch
digestion and amylase: association between copy number variants at human salivary
(AMY1) and pancreatic (AMY2) amylase genes. Hum. Mol. Genet. 2015; 24(12):3472–
3480. doi:10.1093/hmg/ddv098.
256. Brayer GD, Luo Y, Withers SG. The structure of human pancreatic alpha-amylase at 1.8
A resolution and comparisons with related enzymes. Protein Sci. 1995; 4(9):1730–1742.
159
doi:10.1002/pro.5560040908.
257. Ramasubbu N, Paloth V, Luo Y, Brayer GD, Levine MJ. Structure of human salivary
alpha-amylase at 1.6 A resolution: implications for its role in the oral cavity. Acta
Crystallogr. D. Biol. Crystallogr. 1996; 52(Pt 3):435–446.
doi:10.1107/S0907444995014119.
258. Minobe S, Nakajima H, Itoh N, Funakoshi I, Yamashina I. Structure of a major
oligosaccharide of Taka-amylase A. J. Biochem. 1979; 86(6):1851–1854.
259. Ji C, Wei G. Deglycosylation induces extensive dynamics changes in α-amylase revealed
by hydrogen/deuterium exchange mass spectrometry. Rapid Commun. Mass Spectrom.
[Internet]. 2013; 27(23):2625–2630. doi:10.1002/rcm.6732.
260. Kasperczyk S, Brzoza Z, Kasperczyk A, Beck B, Duiban H, Mertas A. The changes of
alpha-amylase activity in serum and different tissues of female rat during sex cycle--
isoelectrofocusing studies of alpha-amylase. Med. Sci. Monit. 2001; 7(1):49–53.
261. Weiss MJ, Edmondson HA, Wertman M. Elevated serum amylase associated with
bronchogenic carcinoma; report of case. Am J Clin Pathol. 1951; 21(11):1057–1061.
262. Kavitha S, Balasubramanian R. Elderly lady with ascites. J. Assoc. Physicians India.
2006; 54:325–326.
263. Omichi K, Hase S. Identification of the characteristic amino-acid sequence for human
alpha-amylase encoded by the AMY2B gene. Biochim. Biophys. Acta. 1993;
1203(2):224–229.
264. Sinha S, Khan H, Timms PM, Olagbaiye OA. Pancreatic-type hyperamylasemia and
hyperlipasemia secondary to ruptured ovarian cyst: a case report and review of the
literature. J. Emerg. Med. 2010; 38(4):463–466. doi:10.1016/j.jemermed.2008.04.042.
160
265. Yokouchi H, Horii A, Emi M, Tomita N, Doi S, Ogawa M, et al. Cloning and
characterization of a third type of human alpha-amylase gene, AMY2B. Gene. 1990;
90(2):281–286.
266. Flood JG, Schuerch C, Dorazio RC, Bowers GNJ. Marked hyperamylasemia associated
with carcinoma of the lung. Clin. Chem. 1978; 24(7):1207–1212.
267. Tohya T, Shimajiri S, Onoda C, Yoshimura T. Complete remission of ovarian
endometrioid adenocarcinoma associated with hyperamylasemia and liver metastasis
treated by paclitaxel and carboplatin chemotherapy: a case report. Int. J. Gynecol. Cancer
[Internet]. 2004; 14(2):378–380. doi:10.1111/j.1048-891x.2004.014225.x.
268. Sandiford JA, Chiknas SG. Hyperamylasemia and ovarian carcinoma. Clin. Chem. 1979;
25(6):948–950.
269. Kawakita T, Sasaki H, Hoshiba T, Asamoto A, Williamson E. Amylase-producing ovarian
carcinoma: A case report and a retrospective study. Gynecol. Oncol. Case Reports
[Internet]. 2012; 2(3):112–114. doi:10.1016/j.gynor.2012.06.002.
270. Takeuchi T, Fujiki H, Kameya T. Characterization of amylases produced by tumors. Clin.
Chem. 1981; 27(4):556–559.
271. Moriyama T. Sialyl salivary-type amylase associated with ovarian cancer. Clin. Chim.
Acta [Internet]. 2008; 391(1–2):106–111. doi:http://dx.doi.org/10.1016/j.cca.2008.01.025.
272. Shimamura J, Fridhandler L, Berk JE. Unusual isomaylase in cancer-associated
hyperamylasemia. Cancer. 1976; 38(5):2121–2126.
273. Berk JE, Shimamura J, Fridhandler L. Tumor-associated hyperamylasemia. Am J
Gastroenterol. 1977; 68(6):572–577.
274. Warshaw AL, Lee KH. Characteristic alterations of serum isoenzymes of amylase in
161
diseases of liver, pancreas, salivary gland, lung, and genitalia. J. Surg. Res. 1977;
22(4):362–369.
275. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al.
Clustal W and Clustal X version 2.0. Bioinformatics [Internet]. 2007; 23(21):2947–2948.
doi:10.1093/bioinformatics/btm404.
276. Goujon M, McWilliam H, Li W, Valentin F, Squizzato S, Paern J, et al. A new
bioinformatics analysis tools framework at EMBL-EBI. Nucleic Acids Res [Internet].
2010; 38(Web Server issue):W695-9. doi:10.1093/nar/gkq313.
277. McWilliam H, Li W, Uludag M, Squizzato S, Park YM, Buso N, et al. Analysis Tool Web
Services from the EMBL-EBI. Nucleic Acids Res [Internet]. 2013; 41(Web Server
issue):W597-600. doi:10.1093/nar/gkt376.
278. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Gasteiger
E., Hoogland C., Gattiker A., Duvaud S., Wilkins M.R., Appel R.D., Bairoch A. John M.
Walk. Proteomics Protoc. Handbook, Humana Press. 2005; :571–607.
279. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal
peptides from transmembrane regions. Nat Methods. 2011; 8(10):785–786.
doi:10.1038/nmeth.1701.
280. Vlahovicek K, Murvai J, Barta E, Pongor S. The SBASE protein domain library, release
9.0: an online resource for protein domain identification. Nucleic Acids Res [Internet].
2002; 30(1):273–275. doi:10.1093/nar/30.1.273.
281. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz H-R, et al. The Pfam protein
families database. Nucleic Acids Res [Internet]. 2008; 36(Database issue):D281-8.
doi:10.1093/nar/gkm960.
162
282. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the
protein families database. Nucleic Acids Res [Internet]. 2014; 42(D1):D222–D230.
doi:10.1093/nar/gkt1223.
283. Gupta A, Deshpande A, Amburi JK, Sabarinathan R, Senthilkumar R, Sekar K. CSSP
(Consensus Secondary Structure Prediction): a web-based server for structural biologists.
J. Appl. Crystallogr. [Internet]. 2009; 42(2):336–338.
doi:doi:10.1107/S0021889808043847.
284. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure
and function prediction. Nat. Methods. 2015; 12(1):7–8. doi:10.1038/nmeth.3213.
285. Yang J, Zhang Y. I-TASSER server: new development for protein structure and function
predictions. Nucleic Acids Res. 2015; 43(W1):W174-81. doi:10.1093/nar/gkv342.
286. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein
structure and function prediction. Nat. Protoc. 2010; 5(4):725–738.
doi:10.1038/nprot.2010.5.
287. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics.
2008; 9:40. doi:10.1186/1471-2105-9-40.
288. Mészáros B, Simon I, Dosztányi Z. Prediction of Protein Binding Regions in Disordered
Proteins. PLoS Comput. Biol. [Internet]. 2009; 5(5):e1000376.
doi:10.1371/journal.pcbi.1000376.
289. Dosztányi Z, Mészáros B, Simon I. ANCHOR: web server for predicting protein binding
regions in disordered proteins. Bioinformatics [Internet]. 2009; 25(20):2745–2746.
doi:10.1093/bioinformatics/btp518.
290. Kallberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, et al. Template-based protein
163
structure modeling using the RaptorX web server. Nat Protoc. 2012; 7(8):1511–1522.
doi:10.1038/nprot.2012.085.
291. HMMpTM: Improving transmembrane protein topology prediction using phosphorylation
and glycosylation site prediction. Biochim. Biophys. Acta. 2013; 1844:316–322.
doi:10.1016/j.bbapap.2013.11.001.
292. Cunningham F, Amode MR, Barrell D, Beal K, Billis K, Brent S, et al. Ensembl 2015.
Nucleic Acids Res [Internet]. 2015; 43(D1):D662–D669. doi:10.1093/nar/gku1010.
293. Messeguer X, Escudero R, Farre D, Nunez O, Martinez J, Alba MM. PROMO: detection
of known transcription regulatory elements using species-tailored searches.
Bioinformatics. 2002; 18(2):333–334.
294. Farré D, Roset R, Huerta M, Adsuara JE, Roselló L, Albà MM, et al. Identification of
patterns in biological sequences at the ALGGEN server: PROMO and MALGEN. Nucleic
Acids Res [Internet]. 2003; 31(13):3651–3653. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC169011/.
295. Dreos R, Ambrosini G, Cavin Perier R, Bucher P. EPD and EPDnew, high-quality
promoter resources in the next-generation sequencing era. Nucleic Acids Res. 2013;
41(Database issue):D157-64. doi:10.1093/nar/gks1233.
296. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative
Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal
[Internet]. 2013. doi:10.1126/scisignal.2004088.
297. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer
genomics portal: an open platform for exploring multidimensional cancer genomics data.
Cancer Discov. 2012; 2(5):401–404. doi:10.1158/2159-8290.cd-12-0095.
164
298. Doyon Y, Home W, Daull P, LeBel D. Effect of C-domain N-glycosylation and deletion
on rat pancreatic alpha-amylase secretion and activity. Biochem. J. [Internet]. 2002;
362(Pt 1):259–264. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1222384/.
299. SQUIRES BT. Human salivary amylase secretion in relation to diet. J. Physiol. 1953;
119(2–3):153–156.
300. Aghajari N, Feller G, Gerday C, Haser R. Structural basis of alpha-amylase activation by
chloride. Protein Sci. 2002; 11(6):1435–1441. doi:10.1110/ps.0202602.
301. Morris C, Fichtel S, Taylor A. Impact of Calcium on Salivary α-Amylase Activity, Starch
Paste Apparent Viscosity, and Thickness Perception. Chemosens. Percept. [Internet].
2011; 4(3):116–122. doi:10.1007/s12078-011-9091-7.
302. Zabel BU, Naylor SL, Sakaguchi AY, Bell GI, Shows TB. High-resolution chromosomal
localization of human genes for amylase, proopiomelanocortin, somatostatin, and a DNA
fragment (D3S1) by in situ hybridization. Proc Natl Acad Sci U S A. 1983; 80(22):6932–
6936.
303. Tricoli J V, Shows TB. Regional assignment of human amylase (AMY) to p22----p21 of
chromosome 1. Somat. Cell Mol. Genet. 1984; 10(2):205–210.
304. Munke M, Lindgren V, de Martinville B, Francke U. Comparative analysis of mouse-
human hybrids with rearranged chromosomes 1 by in situ hybridization and Southern
blotting: high-resolution mapping of NRAS, NGFB, and AMY on human chromosome 1.
Somat. Cell Mol. Genet. 1984; 10(6):589–599.
305. Levitzki A, Steer ML. The allosteric activation of mammalian alpha-amylase by chloride.
Eur. J. Biochem. 1974; 41(1):171–180.
165
306. Lifshitz R, Levitzki A. Identity and properties of the chloride effector binding site in hog
pancreatic alpha-amylase. Biochemistry. 1976; 15(9):1987–1993.
307. MacGregor EA, Janecek S, Svensson B. Relationship of sequence and structure to
specificity in the alpha-amylase family of enzymes. Biochim. Biophys. Acta. 2001;
1546(1):1–20.
308. Brayer GD, Luo Y, Withers SG. The structure of human pancreatic α-amylase at 1.8 Å
resolution and comparisons with related enzymes. Protein Sci. [Internet]. 1995;
4(9):1730–1742. doi:10.1002/pro.5560040908.
309. Parker CE, Mocanu V, Mocanu M et al. Mass Spectrometry for Post-Translational
Modifications [Internet]. In: O A, editor. Neuroproteomics. Boca Raton (FL): CRC
Press/Taylor & Francis; 2010. Available from:
http://www.ncbi.nlm.nih.gov/books/NBK56012/.
310. Lodish H, Berk A, Zipursky SL et al. Section 17.7Protein Glycosylation in the ER and
Golgi Complex [Internet]. In: Molecular Cell Biology. New York: W. H. Freeman and
Company.; 2000. p. Section 17.7. Available from:
http://www.ncbi.nlm.nih.gov/books/NBK21744/.
311. Rakus JF, Mahal LK. New technologies for glycomic analysis: toward a systematic
understanding of the glycome. Annu. Rev. Anal. Chem. (Palo Alto. Calif). 2011; 4:367–
392. doi:10.1146/annurev-anchem-061010-113951.
312. Varki A, Lowe JB. Biological Roles of Glycans. In: Varki A, Cummings RD, Esko JD,
Freeze HH, Stanley P, Bertozzi CR, et al., editors. . Cold Spring Harbor (NY): 2009.
313. Xue J, Zhao Q, Zhu L, Zhang W. Deglycosylation of FcalphaR at N58 increases its
binding to IgA. Glycobiology. 2010; 20(7):905–915. doi:10.1093/glycob/cwq048.
166
314. Mendez JD, Xie J, Aguilar-Hernandez M, Mendez-Valenzuela V. Trends in advanced
glycation end products research in diabetes mellitus and its complications. Mol. Cell.
Biochem. 2010; 341(1–2):33–41. doi:10.1007/s11010-010-0434-5.
315. Cohen P. The origins of protein phosphorylation. Nat Cell Biol [Internet]. 2002;
4(5):E127–E130. Available from: http://dx.doi.org/10.1038/ncb0502-e127.
316. Verger A, Perdomo J, Crossley M. Modification with SUMO: A role in transcriptional
regulation. EMBO Rep. 2003; 4(2):137–142. doi:10.1038/sj.embor.embor738.
317. van der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, et al.
Classification of intrinsically disordered regions and proteins. Chem. Rev. 2014;
114(13):6589–6631. doi:10.1021/cr400525m.
318. Liu H-L, Chen W-J, Chou S-N. Mechanisms of aggregation of alpha- and beta-amylases
in aqueous dispersions. Colloids Surfaces B Biointerfaces [Internet]. 2003; 28(2–3):215–
225. doi:http://dx.doi.org/10.1016/S0927-7765(02)00142-X.
319. Branden C, Tooze J. Introduction to Protein Structure. 2nd ed. Garland Science; 1999.
320. Rambaran RN, Serpell LC. Amyloid fibrils: Abnormal protein assembly. Prion [Internet].
2008; 2(3):112–117. Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2634529/.
321. Pieper-Bigelow C, Strocchi A, Levitt MD. Where does serum amylase come from and
where does it go? Gastroenterol Clin North Am. 1990; 19(4):793–810.
322. Kazmierczak SC, Van Lente F, McHugh AM, Katzin WE. Macroamylasemia with a
markedly increased amylase clearance ratio in a patient with renal cell carcinoma. Clin.
Chem. 1988; 34(2):435–438.
323. Warshaw AL, Lee KH. Macroamylasemia and other chronic nonspecific
167
hyperamylasemias: chemical oddities or clinical entities? Am J Surg. 1978; 135(4):488–
493.
324. Otsuki M, Yuu H, Maeda M, Saeki S, Yamasaki T. Amylase in the lung. Cancer. 1977;
39(4):1656–1663.
325. Hayashi Y, Fukayama M, Koike M, Nakayama T. Amylase in human lungs and the
female genital tract. Histochemical and immunohistochemical localization.
Histochemistry. 1986; 85(6):491–496.
326. Koschmieder S, Halmos B, Levantini E, Tenen DG. Dysregulation of the C/EBPα
Differentiation Pathway in Human Cancer. J. Clin. Oncol. [Internet]. 2009; 27(4):619–
628. doi:10.1200/JCO.2008.17.9812.
327. Sundfeldt K, Ivarsson K, Carlsson M, Enerbäck S, Janson PO, Brännström M, et al. The
expression of CCAAT/enhancer binding protein (C/EBP) in the human ovary in vivo:
specific increase in C/EBPβ during epithelial tumour progression. Br. J. Cancer [Internet].
1999; 79(7–8):1240–1248. doi:10.1038/sj.bjc.6690199.
328. Gan L, Chen S, Zhong J, Wang X, Lam EKY, Liu X, et al. ZIC1 is downregulated through
promoter hypermethylation, and functions as a tumor suppressor gene in colorectal cancer.
PLoS One. 2011; 6(2):e16916. doi:10.1371/journal.pone.0016916.
329. Peck AR, Witkiewicz AK, Liu C, Klimowicz AC, Stringer GA, Pequignot E, et al. Low
levels of Stat5a protein in breast cancer are associated with tumor progression and
unfavorable clinical outcomes. Breast Cancer Res. 2012; 14(5):R130.
doi:10.1186/bcr3328.
330. Yang C, Bolotin E, Jiang T, Sladek FM, Martinez E. Prevalence of the Initiator over the
TATA box in human and yeast genes and identification of DNA motifs enriched in human
168
TATA-less core promoters. Gene. 2007; 389(1):52–65. doi:10.1016/j.gene.2006.09.029.
331. Klug WS, Cummings MR, Spencer CA, Palladina M. Concepts of Genetics. Ninth. San
Francisco: Pearson Benjamin Cummings; 2009.
332. Canettieri G, Santaguida MG, Antonucci L, Della Guardia M, Franchi A, Coni S, et al.
CCAAT/Enhancer-Binding Proteins Are Key Regulators of Human Type Two Deiodinase
Expression in a Placenta Cell Line. Endocrinology [Internet]. 2012; 153(8):4030–4038.
doi:10.1210/en.2011-2113.
333. Ramji DP, Foka P. CCAAT/enhancer-binding proteins: structure, function and regulation.
Biochem. J. 2002; 365(Pt 3):561–575. doi:10.1042/BJ20020508.
334. Cammack, Richard; Atwood, Teresa; Campbell, Peter; Parish, Howard; Smith, Anthony;
Vella, Frank; Stirling J, editor. Oxford Dictionary of Biochemistry and Molecular
Biology. 2nd ed. Oxford University Press.; 2006.
335. Mantovani R. The molecular biology of the CCAAT-binding factor NF-Y. Gene. 1999;
239(1):15–27.
336. Juang CM, Yeng MS, Twu NF, Chao GC. Hyperamylasemia associated with endometroid
carcinoma of the ovary. Zhonghua Yi Xue Za Zhi (Taipei). 2000; 63(9):710–713.
337. Schlikker I, Nakad A, Gerbaux A, Azzouzi K, Kadou J, Lezaire P, et al. Hyperamylasemia
with papillary serous cystadenocarcinoma of the ovary. Acta Clin. Belg. 1989; 44(4):255–
258.
338. Jacobs E, Jennette JC, Reavis RA. Chronic hyperamylasemia and chronic pelvic
inflammatory disease. Clin. Chem. 1983; 29(5):887–888.
339. Zakrzewska I, Pietrynczak M. The activity of alpha-amylase and its salivary isoenzymes
in serum and urine of patients with neoplastic diseases of female reproductive organs.
169
Rocz. Akad. Med. Bialymst. 1996; 41(2):492–498.
340. Hayakawa T, Kameya A, Mizuno R, Noda A, Kondo T, Hirabayashi N. Hyperamylasemia
with papillary serous cystadenocarcinoma of the ovary. Cancer. 1984; 54(8):1662–1665.
341. Hodes ME, Sisk CJ, Karn RC, Ehrlich CE, Lehrner LM, Roth LM, et al. An amylase-
producing serous cystadenocarcinoma of the ovary. Oncology. 1985; 42(4):242–247.
342. Lehrner LM, Ward JC, Karn RC, Ehrlich CE, Merritt D. An evaluation of the usefulness
of amylase isozyme differentiation in patients with hyperamylasemia. Am. J. Clin. Pathol.
1976; 66(3):576–587.
343. O’Riordan T, Gaffney E, Tormey V, Daly P. Hyperamylasemia associated with
progression of a serous surface papillary carcinoma. Gynecol. Oncol. 1990; 36(3):432–
434.
344. Kruk PA, Maines-Bandiera SL, Auersperg N. A simplified method to culture human
ovarian surface epithelium. Lab. Invest. 1990; 63(1):132–136.
345. Maines-Bandiera SL, Kruk PA, Auersperg N. Simian virus 40-transformed human ovarian
surface epithelial cells escape normal growth controls but retain morphogenetic responses
to extracellular matrix. Am. J. Obstet. Gynecol. 1992; 167(3):729–735.
346. Hanson WG, Ferguson PJ. Differential methotrexate toxicity between two human oral
squamous carcinoma cell lines. J Otolaryngol. 1993; 22(3):143–147.
347. Leuchs EF. Wirkung des Speichels auf Stärke" (Effect of saliva on starch). Ann. der Phys.
und Chemie. 1831; 98(8):623.
348. Danilewsky. “Über specifisch wirkende Körper des natürlichen und künstlichen
pancreatischen Saftes” (On the specifically-acting principles of the natural and artificial
pancreatic juice). Virchows Arch. für Pathol. Anat. und Physiol. und für Klin. Medizin.
170
1862; 25:279–307.
349. Zhang J, Zhang L, Pan S, Gu B, Zhen Y, Yan J, et al. Amylase: sensitive tumor marker for
amylase-producing lung adenocarcinoma. J. Thorac. Dis. 2013; 5(4):E167-9.
doi:10.3978/j.issn.2072-1439.2013.08.37.
350. Kang JU, Koo SH, Kwon KC, Park JW. AMY2A: a possible tumor-suppressor gene of
1p21.1 loss in gastric carcinoma. Int. J. Oncol. 2010; 36(6):1429–1435.
351. Barua A, Bitterman P, Abramowicz JS, Dirks AL, Bahr JM, Hales DB, et al.
Histopathology of ovarian tumors in laying hens: a preclinical model of human ovarian
cancer. Int. J. Gynecol. Cancer. 2009; 19(4):531–539.
doi:10.1111/IGC.0b013e3181a41613.
352. Bai W, Oliveros-Saunders B, Wang Q, Acevedo-Duncan ME, Nicosia S V. Estrogen
stimulation of ovarian surface epithelial cell proliferation. In Vitro Cell. Dev. Biol. Anim.
2000; 36(10):657–666. doi:10.1290/1071-2690(2000)036<0657:ESOOSE>2.0.CO;2.
353. Bobbs AS, Cole JM, Cowden Dahl KD. Emerging and Evolving Ovarian Cancer Animal
Models. Cancer Growth Metastasis. 2015; 8(Suppl 1):29–36. doi:10.4137/CGM.S21221.
354. Auersperg N, Maclaren IA, Kruk PA. Ovarian surface epithelium: autonomous production
of connective tissue-type extracellular matrix. Biol. Reprod. 1991; 44(4):717–724.
355. Kruk PA, Auersperg N. Human ovarian surface epithelial cells are capable of physically
restructuring extracellular matrix. Am. J. Obstet. Gynecol. 1992; 167(5):1437–1443.
356. Fuster MM, Esko JD. The sweet and sour of cancer: glycans as novel therapeutic targets.
Nat Rev Cancer. 2005; 5(7):526–542. doi:10.1038/nrc1649.
357. Lange T, Samatov TR, Tonevitsky AG, Schumacher U. Importance of altered
glycoprotein-bound N- and O-glycans for epithelial-to-mesenchymal transition and
171
adhesion of cancer cells. Carbohydr Res. 2014; 389:39–45.
doi:10.1016/j.carres.2014.01.010.
358. Mitchell MJ, King MR. Physical Biology in Cancer. 3. The role of cell glycocalyx in
vascular transport of circulating tumor cells [Internet]. 2014.
doi:10.1152/ajpcell.00285.2013.
359. Krahling H, Mally S, Eble JA, Noel J, Schwab A, Stock C. The glycocalyx maintains a
cell surface pH nanoenvironment crucial for integrin-mediated migration of human
melanoma cells. Pflugers Arch. 2009; 458(6):1069–1083. doi:10.1007/s00424-009-0694-
7.
360. Dobrossy L, Pavelic ZP, Bernacki RJ. A correlation between cell surface sialyltransferase,
sialic acid, and glycosidase activities and the implantability of B16 murine melanoma.
Cancer Res. 1981; 41(6):2262–2266.
361. Terajima M, Perdivara I, Sricholpech M, Deguchi Y, Pleshko N, Tomer KB, et al.
Glycosylation and cross-linking in bone type I collagen. J. Biol. Chem. 2014;
289(33):22636–22647. doi:10.1074/jbc.M113.528513.
362. Quintarelli G, Dellovo MC, Balduini C, Castellani AA. The effects of alpha amylase on
collagen-proteoglycans and collagen-glycoprotein complexes in connective tissue
matrices. Histochemie [Internet]. 1969; 18(4):373–375. doi:10.1007/BF00279887.
363. Balfour JA, McTavish D. Acarbose. An update of its pharmacology and therapeutic use in
diabetes mellitus. Drugs. 1993; 46(6):1025–1054.
364. Deng R, Chow T-J. Hypolipidemic, antioxidant, and antiinflammatory activities of
microalgae Spirulina. Cardiovasc. Ther. 2010; 28(4):e33-45. doi:10.1111/j.1755-
5922.2010.00200.x.
172
365. Selmi C, Leung PSC, Fischer L, German B, Yang C-Y, Kenny TP, et al. The effects of
Spirulina on anemia and immune function in senior citizens. Cell. Mol. Immunol. 2011;
8(3):248–254. doi:10.1038/cmi.2010.76.
366. Gemma C, Mesches MH, Sepesi B, Choo K, Holmes DB, Bickford PC. Diets enriched in
foods with high antioxidant activity reverse age-induced decreases in cerebellar beta-
adrenergic function and increases pro-inflammatory cytokines. J. Neurosci. 2002;
22(14):6114–6120.
367. Stromberg I, Gemma C, Vila J, Bickford PC. Blueberry and Spirulina-enriched diets
enhance striatal dopamine recovery and induce a rapid, transient microglia activation after
injury of the rat nigrostriatal dopamine system. J. Exp. Neurol. 2005; 196(2):298–307.
368. Wang Y, Chang CF, Chou J, Chen HL, Deng X, Harvey BK, et al. Dietary
supplementation with blueberries, spinach, or spirulina reduces ischemic brain damage.
Exp Neurol [Internet]. 2005; 193(1):75–84. doi:10.1016/j.expneurol.2004.12.014.
369. Lupatini AL, Colla LM, Canan C, Colla E. Potential application of microalga Spirulina
platensis as a protein source. J. Sci. Food Agric. 2017; 97(3):724–732.
doi:10.1002/jsfa.7987.
370. Gutierrez-Salmean G, Fabila-Castillo L, Chamorro-Cevallos G. Nutritional and
Toxicological Aspects of Spirulina (Arthrospira). Nutr. Hosp. 2015; 32(1):34–40.
doi:10.3305/nh.2015.32.1.9001.
371. Romay C, Gonzalez R, Ledon N, Remirez D, Rimbau V. C-phycocyanin: a biliprotein
with antioxidant, anti-inflammatory and neuroprotective effects. Curr. Protein Pept. Sci.
2003; 4(3):207–216.
372. Chen Q, Gao Q, Chen K, Wang Y, Chen L, Li XU. Curcumin suppresses migration and
173
invasion of human endometrial carcinoma cells. Oncol. Lett. [Internet]. 2015; 10(3):1297–
1302. doi:10.3892/ol.2015.3478.
373. Gupta SC, Kim JH, Prasad S, Aggarwal BB. Regulation of survival, proliferation,
invasion, angiogenesis, and metastasis of tumor cells through modulation of inflammatory
pathways by nutraceuticals. Cancer Metastasis Rev. [Internet]. 2010; 29(3):405–434.
doi:10.1007/s10555-010-9235-2.
374. Torres-Duran P V, Ferreira-Hermosillo A, Juarez-Oropeza MA. Antihyperlipemic and
antihypertensive effects of Spirulina maxima in an open sample of Mexican population: a
preliminary report. Lipids Health Dis. 2007; 6:33. doi:10.1186/1476-511X-6-33.
375. Parikh P, Mani U, Iyer U. Role of Spirulina in the Control of Glycemia and Lipidemia in
Type 2 Diabetes Mellitus. J. Med. Food. 2001; 4(4):193–199.
doi:10.1089/10966200152744463.
376. Samuels R, Mani U V, Iyer UM, Nayak US. Hypocholesterolemic effect of spirulina in
patients with hyperlipidemic nephrotic syndrome. J. Med. Food. 2002; 5(2):91–96.
doi:10.1089/109662002760178177.
377. Ramamoorthy A, Premakumari S. Effect of supplementation of Spirulina on
hypercholesterolemic patients. Food Sci Technol. 1996; 33:124–128.
378. Khan M, Shobha JC, Mohan IK, Rao Naidu MU, Prayag A, Kutala VK. Spirulina
attenuates cyclosporine-induced nephrotoxicity in rats. J. Appl. Toxicol. 2006; 26(5):444–
451. doi:10.1002/jat.1159.
379. Germán Chamorro, Mónica Pérez-Albiter, Norma Serrano-García, José J. Mares-Sámano
PR. Spirulina maxima pretreatment partially protects against 1-methyl-4-phenyl-1,2,3,6-
tetrahydropyridine neurotoxicity. Nutr. Neurosci. [Internet]. 2006; 9(5–6):207–212.
174
doi:10.1080/10284150600929748.
380. Romay C, Delgado R, Remirez D, Gonzalez R, Rojas A. Effects of phycocyanin extract
on tumor necrosis factor-alpha and nitrite levels in serum of mice treated with endotoxin.
Arzneimittelforschung. 2001; 51(9):733–736. doi:10.1055/s-0031-1300107.
381. Bai S-K, Lee S-J, Na H-J, Ha K-S, Han J-A, Lee H, et al. beta-Carotene inhibits
inflammatory gene expression in lipopolysaccharide-stimulated macrophages by
suppressing redox-based NF-kappaB activation. Exp. Mol. Med. 2005; 37(4):323–334.
doi:10.1038/emm.2005.42.
382. Konickova R, Vankova K, Vanikova J, Vanova K, Muchova L, Subhanova I, et al. Anti-
cancer effects of blue-green alga Spirulina platensis, a natural source of bilirubin-like
tetrapyrrolic compounds. Ann. Hepatol. 2014; 13(2):273–283.
383. Yogianti F, Kunisada M, Nakano E, Ono R, Sakumi K, Oka S, et al. Inhibitory effects of
dietary Spirulina platensis on UVB-induced skin inflammatory responses and
carcinogenesis. J. Invest. Dermatol. 2014; 134(10):2610–2619. doi:10.1038/jid.2014.188.
384. Wang Z, Zhang X. Inhibitory effects of small molecular peptides from Spirulina
(Arthrospira) platensis on cancer cell growth. Food Funct. 2016; 7(2):781–788.
doi:10.1039/c5fo01186h.
385. Smieszek A, Giezek E, Chrapiec M, Murat M, Mucha A, Michalak I, et al. The Influence
of Spirulina platensis Filtrates on Caco-2 Proliferative Activity and Expression of
Apoptosis-Related microRNAs and mRNA. Mar. Drugs. 2017; 15(3).
doi:10.3390/md15030065.
386. Pan R, Lu R, Zhang Y, Zhu M, Zhu W, Yang R, et al. Spirulina phycocyanin induces
differential protein expression and apoptosis in SKOV-3 cells. Int. J. Biol. Macromol.
175
2015; 81:951–959. doi:10.1016/j.ijbiomac.2015.09.039.
387. Ying J, Wang J, Ji H, Lin C, Pan R, Zhou L, et al. Transcriptome analysis of phycocyanin
inhibitory effects on SKOV-3 cell proliferation. Gene. 2016; 585(1):58–64.
doi:10.1016/j.gene.2016.03.023.
388. Zhang B, Cai FF, Zhong XY. An overview of biomarkers for the ovarian cancer diagnosis.
Eur. J. Obstet. Gynecol. Reprod. Biol. 2011; 158(2):119–123.
doi:10.1016/j.ejogrb.2011.04.023.
389. Kufe DW. Mucins in cancer: function, prognosis and therapy. Nat. Rev. Cancer. 2009;
9(12):874–885. doi:10.1038/nrc2761.
390. Paszek MJ, DuFort CC, Rossier O, Bainer R, Mouw JK, Godula K, et al. The cancer
glycocalyx mechanically primes integrin-mediated growth and survival. Nature. 2014;
511(7509):319–325. doi:10.1038/nature13535.
176
Appendix I
Potential novel regulators or biomarkers of OC – 683 proteins in total of non-redundant,
secreted, ordered and aggregation-prone human proteins.
Appendix I
Uniprot
entry
Uniprot
entry
name
Gene name Protein name
Proteins functionally related to ECM/microenvironment modifications
A8K2U0 A2ML1 A2ML1 CPAMD9
Q9Y653 AGRG1 ADGRG1 GPR56 TM7LN4
TM7XN1 UNQ540/PRO1083
P01019 ANGT AGT SERPINA8 Angiotensinogen (Serpin A8)
[Cleaved into: Angiotensin-1
(Angiotensin 1-10) (Angiotensin I)
(Ang I); Angiotensin-2
(Angiotensin 1-8) (Angiotensin II)
(Ang II); Angiotensin-3
(Angiotensin 2-8) (Angiotensin
III) (Ang III) (Des-Asp[1]-
angiotensin II); Angiotensin-4
(Angiotensin 3-8) (Angiotensin
IV) (Ang IV); Angiotensin 1-9;
Angiotensin 1-7; Angiotensin 1-5;
Angiotensin 1-4]
Q13740 CD166 ALCAM MEMD CD166 antigen (Activated
leukocyte cell adhesion molecule)
(CD antigen CD166)
S4R3Y4 S4R3Y4 AMBP Protein AMBP
Q15389 ANGP1 ANGPT1 KIAA0003 Angiopoietin-1 (ANG-1)
O15123 ANGP2 ANGPT2 Angiopoietin-2 (ANG-2)
O95841 ANGL1 ANGPTL1 ANG3 ANGPT3
ARP1 PSEC0154
UNQ162/PRO188
Angiopoietin-related protein 1
(Angiopoietin-3) (ANG-3)
(Angiopoietin-like protein 1)
Q9UKU9 ANGL2 ANGPTL2 ARP2
UNQ170/PRO196
Angiopoietin-related protein 2
(Angiopoietin-like protein 2)
177
P58335 ANTR2 ANTXR2 CMG2 Anthrax toxin receptor 2
(Capillary morphogenesis gene 2
protein) (CMG-2)
O95393 BMP10 BMP10 Bone morphogenetic protein 10
(BMP-10)
P22003 BMP5 BMP5 Bone morphogenetic protein 5
(BMP-5)
P22004 BMP6 BMP6 VGR Bone morphogenetic protein 6
(BMP-6) (VG-1-related protein)
(VG-1-R) (VGR-1)
P18075 BMP7 BMP7 OP1 Bone morphogenetic protein 7
(BMP-7) (Osteogenic protein 1)
(OP-1) (Eptotermin alfa)
P59826 BPIB3 BPIFB3 C20orf185 LPLUNC3 BPI fold-containing family B
member 3 (Ligand-binding protein
RYA3) (Long palate, lung and
nasal epithelium carcinoma-
associated protein 3)
Q075Z2 BSPH1 BSPH1 Binder of sperm protein homolog
1 (Bovine seminal plasma protein
homolog 1) (Bovine seminal
plasma protein-like 1)
P35070 BTC BTC Probetacellulin [Cleaved into:
Betacellulin (BTC)]
Q9BXJ1 C1QT1 C1QTNF1 CTRP1
UNQ310/PRO353
Complement C1q tumor necrosis
factor-related protein 1 (G protein-
coupled receptor-interacting
protein) (GIP)
Q9BXJ0 C1QT5 C1QTNF5 CTRP5
UNQ303/PRO344
Complement C1q tumor necrosis
factor-related protein 5
P60827 C1QT8 C1QTNF8
UNQ5829/PRO19648
Complement C1q tumor necrosis
factor-related protein 8 (C1q/TNF-
related protein 8) (CTRP8)
Q8IUK8 CBLN2 CBLN2 UNQ1892/PRO4338 Cerebellin-2
P48960 CD97 CD97
Q9H5V8 CDCP1 CDCP1 TRASK
UNQ2486/PRO5773
P13688 CEAM1 CEACAM1 BGP BGP1 Carcinoembryonic antigen-related
cell adhesion molecule 1 (Biliary
glycoprotein 1) (BGP-1) (CD
antigen CD66a)
Q2WEN9 CEA16 CEACAM16 CEAL2 Carcinoembryonic antigen-related
cell adhesion molecule 16
(Carcinoembryonic antigen-like 2)
178
Q9UNI1 CELA1 CELA1 ELA1 Chymotrypsin-like elastase family
member 1 (EC 3.4.21.36)
(Elastase-1) (Pancreatic elastase 1)
P08217 CEL2A CELA2A ELA2A Chymotrypsin-like elastase family
member 2A (EC 3.4.21.71)
(Elastase-2A)
Q9BXR6 FHR5 CFHR5 CFHL5 FHR5 Complement factor H-related
protein 5 (FHR-5)
Q15782 CH3L2 CHI3L2 Chitinase-3-like protein 2
(Chondrocyte protein 39) (YKL-
39)
Q9BU40 CRDL1 CHRDL1 NRLN1 Chordin-like protein 1 (Neuralin-
1) (Neurogenesin-1) (Ventroptin)
Q9UQC9 CLCA2 CLCA2 CACC3
O43405 COCH COCH COCH5B2
UNQ257/PRO294
Cochlin (COCH-5B2)
P12109 CO6A1 COL6A1
P12111 CO6A3 COL6A3
A8TX70 CO6A5 COL6A5 COL29A1 VWA4
A6NMZ7 CO6A6 COL6A6
Q8IVL8 CBPO CPO Carboxypeptidase O (CPO) (EC
3.4.17.-)
O75629 CREG1 CREG1 CREG
UNQ727/PRO1409
Protein CREG1 (Cellular repressor
of E1A-stimulated genes 1)
Q9H0B8 CRLD2 CRISPLD2 CRISP11 LCRISP2
UNQ2914/PRO1156/PRO9783
Cysteine-rich secretory protein
LCCL domain-containing 2
(Cysteine-rich secretory protein
11) (CRISP-11) (LCCL domain-
containing cysteine-rich secretory
protein 2)
O60676 CST8 CST8 CRES Cystatin-8 (Cystatin-related
epididymal spermatogenic protein)
Q9H4G1 CST9L CST9L CTES7B
UNQ1835/PRO3543
Cystatin-9 (Cystatin-like
molecule)
Q8N907 DAND5 DAND5 CER2 CKTSF1B3
GREM3 SP1
DAN domain family member 5
(Cerberus-like protein 2) (Cerl-2)
(Cysteine knot superfamily 1,
BMP antagonist 3) (Gremlin-3)
Q07507 DERM DPT Dermatopontin (Tyrosine-rich
acidic matrix protein) (TRAMP)
O60469 DSCAM DSCAM
Q14507 EP3A EDDM3A FAM12A HE3A Epididymal secretory protein E3-
alpha (Human epididymis-specific
protein 3-alpha) (HE3-alpha)
179
P56851 EP3B EDDM3B FAM12B HE3B
UNQ6412/PRO21187
Epididymal secretory protein E3-
beta (Human epididymis-specific
protein 3-beta) (HE3-beta)
O43854 EDIL3 EDIL3 DEL1 EGF-like repeat and discoidin I-
like domain-containing protein 3
(Developmentally-regulated
endothelial cell locus 1 protein)
(Integrin-binding protein DEL1)
Q12805 FBLN3 EFEMP1 FBLN3 FBNL EGF-containing fibulin-like
extracellular matrix protein 1
(Extracellular protein S1-5)
(Fibrillin-like protein) (Fibulin-3)
(FIBL-3)
O95967 FBLN4 EFEMP2 FBLN4
UNQ200/PRO226
EGF-containing fibulin-like
extracellular matrix protein 2
(Fibulin-4) (FIBL-4) (Protein
UPH1)
P20827 EFNA1 EFNA1 EPLG1 LERK1
TNFAIP4
Ephrin-A1 (EPH-related receptor
tyrosine kinase ligand 1) (LERK-
1) (Immediate early response
protein B61) (Tumor necrosis
factor alpha-induced protein 4)
(TNF alpha-induced protein 4)
[Cleaved into: Ephrin-A1, secreted
form]
P52798 EFNA4 EFNA4 EPLG4 LERK4 Ephrin-A4 (EPH-related receptor
tyrosine kinase ligand 4) (LERK-
4)
Q96BH3 ESPB1 ELSPBP1 E12
Q9UJA9 ENPP5 ENPP5 UNQ550/PRO1107 Ectonucleotide
pyrophosphatase/phosphodiesteras
e family member 5 (E-NPP 5)
(NPP-5) (EC 3.1.-.-)
Q6UW88 EPGN EPGN UNQ3072/PRO9904 Epigen (Epithelial mitogen) (EPG)
M5A8F1 SUPYN ERVH48-1 C21orf105 HERV-
Fb1 NDUFV3-AS1
Suppressyn (Endogenous
retrovirus group 48 member 1)
(NDUFV3 antisense RNA 1)
(endogenous retrovirus group Fb
member 1)
Q5T1H1 EYS EYS C6orf178 C6orf179 C6orf180 EGFL10 EGFL11 SPAM
UNQ9424/PRO34591
P03951 FA11 F11 Coagulation factor XI (FXI) (EC
3.4.21.27) (Plasma thromboplastin
antecedent) (PTA) [Cleaved into:
Coagulation factor XIa heavy
180
chain; Coagulation factor XIa light
chain]
P13726 TF F3 Tissue factor (TF) (Coagulation
factor III) (Thromboplastin) (CD
antigen CD142)
P08709 FA7 F7 Coagulation factor VII
P00740 FA9 F9 Coagulation factor IX (EC
3.4.21.22) (Christmas factor)
(Plasma thromboplastin
component) (PTC) [Cleaved into:
Coagulation factor IXa light chain;
Coagulation factor IXa heavy
chain]
Q96MK3 FA20A FAM20A UNQ9388/PRO34279 Pseudokinase FAM20A
Q8IXL6 FA20C FAM20C DMP4 Extracellular serine/threonine
protein kinase FAM20C (EC
2.7.11.1) (Dentin matrix protein 4)
(DMP-4) (Golgi casein kinase)
(Golgi-enriched fraction casein
kinase) (GEF-CK)
Q9Y6R7 FCGBP FCGBP
P08620 FGF4 FGF4 HST HSTF1 KS3 Fibroblast growth factor 4 (FGF-
4) (Heparin secretory-
transforming protein 1) (HST)
(HST-1) (HSTF-1) (Heparin-
binding growth factor 4) (HBGF-
4) (Transforming protein KS3)
P10767 FGF6 FGF6 HST2 HSTF2 Fibroblast growth factor 6 (FGF-
6) (Heparin secretory-
transforming protein 2) (HST-2)
(HSTF-2) (Heparin-binding
growth factor 6) (HBGF-6)
P21781 FGF7 FGF7 KGF Fibroblast growth factor 7 (FGF-
7) (Heparin-binding growth factor
7) (HBGF-7) (Keratinocyte
growth factor)
P02679 FIBG FGG PRO2061 Fibrinogen gamma chain
Q9NZU1 FLRT1 FLRT1 UNQ752/PRO1483 Leucine-rich repeat
transmembrane protein FLRT1
(Fibronectin-like domain-
containing leucine-rich
transmembrane protein 1)
O43155 FLRT2 FLRT2 KIAA0405
UNQ232/PRO265
Leucine-rich repeat
transmembrane protein FLRT2
(Fibronectin-like domain-
181
containing leucine-rich
transmembrane protein 2)
Q9NZU0 FLRT3 FLRT3 KIAA1469
UNQ856/PRO1865
Leucine-rich repeat
transmembrane protein FLRT3
(Fibronectin-like domain-
containing leucine-rich
transmembrane protein 3)
P0C091 FREM3 FREM3
P01225 FSHB FSHB Follitropin subunit beta (Follicle-
stimulating hormone beta subunit)
(FSH-B) (FSH-beta) (Follitropin
beta chain)
Q9BTY2 FUCO2 FUCA2 PSEC0151
UNQ227/PRO260
Plasma alpha-L-fucosidase (EC
3.2.1.51) (Alpha-L-fucoside
fucohydrolase 2) (Alpha-L-
fucosidase 2)
Q9UBS5 GABR1 GABBR1 GPRC3A
Q9NR23 GDF3 GDF3 UNQ222/PRO248 Growth/differentiation factor 3
(GDF-3)
O60383 GDF9 GDF9 Growth/differentiation factor 9
(GDF-9)
Q3B7J2 GFOD2 GFOD2 UNQ9430/PRO34691 Glucose-fructose oxidoreductase
domain-containing protein 2 (EC
1.-.-.-)
P10912 GHR GHR Growth hormone receptor (GH
receptor) (Somatotropin receptor)
[Cleaved into: Growth hormone-
binding protein (GH-binding
protein) (GHBP) (Serum-binding
protein)]
Q86XP6 GKN2 GKN2 BLOT GDDR TFIZ1
UNQ465/PRO813
Gastrokine-2 (Blottin) (Down-
regulated in gastric cancer)
(Trefoil factor interactions(z) 1)
P0CG01 GKN3 GKN3P Gastrokine-3
Q6UWM
5
GPRL1 GLIPR1L1 UNQ2972/PRO7434 GLIPR1-like protein 1
P01148 GON1 GNRH1 GNRH GRH LHRH Progonadoliberin-1
(Progonadoliberin I) [Cleaved
into: Gonadoliberin-1
(Gonadoliberin I) (Gonadorelin)
(Gonadotropin-releasing hormone
I) (GnRH-I) (Luliberin I)
(Luteinizing hormone-releasing
hormone I) (LH-RH I); GnRH-
associated peptide 1 (GnRH-
associated peptide I)]
182
P51654 GPC3 GPC3 OCI5 Glypican-3 (GTR2-2) (Intestinal
protein OCI-5) (MXR7) [Cleaved
into: Secreted glypican-3]
O75487 GPC4 GPC4 UNQ474/PRO937 Glypican-4 (K-glypican) [Cleaved
into: Secreted glypican-4]
P78333 GPC5 GPC5 Glypican-5 [Cleaved into:
Secreted glypican-5]
Q9Y625 GPC6 GPC6 UNQ369/PRO705 Glypican-6 [Cleaved into:
Secreted glypican-6]
Q96T91 GPHA2 GPHA2 GPA2 ZSIG51 Glycoprotein hormone alpha-2
(Putative secreted protein Zsig51)
(Thyrostimulin subunit alpha)
Q86YW7 GPHB5 GPHB5 GPB5 ZLUT1 Glycoprotein hormone beta-5
(Thyrostimulin subunit beta)
Q02747 GUC2A GUCA2A GUCA2 Guanylin (Guanylate cyclase
activator 2A) (Guanylate cyclase-
activating protein 1) (Guanylate
cyclase-activating protein I)
(GCAP-I) [Cleaved into: HMW-
guanylin; Guanylin]
Q96RW7 HMCN1 HMCN1 FIBL6
Q9Y251 HPSE HPSE HEP HPA HPA1 HPR1
HPSE1 HSE1
Heparanase (EC 3.2.1.166) (Endo-
glucoronidase) (Heparanase-1)
(Hpa1) [Cleaved into: Heparanase
8 kDa subunit; Heparanase 50 kDa
subunit]
Q8WWQ
2
HPSE2 HPSE2 HPA2 Inactive heparanase-2 (Hpa2)
P02790 HEMO HPX Hemopexin (Beta-1B-
glycoprotein)
Q92743 HTRA1 HTRA1 HTRA PRSS11 Serine protease HTRA1 (EC
3.4.21.-) (High-temperature
requirement A serine peptidase 1)
(L56) (Serine protease 11)
P83110 HTRA3 HTRA3 PRSP Serine protease HTRA3 (EC
3.4.21.-) (High-temperature
requirement factor A3)
(Pregnancy-related serine
protease)
P83105 HTRA4 HTRA4 Serine protease HTRA4 (EC
3.4.21.-) (High-temperature
requirement factor A4)
Q12794 HYAL1 HYAL1 LUCA1 Hyaluronidase-1 (Hyal-1) (EC
3.2.1.35)
(Hyaluronoglucosaminidase-1)
183
(Lung carcinoma protein 1)
(LuCa-1)
O43820 HYAL3 HYAL3 LUCA3 Hyaluronidase-3 (Hyal-3) (EC
3.2.1.35)
(Hyaluronoglucosaminidase-3)
(Lung carcinoma protein 3)
(LuCa-3)
P35858 ALS IGFALS ALS Insulin growth factor-like family
member 1
Q8N6C5 IGSF1 IGSF1 IGDC1 KIAA0364
PGSF2
P35968 VGFR2 KDR FLK1 VEGFR2
Q14667 K0100 KIAA0100 BCOX1
O43240 KLK10 KLK10 NES1 PRSSL1 Kallikrein-10 (EC 3.4.21.-)
(Normal epithelial cell-specific 1)
(Protease serine-like 1)
Q9UKR0 KLK12 KLK12 KLKL5
UNQ669/PRO1303
Kallikrein-12 (EC 3.4.21.-)
(Kallikrein-like protein 5) (KLK-
L5)
P07288 KLK3 KLK3 APS Prostate-specific antigen (PSA)
(EC 3.4.21.77) (Gamma-
seminoprotein) (Seminin)
(Kallikrein-3) (P-30 antigen)
(Semenogelase)
Q9Y5K2 KLK4 KLK4 EMSP1 PRSS17 PSTS Kallikrein-4 (EC 3.4.21.-)
(Enamel matrix serine proteinase
1) (Kallikrein-like protein 1)
(KLK-L1) (Prostase) (Serine
protease 17)
P49862 KLK7 KLK7 PRSS6 SCCE Kallikrein-7 (hK7) (EC
3.4.21.117) (Serine protease 6)
(Stratum corneum chymotryptic
enzyme) (hSCCE)
Q9UKQ9 KLK9 KLK9 Kallikrein-9 (EC 3.4.21.-)
(Kallikrein-like protein 3) (KLK-
L3)
P03952 KLKB1 KLKB1 KLK3 Plasma kallikrein (EC 3.4.21.34)
(Fletcher factor) (Kininogenin)
(Plasma prekallikrein) (PKK)
[Cleaved into: Plasma kallikrein
heavy chain; Plasma kallikrein
light chain]
P25391 LAMA1 LAMA1 LAMA
P24043 LAMA2 LAMA2 LAMM
Q16787 LAMA3 LAMA3 LAMNA
184
O15230 LAMA5 LAMA5 KIAA0533 KIAA1907
Q13751 LAMB3 LAMB3 LAMNB1
Q6JVE6 LCN10 LCN10 Epididymal-specific lipocalin-10
Q6JVE9 LCN8 LCN8 LCN5 Epididymal-specific lipocalin-8
Q6P5S2 LEG1H LEG1 C6orf58 Protein LEG1 homolog
O95970 LGI1 LGI1 EPT UNQ775/PRO1569 Leucine-rich glioma-inactivated
protein 1 (Epitempin-1)
P01229 LSHB LHB Lutropin subunit beta (Lutropin
beta chain) (Luteinizing hormone
subunit beta) (LH-B) (LSH-B)
(LSH-beta)
P02750 A2GL LRG1 LRG Leucine-rich alpha-2-glycoprotein
(LRG)
Q8N6Y2 LRC17 LRRC17 P37NB
UNQ3076/PRO9909
Leucine-rich repeat-containing
protein 17 (p37NB)
P21941 MATN1 MATN1 CMP CRTM Cartilage matrix protein (Matrilin-
1)
O15232 MATN3 MATN3 Matrilin-3
O95460 MATN4 MATN4 Matrilin-4
P08581 MET MET
P08493 MGP MGP MGLAP GIG36 Matrix Gla protein (MGP) (Cell
growth-inhibiting gene 36 protein)
P03956 MMP1 MMP1 CLG Interstitial collagenase (EC
3.4.24.7) (Fibroblast collagenase)
(Matrix metalloproteinase-1)
(MMP-1) [Cleaved into: 22 kDa
interstitial collagenase; 27 kDa
interstitial collagenase]
P09238 MMP10 MMP10 STMY2 Stromelysin-2 (SL-2) (EC
3.4.24.22) (Matrix
metalloproteinase-10) (MMP-10)
(Transin-2)
P39900 MMP12 MMP12 HME Macrophage metalloelastase
(MME) (EC 3.4.24.65)
(Macrophage elastase) (ME)
(hME) (Matrix metalloproteinase-
12) (MMP-12)
P51512 MMP16 MMP16 MMPX2 Matrix metalloproteinase-16
(MMP-16) (EC 3.4.24.-) (MMP-
X2) (Membrane-type matrix
metalloproteinase 3) (MT-MMP 3)
(MTMMP3) (Membrane-type-3
matrix metalloproteinase) (MT3-
MMP) (MT3MMP)
185
Q99542 MMP19 MMP19 MMP18 RASI Matrix metalloproteinase-19
(MMP-19) (EC 3.4.24.-) (Matrix
metalloproteinase RASI) (Matrix
metalloproteinase-18) (MMP-18)
O60882 MMP20 MMP20 Matrix metalloproteinase-20
(MMP-20) (EC 3.4.24.-) (Enamel
metalloproteinase) (Enamelysin)
Q8N119 MMP21 MMP21 Matrix metalloproteinase-21
(MMP-21) (EC 3.4.24.-)
Q9H239 MMP28 MMP28 MMP25
UNQ1893/PRO4339
Matrix metalloproteinase-28
(MMP-28) (EC 3.4.24.-)
(Epilysin)
P08254 MMP3 MMP3 STMY1 Stromelysin-1 (SL-1) (EC
3.4.24.17) (Matrix
metalloproteinase-3) (MMP-3)
(Transin-1)
P22894 MMP8 MMP8 CLG1 Neutrophil collagenase (EC
3.4.24.34) (Matrix
metalloproteinase-8) (MMP-8)
(PMNL collagenase) (PMNL-CL)
P08118 MSMB MSMB PRSP Beta-microseminoprotein
(Immunoglobulin-binding factor)
(IGBF) (PN44) (Prostate secreted
seminal plasma protein) (Prostate
secretory protein of 94 amino
acids) (PSP-94) (PSP94) (Seminal
plasma beta-inhibin)
Q1L6U9 MSMP MSMP PSMP Prostate-associated
microseminoprotein (PC3-secreted
microprotein)
Q99972 MYOC MYOC GLC1A TIGR Myocilin (Myocilin 55 kDa
subunit) (Trabecular meshwork-
induced glucocorticoid response
protein) [Cleaved into: Myocilin,
N-terminal fragment (Myocilin 20
kDa N-terminal fragment);
Myocilin, C-terminal fragment
(Myocilin 35 kDa N-terminal
fragment)]
Q8TB73 NDNF NDNF C4orf31
UNQ2748/PRO6487
Protein NDNF (Neuron-derived
neurotrophic factor)
Q00604 NDP NDP EVR2 Norrin (Norrie disease protein)
(X-linked exudative
vitreoretinopathy 2 protein)
Q15223 NECT1 NECTIN1 HVEC PRR1 PVRL1 Nectin-1 (Herpes virus entry
mediator C) (Herpesvirus entry
186
mediator C) (HveC) (Herpesvirus
Ig-like receptor) (HIgR) (Nectin
cell adhesion molecule 1)
(Poliovirus receptor-related
protein 1) (CD antigen CD111)
Q96NY8 NECT4 NECTIN4 LNIR PRR4 PVRL4 Nectin-4 (Ig superfamily receptor
LNIR) (Nectin cell adhesion
molecule 4) (Poliovirus receptor-
related protein 4) [Cleaved into:
Processed poliovirus receptor-
related protein 4]
Q8TDF5 NETO1 NETO1 BTCL1 Neuropilin and tolloid-like protein
1 (Brain-specific transmembrane
protein containing 2 CUB and 1
LDL-receptor class A domains
protein 1)
Q6P988 NOTUM NOTUM OK/SW-CL.30 Palmitoleoyl-protein
carboxylesterase NOTUM (EC
3.1.1.98) (hNOTUM)
P0C0P6 NPS NPS Neuropeptide S
P47972 NPTX2 NPTX2 Neuronal pentraxin-2 (NP2)
(Neuronal pentraxin II) (NP-II)
Q92823 NRCAM NRCAM KIAA0343
O95156 NXPH2 NXPH2 NPH2 Neurexophilin-2
Q99784 NOE1 OLFM1 NOE1 NOEL1 Noelin (Neuronal olfactomedin-
related ER localized protein)
(Olfactomedin-1)
Q6UX06 OLFM4 OLFM4 GW112
UNQ362/PRO698
Olfactomedin-4 (OLM4)
(Antiapoptotic protein GW112)
(G-CSF-stimulated clone 1
protein) (hGC-1) (hOLfD)
Q6IE37 OVOS1 OVOS1
Q6IE36 OVOS2 OVOS2
P09466 PAEP PAEP Glycodelin (GD) (Placental
protein 14) (PP14) (Pregnancy-
associated endometrial alpha-2
globulin) (PAEG) (PEG)
(Progestagen-associated
endometrial protein)
(Progesterone-associated
endometrial protein)
P0C8F1 PATE4 PATE4 Prostate and testis expressed
protein 4 (PATE-like protein B)
(PATE-B)
Q96QU1 PCD15 PCDH15 USH1F
187
Q15113 PCOC1 PCOLCE PCPE1 Procollagen C-endopeptidase
enhancer 1 (Procollagen COOH-
terminal proteinase enhancer 1)
(PCPE-1) (Procollagen C-
proteinase enhancer 1) (Type 1
procollagen C-proteinase enhancer
protein) (Type I procollagen
COOH-terminal proteinase
enhancer)
Q9UKZ9 PCOC2 PCOLCE2 PCPE2
UNQ250/PRO287
Procollagen C-endopeptidase
enhancer 2 (Procollagen COOH-
terminal proteinase enhancer 2)
(PCPE-2) (Procollagen C-
proteinase enhancer 2)
Q15198 PGFRL PDGFRL PRLTS Platelet-derived growth factor
receptor-like protein (PDGFR-like
protein) (PDGF receptor beta-like
tumor suppressor)
P12273 PIP PIP GCDFP15 GPIP4 Prolactin-inducible protein (Gross
cystic disease fluid protein 15)
(GCDFP-15) (Prolactin-induced
protein) (Secretory actin-binding
protein) (SABP) (gp17)
Q504Y2 PKDCC PKDCC SGK493 VLK Extracellular tyrosine-protein
kinase PKDCC (EC 2.7.10.2)
(Protein kinase domain-containing
protein, cytoplasmic) (Protein
kinase-like protein SgK493)
(Sugen kinase 493) (Vertebrate
lonesome kinase)
P00750 TPA PLAT Tissue-type plasminogen activator
(t-PA) (t-plasminogen activator)
(tPA) (EC 3.4.21.68) (Alteplase)
(Reteplase) [Cleaved into: Tissue-
type plasminogen activator chain
A; Tissue-type plasminogen
activator chain B]
P00749 UROK PLAU Urokinase-type plasminogen
activator (U-plasminogen
activator) (uPA) (EC 3.4.21.73)
[Cleaved into: Urokinase-type
plasminogen activator long chain
A; Urokinase-type plasminogen
activator short chain A;
Urokinase-type plasminogen
activator chain B]
188
Q15195 PLGA PLGLA PLGLA1 PLGP2
PRGA
Plasminogen-like protein A
(Plasminogen-like protein A1)
(Plasminogen-related protein A)
P40967 PMEL PMEL D12S53E PMEL17 SILV Melanocyte protein PMEL
(ME20-M) (ME20M) (Melanocyte
protein Pmel 17) (Melanocytes
lineage-specific antigen GP100)
(Melanoma-associated ME20
antigen) (P1) (P100)
(Premelanosome protein) (Silver
locus protein homolog) [Cleaved
into: M-alpha (95 kDa
melanocyte-specific secreted
glycoprotein) (P26) (Secreted
melanoma-associated ME20
antigen) (ME20-S) (ME20S); M-
beta]
Q7Z5L7 PODN PODN SLRR5A
UNQ293/PRO332
Podocan
Q15063 POSTN POSTN OSF2
P07478 TRY2 PRSS2 TRY2 TRYP2 Trypsin-2 (EC 3.4.21.4) (Anionic
trypsinogen) (Serine protease 2)
(Trypsin II)
Q8NHM4 TRY6 PRSS3P2 T6 TRY6 Putative trypsin-6 (EC 3.4.21.4)
(Serine protease 3 pseudogene 2)
(Trypsinogen C)
Q6UWY2 PRS57 PRSS57 PRSSL1
UNQ782/PRO1599
Serine protease 57 (EC 3.4.21.-)
(Serine protease 1-like protein 1)
Q16557 PSG3 PSG3 Pregnancy-specific beta-1-
glycoprotein 3 (PS-beta-G-3)
(PSBG-3) (Pregnancy-specific
glycoprotein 3)
(Carcinoembryonic antigen SG5)
Q00889 PSG6 PSG6 CGM3 PSG10 PSG12
PSGGB
Pregnancy-specific beta-1-
glycoprotein 6 (PS-beta-G-6)
(PSBG-6) (Pregnancy-specific
glycoprotein 6) (Pregnancy-
specific beta-1-glycoprotein 10)
(PS-beta-G-10) (PSBG-10)
(Pregnancy-specific glycoprotein
10) (Pregnancy-specific beta-1-
glycoprotein 12) (PS-beta-G-12)
(PSBG-12) (Pregnancy-specific
glycoprotein 12)
Q13046 PSG7 PSG7 Putative pregnancy-specific beta-
1-glycoprotein 7 (PS-beta-G-7)
189
(PSBG-7) (Pregnancy-specific
glycoprotein 7)
Q9UQ74 PSG8 PSG8 Pregnancy-specific beta-1-
glycoprotein 8 (PS-beta-G-8)
(PSBG-8) (Pregnancy-specific
glycoprotein 8)
Q96A98 TIP39 PTH2 TIP39 TIPF39 Tuberoinfundibular peptide of 39
residues (TIP39) (Parathyroid
hormone 2)
P21246 PTN PTN HBNF1 NEGF1 Pleiotrophin (PTN) (Heparin-
binding brain mitogen) (HBBM)
(Heparin-binding growth factor 8)
(HBGF-8) (Heparin-binding
growth-associated molecule) (HB-
GAM) (Heparin-binding neurite
outgrowth-promoting factor 1)
(HBNF-1) (Osteoblast-specific
factor 1) (OSF-1)
P10082 PYY PYY Peptide YY (PYY) (PYY-I)
(Peptide tyrosine tyrosine)
[Cleaved into: Peptide YY(3-36)
(PYY-II)]
P20742 PZP PZP CPAMD6
P78509 RELN RELN
Q6XE38 SG1D4 SCGB1D4 UNQ517/PRO812 Secretoglobin family 1D member
4 (IFN-gamma-inducible
secretoglobin) (IIS)
Q96PL1 SG3A2 SCGB3A2 PNSP1 UGRP1
UNQ566/PRO1128
Secretoglobin family 3A member
2 (Pneumo secretory protein 1)
(PnSP-1) (Uteroglobin-related
protein 1)
Q07699 SCN1B SCN1B Sodium channel subunit beta-1
P01009 A1AT SERPINA1 AAT PI PRO0684
PRO2209
Alpha-1-antitrypsin (Alpha-1
protease inhibitor) (Alpha-1-
antiproteinase) (Serpin A1)
[Cleaved into: Short peptide from
AAT (SPAAT)]
P29622 KAIN SERPINA4 KST PI4 Kallistatin (Kallikrein inhibitor)
(Peptidase inhibitor 4) (PI-4)
(Serpin A4)
P05154 IPSP SERPINA5 PCI PLANH3
PROCI
Plasma serine protease inhibitor
(Acrosomal serine protease
inhibitor) (Plasminogen activator
inhibitor 3) (PAI-3) (PAI3)
(Protein C inhibitor) (PCI) (Serpin
A5)
190
P01008 ANT3 SERPINC1 AT3 PRO0309 Antithrombin-III (ATIII) (Serpin
C1)
P05121 PAI1 SERPINE1 PAI1 PLANH1 Plasminogen activator inhibitor 1
(PAI) (PAI-1) (Endothelial
plasminogen activator inhibitor)
(Serpin E1)
P07093 GDN SERPINE2 PI7 PN1 Glia-derived nexin (GDN)
(Peptidase inhibitor 7) (PI-7)
(Protease nexin 1) (PN-1)
(Protease nexin I) (Serpin E2)
A8MV23 SERP3 SERPINE3 Serpin E3
P36955 PEDF SERPINF1 PEDF PIG35 Pigment epithelium-derived factor
(PEDF) (Cell proliferation-
inducing gene 35 protein) (EPC-1)
(Serpin F1)
P08697 A2AP SERPINF2 AAP PLI Alpha-2-antiplasmin (Alpha-2-
AP) (Alpha-2-plasmin inhibitor)
(Alpha-2-PI) (Serpin F2)
P05155 IC1 SERPING1 C1IN C1NH Plasma protease C1 inhibitor (C1
Inh) (C1Inh) (C1 esterase
inhibitor) (C1-inhibiting factor)
(Serpin G1)
Q99574 NEUS SERPINI1 PI12 Neuroserpin (Peptidase inhibitor
12) (PI-12) (Serpin I1)
O75830 SPI2 SERPINI2 MEPI PI14 Serpin I2 (Myoepithelium-derived
serine protease inhibitor)
(Pancpin) (Pancreas-specific
protein TSA2004) (Peptidase
inhibitor 14) (PI-14)
Q15465 SHH SHH Sonic hedgehog protein (SHH)
(HHG-1) [Cleaved into: Sonic
hedgehog protein N-product;
Sonic hedgehog protein C-
product]
O75093 SLIT1 SLIT1 KIAA0813 MEGF4 SLIL1
X6R3P0 X6R3P0 SLIT2
O94813 SLIT2 SLIT2 SLIL3
P55000 SLUR1 SLURP1 ARS Secreted Ly-6/uPAR-related
protein 1 (SLURP-1) (ARS
component B) (ARS(component
B)-81/S) (Anti-neoplastic urinary
protein) (ANUP)
Q1W4C9 ISK13 SPINK13 HBVDNAPTP1
SPINK5L3
Serine protease inhibitor Kazal-
type 13 (Hepatitis B virus DNA
polymerase transactivated serine
protease inhibitor) (Hespintor)
191
(Serine protease inhibitor Kazal-
type 5-like 3)
Q6IE38 ISK14 SPINK14 SPINK5L2 Serine protease inhibitor Kazal-
type 14
P20155 ISK2 SPINK2 Serine protease inhibitor Kazal-
type 2 (Acrosin-trypsin inhibitor)
(Epididymis tissue protein Li 172)
(HUSI-II)
O60575 ISK4 SPINK4 Serine protease inhibitor Kazal-
type 4 (Peptide PEC-60 homolog)
Q6UWN8 ISK6 SPINK6 UNQ844/PRO1782 Serine protease inhibitor Kazal-
type 6 (Kallikrein inhibitor)
P58062 ISK7 SPINK7 ECG2
UNQ745/PRO1474
Serine protease inhibitor Kazal-
type 7 (Esophagus cancer-related
gene 2 protein) (ECRG-2)
O60687 SRPX2 SRPX2 SRPUL Sushi repeat-containing protein
SRPX2 (Sushi-repeat protein
upregulated in leukemia)
P02808 STAT STATH Statherin
P52823 STC1 STC1 STC Stanniocalcin-1 (STC-1)
Q4LDE5 SVEP1 SVEP1 C9orf13 CCP22 SELOB
Q2MV58 TECT1 TCTN1 TECT1
UNQ9369/PRO34160
Tectonic-1
O75443 TECTA TECTA
Q96PL2 TECTB TECTB Beta-tectorin
P04155 TFF1 TFF1 BCEI PS2 Trefoil factor 1 (Breast cancer
estrogen-inducible protein) (PNR-
2) (Polypeptide P1.A) (hP1.A)
(Protein pS2)
P01135 TGFA TGFA Protransforming growth factor
alpha [Cleaved into: Transforming
growth factor alpha (TGF-alpha)
(EGF-like TGF) (ETGF) (TGF
type 1)]
P10600 TGFB3 TGFB3 Transforming growth factor beta-3
(TGF-beta-3) [Cleaved into:
Latency-associated peptide (LAP)]
Q9UPZ6 THS7A THSD7A KIAA0960
P01033 TIMP1 TIMP1 CLGI TIMP Metalloproteinase inhibitor 1
(Erythroid-potentiating activity)
(EPA) (Fibroblast collagenase
inhibitor) (Collagenase inhibitor)
(Tissue inhibitor of
metalloproteinases 1) (TIMP-1)
192
P35625 TIMP3 TIMP3 Metalloproteinase inhibitor 2
(CSC-21K) (Tissue inhibitor of
metalloproteinases 2) (TIMP-2)
Q99727 TIMP4 TIMP4 Metalloproteinase inhibitor 3
(Protein MIG-5) (Tissue inhibitor
of metalloproteinases 3) (TIMP-3)
P24821 TENA TNC HXB
Q9UQP3 TENN TNN
Q8WU66 TSEAR TSPEAR C21orf29 Thrombospondin-type laminin G
domain and EAR repeat-
containing protein (TSP-EAR)
P04275 VWF VWF F8VWF
F5GYM2 F5GYM
2
WNT5B Protein Wnt-5b
D6RF94 D6RF94 WNT8A Protein Wnt-8a (Protein Wnt-8d)
O60844 ZG16 ZG16 Zymogen granule membrane
protein 16 (Zymogen granule
protein 16) (hZG16) (Secretory
lectin ZG16)
P21754 ZP3 ZP3 ZP3A ZP3B ZPC Zona pellucida sperm-binding
protein 3 (Sperm receptor)
(ZP3A/ZP3B) (Zona pellucida
glycoprotein 3) (Zp-3) (Zona
pellucida protein C) [Cleaved into:
Processed zona pellucida sperm-
binding protein 3]
Q12836 ZP4 ZP4 ZPB Zona pellucida sperm-binding
protein 4 (Zona pellucida
glycoprotein 4) (Zp-4) (Zona
pellucida protein B) [Cleaved into:
Processed zona pellucida sperm-
binding protein 4]
Q9BS86 ZPBP1 ZPBP ZPBP1 Zona pellucida-binding protein 1
(Sp38)
Q6X784 ZPBP2 ZPBP2 ZPBPL Zona pellucida-binding protein 2
(ZPBP-like protein)
P15151 PVR PVR PVS Poliovirus receptor (Nectin-like
protein 5) (NECL-5) (CD antigen
CD155)
Q9Y5C1 ANGL3 ANGPTL3 ANGPT5
UNQ153/PRO179
Angiopoietin-related protein 3
(Angiopoietin-5) (ANG-5)
(Angiopoietin-like protein 3)
[Cleaved into: ANGPTL3(17-
221); ANGPTL3(17-224)]
193
Q6GPI1 CTRB2 CTRB2 Chymotrypsinogen B2 (EC
3.4.21.1) [Cleaved into:
Chymotrypsin B2 chain A;
Chymotrypsin B2 chain B;
Chymotrypsin B2 chain C]
Q99983 OMD OMD SLRR2C
UNQ190/PRO216
Osteomodulin (Keratan sulfate
proteoglycan osteomodulin)
(KSPG osteomodulin)
(Osteoadherin) (OSAD)
Proteins functionally related to the regulation/modulation of the immune response
Q6UW15 REG3G REG3G PAP1B
UNQ429/PRO162
Regenerating islet-derived protein
3-gamma (REG-3-gamma)
(Pancreatitis-associated protein
1B) (PAP-1B) (Pancreatitis-
associated protein IB) (PAP IB)
(Regenerating islet-derived protein
III-gamma) (REG III) (Reg III-
gamma) [Cleaved into:
Regenerating islet-derived protein
3-gamma 16.5 kDa form;
Regenerating islet-derived protein
3-gamma 15 kDa form]
P07360 CO8G C8G Complement component C8
gamma chain
O43699 SIGL6 SIGLEC6 CD33L CD33L1
OBBP1
Sialic acid-binding Ig-like lectin 6
(Siglec-6) (CD33 antigen-like 1)
(CDw327) (Obesity-binding
protein 1) (OB-BP1) (CD antigen
CD327)
Q9BY15 AGRE3 ADGRE3 EMR3
UNQ683/PRO1562
Adhesion G protein-coupled
receptor E3 (EGF-like module
receptor 3) (EGF-like module-
containing mucin-like hormone
receptor-like 3)
P02771 FETA AFP HPAFP Alpha-fetoprotein (Alpha-1-
fetoprotein) (Alpha-fetoglobulin)
P28039 AOAH AOAH Acyloxyacyl hydrolase (EC
3.1.1.77) [Cleaved into:
Acyloxyacyl hydrolase small
subunit; Acyloxyacyl hydrolase
large subunit]
O75882 ATRN ATRN KIAA0548 MGCA
P61769 B2MG B2M CDABP0092 HDCMA22P
Q13072 BAGE1 BAGE BAGE1 B melanoma antigen 1 (B
melanoma antigen) (Antigen
194
MZ2-BA) (Cancer/testis antigen
2.1) (CT2.1)
O95972 BMP15 BMP15 GDF9B Bone morphogenetic protein 15
(BMP-15) (Growth/differentiation
factor 9B) (GDF-9B)
P17213 BPI BPI Bactericidal permeability-
increasing protein (BPI) (CAP 57)
Q9NP55 BPIA1 BPIFA1 LUNX NASG PLUNC
SPLUNC1 SPURT
UNQ787/PRO1606
BPI fold-containing family A
member 1 (Lung-specific protein
X) (Nasopharyngeal carcinoma-
related protein) (Palate lung and
nasal epithelium clone protein)
(Secretory protein in upper
respiratory tracts) (Short
PLUNC1) (SPLUNC1) (Tracheal
epithelium-enriched protein) (Von
Ebner protein Hl)
Q96DR5 BPIA2 BPIFA2 C20orf70 SPLUNC2
UNQ510/PRO1025
BPI fold-containing family A
member 2 (Parotid secretory
protein) (PSP) (Short palate, lung
and nasal epithelium carcinoma-
associated protein 2)
Q8TDL5 BPIB1 BPIFB1 C20orf114 LPLUNC1
UNQ706/PRO1357
BPI fold-containing family B
member 1 (Long palate, lung and
nasal epithelium carcinoma-
associated protein 1) (Von Ebner
minor salivary gland protein)
(VEMSGP)
Q8NFQ6 BPIFC BPIFC BPIL2 BPI fold-containing family C
protein (Bactericidal/permeability-
increasing protein-like 2) (BPI-
like 2)
Q13410 BT1A1 BTN1A1 BTN Butyrophilin subfamily 1 member
A1 (BT)
Q6UWK7 CJ099 C10orf99 UNQ1833/PRO3446 Secreted protein C10orf99
(Antimicrobial peptide-57) (AP-
57) (Colon-derived SUSD2
binding factor) (CSBF)
P02745 C1QA C1QA Complement C1q subcomponent
subunit A
P02746 C1QB C1QB Complement C1q subcomponent
subunit B
P02747 C1QC C1QC C1QG Complement C1q subcomponent
subunit C
Q9BXJ4 C1QT3 C1QTNF3 CTRP3
UNQ753/PRO1484
Complement C1q tumor necrosis
factor-related protein 3
195
(Collagenous repeat-containing
sequence 26 kDa protein)
(CORS26) (Secretory protein
CORS26)
Q9BXI9 C1QT6 C1QTNF6 CTRP6
UNQ581/PRO1151
Complement C1q tumor necrosis
factor-related protein 6
Q9BXJ2 C1QT7 C1QTNF7 CTRP7 Complement C1q tumor necrosis
factor-related protein 7
Q9NZP8 C1RL C1RL C1RL1 C1RLP CLSPA Complement C1r subcomponent-
like protein (C1r-LP) (C1r-like
protein) (EC 3.4.21.-) (C1r-like
serine protease analog protein)
(CLSPa)
P01024 CO3 C3 CPAMD1
P0C0L4 CO4A C4A CO4 CPAMD2
P0C0L5 CO4B C4B CO4 CPAMD3; C4B_2
P04003 C4BPA C4BPA C4BP C4b-binding protein alpha chain
(C4bp) (Proline-rich protein)
(PRP)
P07358 CO8B C8B Complement component C8 beta
chain (Complement component 8
subunit beta)
P02748 CO9 C9 Complement component C9
[Cleaved into: Complement
component C9a; Complement
component C9b]
P49913 CAMP CAMP CAP18 FALL39 HSD26 Cathelicidin antimicrobial peptide
(18 kDa cationic antimicrobial
protein) (CAP-18) (hCAP-18)
[Cleaved into: Antibacterial
peptide FALL-39 (FALL-39
peptide antibiotic); Antibacterial
peptide LL-37]
P22362 CCL1 CCL1 SCYA1 C-C motif chemokine 1 (Small-
inducible cytokine A1) (T
lymphocyte-secreted protein I-
309)
P51671 CCL11 CCL11 SCYA11 Eotaxin (C-C motif chemokine 11)
(Eosinophil chemotactic protein)
(Small-inducible cytokine A11)
Q99616 CCL13 CCL13 MCP4 NCC1 SCYA13 C-C motif chemokine 13 (CK-
beta-10) (Monocyte
chemoattractant protein 4)
(Monocyte chemotactic protein 4)
(MCP-4) (NCC-1) (Small-
inducible cytokine A13) [Cleaved
196
into: C-C motif chemokine 13,
long chain; C-C motif chemokine
13, medium chain; C-C motif
chemokine 13, short chain]
Q16627 CCL14 CCL14 NCC2 SCYA14 C-C motif chemokine 14
(Chemokine CC-1/CC-3) (HCC-
1/HCC-3) (HCC-1(1-74)) (NCC-
2) (Small-inducible cytokine A14)
[Cleaved into: HCC-1(3-74);
HCC-1(4-74); HCC-1(9-74)]
Q16663 CCL15 CCL15 MIP5 NCC3 SCYA15 C-C motif chemokine 15
(Chemokine CC-2) (HCC-2)
(Leukotactin-1) (LKN-1) (MIP-1
delta) (Macrophage inflammatory
protein 5) (MIP-5) (Mrp-2b)
(NCC-3) (Small-inducible
cytokine A15) [Cleaved into:
CCL15(22-92); CCL15(25-92);
CCL15(29-92)]
O15467 CCL16 CCL16 ILINCK NCC4
SCYA16
C-C motif chemokine 16
(Chemokine CC-4) (HCC-4)
(Chemokine LEC) (IL-10-
inducible chemokine) (LCC-1)
(Liver-expressed chemokine)
(Lymphocyte and monocyte
chemoattractant) (LMC)
(Monotactin-1) (MTN-1) (NCC-4)
(Small-inducible cytokine A16)
Q92583 CCL17 CCL17 SCYA17 TARC C-C motif chemokine 17 (CC
chemokine TARC) (Small-
inducible cytokine A17) (Thymus
and activation-regulated
chemokine)
P55774 CCL18 CCL18 AMAC1 DCCK1 MIP4
PARC SCYA18
C-C motif chemokine 18
(Alternative macrophage
activation-associated CC
chemokine 1) (AMAC-1) (CC
chemokine PARC) (Dendritic cell
chemokine 1) (DC-CK1)
(Macrophage inflammatory
protein 4) (MIP-4) (Pulmonary
and activation-regulated
chemokine) (Small-inducible
cytokine A18) [Cleaved into:
CCL18(1-68); CCL18(3-69);
CCL18(4-69)]
197
Q99731 CCL19 CCL19 ELC MIP3B SCYA19 C-C motif chemokine 19 (Beta-
chemokine exodus-3) (CK beta-
11) (Epstein-Barr virus-induced
molecule 1 ligand chemokine)
(EBI1 ligand chemokine) (ELC)
(Macrophage inflammatory
protein 3 beta) (MIP-3-beta)
(Small-inducible cytokine A19)
P13500 CCL2 CCL2 MCP1 SCYA2 C-C motif chemokine 2 (HC11)
(Monocyte chemoattractant
protein 1) (Monocyte chemotactic
and activating factor) (MCAF)
(Monocyte chemotactic protein 1)
(MCP-1) (Monocyte secretory
protein JE) (Small-inducible
cytokine A2)
P78556 CCL20 CCL20 LARC MIP3A SCYA20 C-C motif chemokine 20 (Beta-
chemokine exodus-1) (CC
chemokine LARC) (Liver and
activation-regulated chemokine)
(Macrophage inflammatory
protein 3 alpha) (MIP-3-alpha)
(Small-inducible cytokine A20)
[Cleaved into: CCL20(1-67);
CCL20(1-64); CCL20(2-70)]
O00626 CCL22 CCL22 MDC SCYA22 A-
152E5.1
C-C motif chemokine 22 (CC
chemokine STCP-1) (MDC(1-69))
(Macrophage-derived chemokine)
(Small-inducible cytokine A22)
(Stimulated T-cell chemotactic
protein 1) [Cleaved into: MDC(3-
69); MDC(5-69); MDC(7-69)]
O00175 CCL24 CCL24 MPIF2 SCYA24 C-C motif chemokine 24 (CK-
beta-6) (Eosinophil chemotactic
protein 2) (Eotaxin-2) (Myeloid
progenitor inhibitory factor 2)
(MPIF-2) (Small-inducible
cytokine A24)
O15444 CCL25 CCL25 SCYA25 TECK C-C motif chemokine 25
(Chemokine TECK) (Small-
inducible cytokine A25) (Thymus-
expressed chemokine)
Q9Y258 CCL26 CCL26 SCYA26
UNQ216/PRO242
C-C motif chemokine 26 (CC
chemokine IMAC) (Eotaxin-3)
(Macrophage inflammatory
protein 4-alpha) (MIP-4-alpha)
198
(Small-inducible cytokine A26)
(Thymic stroma chemokine-1)
(TSC-1)
Q9Y4X3 CCL27 CCL27 ILC SCYA27 C-C motif chemokine 27 (CC
chemokine ILC) (Cutaneous T-
cell-attracting chemokine)
(CTACK) (ESkine) (IL-11 R-
alpha-locus chemokine)
(Skinkine) (Small-inducible
cytokine A27)
P10147 CCL3 CCL3 G0S19-1 MIP1A SCYA3 C-C motif chemokine 3 (G0/G1
switch regulatory protein 19-1)
(Macrophage inflammatory
protein 1-alpha) (MIP-1-alpha)
(PAT 464.1) (SIS-beta) (Small-
inducible cytokine A3) (Tonsillar
lymphocyte LD78 alpha protein)
[Cleaved into: MIP-1-alpha(4-69)
(LD78-alpha(4-69))]
P16619 CL3L1 CCL3L1 D17S1718 G0S19-2
SCYA3L1; CCL3L3
C-C motif chemokine 3-like 1
(G0/G1 switch regulatory protein
19-2) (LD78-beta(1-70)) (PAT
464.2) (Small-inducible cytokine
A3-like 1) (Tonsillar lymphocyte
LD78 beta protein) [Cleaved into:
LD78-beta(3-70); LD78-beta(5-
70)]
P13236 CCL4 CCL4 LAG1 MIP1B SCYA4 C-C motif chemokine 4 (G-26 T-
lymphocyte-secreted protein)
(HC21) (Lymphocyte activation
gene 1 protein) (LAG-1) (MIP-1-
beta(1-69)) (Macrophage
inflammatory protein 1-beta)
(MIP-1-beta) (PAT 744) (Protein
H400) (SIS-gamma) (Small-
inducible cytokine A4) (T-cell
activation protein 2) (ACT-2)
[Cleaved into: MIP-1-beta(3-69)]
Q8NHW4 CC4L CCL4L1 CCL4L LAG1
SCYA4L1; CCL4L2 CCL4L
SCYA4L2
C-C motif chemokine 4-like
(Lymphocyte activation gene 1
protein) (LAG-1) (Macrophage
inflammatory protein 1-beta)
(MIP-1-beta) (Monocyte
adherence-induced protein 5-
alpha) (Small-inducible cytokine
A4-like)
199
P13501 CCL5 CCL5 D17S136E SCYA5 C-C motif chemokine 5 (EoCP)
(Eosinophil chemotactic cytokine)
(SIS-delta) (Small-inducible
cytokine A5) (T cell-specific
protein P228) (TCP228) (T-cell-
specific protein RANTES)
[Cleaved into: RANTES(3-68);
RANTES(4-68)]
P80098 CCL7 CCL7 MCP3 SCYA6 SCYA7 C-C motif chemokine 7
(Monocyte chemoattractant
protein 3) (Monocyte chemotactic
protein 3) (MCP-3) (NC28)
(Small-inducible cytokine A7)
P80075 CCL8 CCL8 MCP2 SCYA10 SCYA8 C-C motif chemokine 8 (HC14)
(Monocyte chemoattractant
protein 2) (Monocyte chemotactic
protein 2) (MCP-2) (Small-
inducible cytokine A8) [Cleaved
into: MCP-2(6-76)]
Q8TD46 MO2R1 CD200R1 CD200R CRTR2
MOX2R OX2R
UNQ2522/PRO6015
Cell surface glycoprotein CD200
receptor 1 (CD200 cell surface
glycoprotein receptor) (Cell
surface glycoprotein OX2 receptor
1)
P13987 CD59 CD59 MIC11 MIN1 MIN2
MIN3 MSK21
CD59 glycoprotein (1F5 antigen)
(20 kDa homologous restriction
factor) (HRF-20) (HRF20) (MAC-
inhibitory protein) (MAC-IP)
(MEM43 antigen) (Membrane
attack complex inhibition factor)
(MACIF) (Membrane inhibitor of
reactive lysis) (MIRL) (Protectin)
(CD antigen CD59)
P01732 CD8A CD8A MAL T-cell surface glycoprotein CD8
alpha chain (T-lymphocyte
differentiation antigen T8/Leu-2)
(CD antigen CD8a)
P10966 CD8B CD8B CD8B1 T-cell surface glycoprotein CD8
beta chain (CD antigen CD8b)
P00746 CFAD CFD DF PFD Complement factor D (EC
3.4.21.46) (Adipsin) (C3
convertase activator) (Properdin
factor D)
P05156 CFAI CFI IF Complement factor I (EC
3.4.21.45) (C3B/C4B inactivator)
[Cleaved into: Complement factor
200
I heavy chain; Complement factor
I light chain]
P36222 CH3L1 CHI3L1 Chitinase-3-like protein 1 (39 kDa
synovial protein) (Cartilage
glycoprotein 39) (CGP-39) (GP-
39) (hCGP-39) (YKL-40)
Q2VPA4 CR1L CR1L Complement component receptor
1-like protein (Complement C4b-
binding protein CR-1-like protein)
Q9HC73 CRLF2 CRLF2 CRL2 ILXR TSLPR Cytokine receptor-like factor 2
(Cytokine receptor-like 2) (IL-XR)
(Thymic stromal lymphopoietin
protein receptor) (TSLP receptor)
P02741 CRP CRP PTX1 C-reactive protein [Cleaved into:
C-reactive protein(1-205)]
P15509 CSF2R CSF2RA CSF2R CSF2RY Granulocyte-macrophage colony-
stimulating factor receptor subunit
alpha (GM-CSF-R-alpha)
(GMCSFR-alpha) (GMR-alpha)
(CDw116) (CD antigen CD116)
P01037 CYTN CST1 Cystatin-SN (Cystain-SA-I)
(Cystatin-1) (Salivary cystatin-
SA-1)
P01036 CYTS CST4 Cystatin-S (Cystatin-4) (Cystatin-
SA-III) (Salivary acidic protein 1)
O76096 CYTF CST7 Cystatin-F (Cystatin-7) (Cystatin-
like metastasis-associated protein)
(CMAP) (Leukocystatin)
Q5W186 CST9 CST9 CLM CTES7A Cystatin-9 (Cystatin-like
molecule)
P02778 CXL10 CXCL10 INP10 SCYB10 C-X-C motif chemokine 10 (10
kDa interferon gamma-induced
protein) (Gamma-IP10) (IP-10)
(Small-inducible cytokine B10)
[Cleaved into: CXCL10(1-73)]
O14625 CXL11 CXCL11 ITAC SCYB11
SCYB9B
C-X-C motif chemokine 11 (Beta-
R1) (H174) (Interferon gamma-
inducible protein 9) (IP-9)
(Interferon-inducible T-cell alpha
chemoattractant) (I-TAC) (Small-
inducible cytokine B11)
P48061 SDF1 CXCL12 SDF1 SDF1A SDF1B Stromal cell-derived factor 1
(SDF-1) (hSDF-1) (C-X-C motif
chemokine 12) (Intercrine reduced
in hepatomas) (IRH) (hIRH) (Pre-
B cell growth-stimulating factor)
201
(PBSF) [Cleaved into: SDF-1-
beta(3-72); SDF-1-alpha(3-67)]
O43927 CXL13 CXCL13 BCA1 BLC SCYB13 C-X-C motif chemokine 13
(Angie) (B cell-attracting
chemokine 1) (BCA-1) (B
lymphocyte chemoattractant)
(CXC chemokine BLC) (Small-
inducible cytokine B13)
P42830 CXCL5 CXCL5 ENA78 SCYB5 C-X-C motif chemokine 5 (ENA-
78(1-78)) (Epithelial-derived
neutrophil-activating protein 78)
(Neutrophil-activating peptide
ENA-78) (Small-inducible
cytokine B5) [Cleaved into: ENA-
78(8-78); ENA-78(9-78)]
P80162 CXCL6 CXCL6 GCP2 SCYB6 C-X-C motif chemokine 6
(Chemokine alpha 3) (CKA-3)
(Granulocyte chemotactic protein
2) (GCP-2) (Small-inducible
cytokine B6) [Cleaved into:
Small-inducible cytokine B6, N-
processed variant 1; Small-
inducible cytokine B6, N-
processed variant 2; Small-
inducible cytokine B6, N-
processed variant 3]
P10145 IL8 CXCL8 IL8
Q9NRR1 CYTL1 CYTL1 C4orf4
UNQ1942/PRO4425
Neuferricin (Cytochrome b5
domain-containing protein 2)
P59665 DEF1 DEFA1 DEF1 DEFA2 MRS;
DEFA1B
Neutrophil defensin 1 (Defensin,
alpha 1) (HNP-1) (HP-1) (HP1)
[Cleaved into: HP 1-56;
Neutrophil defensin 2 (HNP-2)
(HP-2) (HP2)]
P59666 DEF3 DEFA3 DEF3 Neutrophil defensin 3 (Defensin,
alpha 3) (HNP-3) (HP-3) (HP3)
[Cleaved into: HP 3-56;
Neutrophil defensin 2 (HNP-2)
(HP-2) (HP2)]
P12838 DEF4 DEFA4 DEF4 Neutrophil defensin 4 (Defensin,
alpha 4) (HNP-4) (HP-4)
Q01523 DEF5 DEFA5 DEF5 Defensin-5 (Defensin, alpha 5)
(HD5(20-94)) [Cleaved into:
HD5(23-94); HD5(29-94);
HD5(56-94); HD5(63-94)]
Q01524 DEF6 DEFA6 DEF6 Defensin-6 (Defensin, alpha 6)
202
P60022 DEFB1 DEFB1 BD1 HBD1 Beta-defensin 1 (BD-1) (hBD-1)
(Defensin, beta 1)
P81534 D103A DEFB103A BD3 DEFB103
DEFB3; DEFB103B
Beta-defensin 103 (Beta-defensin
3) (BD-3) (DEFB-3) (HBD3)
(hBD-3) (Defensin, beta 103)
(Defensin-like protein)
Q8NG35 D105A DEFB105A BD5 DEFB105
DEFB5; DEFB105B
Beta-defensin 105 (Beta-defensin
5) (BD-5) (DEFB-5) (Defensin,
beta 105)
Q8IZN7 D107A DEFB107A DEFB107 DEFB7;
DEFB107B
Beta-defensin 107 (Beta-defensin
7) (BD-7) (DEFB-7) (Defensin,
beta 107)
Q30KR1 DB109 DEFB109P1 DEFB109
DEFB109A; DEFB109P1B
Beta-defensin 109 (Defensin, beta
109) (Defensin, beta 109,
pseudogene 1/1B)
Q30KQ9 DB110 DEFB110 DEFB10 DEFB11
DEFB111
Beta-defensin 110 (Beta-defensin
10) (DEFB-10) (Beta-defensin 11)
(DEFB-11) (Beta-defensin 111)
(Defensin, beta 110) (Defensin,
beta 111)
Q30KQ7 DB113 DEFB113 DEFB13 Beta-defensin 113 (Beta-defensin
13) (DEFB-13) (Defensin, beta
113)
Q30KQ6 DB114 DEFB114 DEFB14 Beta-defensin 114 (Beta-defensin
14) (DEFB-14) (Defensin, beta
114)
Q30KQ5 DB115 DEFB115 DEFB15 Beta-defensin 115 (Beta-defensin
15) (DEFB-15) (Defensin, beta
115)
Q8N690 DB119 DEFB119 DEFB120 DEFB19
DEFB20 UNQ2449/PRO5729
Beta-defensin 119 (Beta-defensin
120) (Beta-defensin 19) (DEFB-
19) (Beta-defensin 20) (DEFB-20)
(Defensin, beta 119) (Defensin,
beta 120) (ESC42-RELA)
Q5J5C9 DB121 DEFB121 DEFB21 Beta-defensin 121 (Beta-defensin
21) (DEFB-21) (Defensin, beta
121)
Q8N688 DB123 DEFB123 DEFB23
UNQ1963/PRO4485
Beta-defensin 123 (Beta-defensin
23) (DEFB-23) (Defensin, beta
123)
Q8NES8 DB124 DEFB124 DEFB24 Beta-defensin 124 (Beta-defensin
24) (DEFB-24) (Defensin, beta
124)
Q9BYW3 DB126 DEFB126 C20orf8 DEFB26 Beta-defensin 126 (Beta-defensin
26) (DEFB-26) (Defensin, beta
203
126) (Epididymal secretory
protein 13.2) (ESP13.2) (HBD26)
Q9H1M4 DB127 DEFB127 C20orf73 DEFB27
UNQ1956/PRO6071
Beta-defensin 127 (Beta-defensin
27) (DEFB-27) (Defensin, beta
127)
Q7Z7B8 DB128 DEFB128 DEFB28 Beta-defensin 128 (Beta-defensin
28) (DEFB-28) (Defensin, beta
128)
Q9H1M3 DB129 DEFB129 C20orf87 DEFB29
UNQ5794/PRO19599
Beta-defensin 129 (Beta-defensin
29) (DEFB-29) (Defensin, beta
129)
Q30KQ2 DB130 DEFB130 DEFB30; DEFB130L Beta-defensin 130 (Beta-defensin
30) (DEFB-30) (Defensin, beta
130)
P59861 DB131 DEFB131 DEFB31 Beta-defensin 131 (Beta-defensin
31) (DEFB-31) (Defensin, beta
131)
Q7Z7B7 DB132 DEFB132 DEFB32
UNQ827/PRO1754
Beta-defensin 132 (Beta-defensin
32) (BD-32) (DEFB-32) (Defensin
HEL-75) (Defensin, beta 132)
Q30KQ1 DB133 DEFB133 Beta-defensin 133 (Defensin, beta
133)
Q4QY38 DB134 DEFB134 Beta-defensin 134 (Defensin, beta
134)
Q30KP9 DB135 DEFB135 Beta-defensin 135 (Defensin, beta
135)
Q30KP8 DB136 DEFB136 Beta-defensin 136 (Defensin, beta
136)
O15263 DFB4A DEFB4A DEFB102 DEFB2
DEFB4; DEFB4B
Beta-defensin 4A (Beta-defensin
2) (BD-2) (hBD-2) (Defensin, beta
2) (Skin-antimicrobial peptide 1)
(SAP1)
Q6UX07 DHR13 DHRS13 SDR7C5
UNQ419/PRO853
Dehydrogenase/reductase SDR
family member 13 (EC 1.1.-.-)
(Short chain
dehydrogenase/reductase family
7C member 5)
Q92874 DNSL2 DNASE1L2 DHP1 DNAS1L2 Deoxyribonuclease-1-like 2 (EC
3.1.21.-) (DNase I homolog
protein DHP1)
(Deoxyribonuclease I-like 2)
(DNase I-like 2)
Q9UHL4 DPP2 DPP7 DPP2 QPP Dipeptidyl peptidase 2 (EC
3.4.14.2) (Dipeptidyl
aminopeptidase II) (Dipeptidyl
peptidase 7) (Dipeptidyl peptidase
204
II) (DPP II) (Quiescent cell proline
dipeptidase)
Q14213 IL27B EBI3 IL27B Interleukin-27 subunit beta (IL-27
subunit beta) (IL-27B) (Epstein-
Barr virus-induced gene 3 protein)
(EBV-induced gene 3 protein)
P19235 EPOR EPOR Erythropoietin receptor (EPO-R)
Q7Z5A8 F19A3 FAM19A3 TAFA3 Protein FAM19A3 (Chemokine-
like protein TAFA-3)
P98173 FAM3A FAM3A 2-19 2.19 Protein FAM3A (Cytokine-like
protein 2-19)
P24071 FCAR FCAR CD89 Immunoglobulin alpha Fc receptor
(IgA Fc receptor) (CD antigen
CD89)
P08637 FCG3A FCGR3A CD16A FCG3
FCGR3 IGFR3
Low affinity immunoglobulin
gamma Fc region receptor III-A
(CD16a antigen) (Fc-gamma RIII-
alpha) (Fc-gamma RIII) (Fc-
gamma RIIIa) (FcRIII) (FcRIIIa)
(FcR-10) (IgG Fc receptor III-2)
(CD antigen CD16a)
O75015 FCG3B FCGR3B CD16B FCG3 FCGR3
IGFR3
Low affinity immunoglobulin
gamma Fc region receptor III-B
(Fc-gamma RIII-beta) (Fc-gamma
RIII) (Fc-gamma RIIIb) (FcRIII)
(FcRIIIb) (FcR-10) (IgG Fc
receptor III-1) (CD antigen
CD16b)
Q6BAA4 FCRLB FCRLB FCRL2 FCRLM2
FCRY FREB2
Fc receptor-like B (Fc receptor
homolog expressed in B-cells
protein 2) (FREB-2) (Fc receptor-
like and mucin-like protein 2) (Fc
receptor-like protein 2) (Fc
receptor-related protein Y) (FcRY)
Q8NFU4 FDSCP FDCSP C4orf7
UNQ733/PRO1419
Follicular dendritic cell secreted
peptide (FDC secreted protein)
(FDC-SP)
O15520 FGF10 FGF10 Fibroblast growth factor 10 (FGF-
10) (Keratinocyte growth factor 2)
Q14314 FGL2 FGL2 Fibroleukin (Fibrinogen-like
protein 2) (pT49)
Q9UK05 GDF2 GDF2 BMP9 Growth/differentiation factor 2
(GDF-2) (Bone morphogenetic
protein 9) (BMP-9)
P55259 GP2 GP2 Pancreatic secretory granule
membrane major glycoprotein
205
GP2 (Pancreatic zymogen granule
membrane protein GP-2) (ZAP75)
P12544 GRAA GZMA CTLA3 HFSP Granzyme A (EC 3.4.21.78) (CTL
tryptase) (Cytotoxic T-lymphocyte
proteinase 1) (Fragmentin-1)
(Granzyme-1) (Hanukkah factor)
(H factor) (HF)
P49863 GRAK GZMK TRYP2 Granzyme K (EC 3.4.21.-)
(Fragmentin-3) (Granzyme-3)
(NK-tryptase-2) (NK-Tryp-2)
P51124 GRAM GZMM MET1 Granzyme M (EC 3.4.21.-) (Met-1
serine protease) (Hu-Met-1) (Met-
ase) (Natural killer cell granular
protease)
A8MTL9 HMSD HMSD Serpin-like protein HMSD (Minor
histocompatibility protein HMSD)
(Minor histocompatibility serpin
domain-containing protein)
P15516 HIS3 HTN3 HIS2 Histatin-3 (Basic histidine-rich
protein) (Hst) (Histidine-rich
protein 3) (PB) [Cleaved into:
Histatin-3; His3-(20-44)-peptide
(His3 20/44) (His3-(1-25)-peptide)
(His3 1/25) (Histatin-3 1/25)
(Histatin-6); His3-(20-43)-peptide
(His3 20/43) (His3-(1-24)-peptide)
(His3 1/24) (Histatin-3 1/24)
(Histatin-5); His3-(20-32)-peptide
(His3 20/32) (His3-(1-13)-peptide)
(His3 1/13) (Histatin-3 1/13);
His3-(20-31)-peptide (His3 20/31)
(His3-(1-12)-peptide) (His3 1/12)
(Histatin-3 1/12); His3-(20-30)-
peptide (His3 20/30) (His3-(1-11)-
peptide) (His3 1/11) (Histatin-3
1/11); His3-(24-32)-peptide (His3
24/32) (His3-(5-13)-peptide) (His3
5/13) (Histatin-3 5/13); His3-(24-
31)-peptide (His3 24/31) (His3-(5-
12)-peptide) (His3 5/12) (Histatin-
11) (Histatin-3 5/12); His3-(24-
30)-peptide (His3 24/30) (His3-(5-
11)-peptide) (His3 5/11) (Histatin-
12) (Histatin-3 5/11); His3-(25-
32)-peptide (His3 25/32) (His3-(6-
13)-peptide) (His3 6/13) (Histatin-
206
3 6/13); His3-(25-30)-peptide
(His3 25/30) (His3-(6-11)-peptide)
(His3 6/11) (Histatin-3 6/11);
His3-(26-32)-peptide (His3 26/32)
(His3-(7-13)-peptide) (His3 7/13)
(Histatin-3 7/13); His3-(26-31)-
peptide (His3 26/31) (His3-(7-12)-
peptide) (His3 7/12) (Histatin-3
7/12); His3-(26-30)-peptide (His3
26/30) (His3-(7-11)-peptide) (His3
7/11) (Histatin-3 7/11); His3-(31-
51)-peptide (His3 31/51) (His3-
(12-32)-peptide) (His3 12/32)
(Histatin-3 12/32) (Histatin-4);
His3-(31-44)-peptide (His3 31/44)
(His3-(12-25)-peptide) (His3
12/25) (Histatin-3 12/25)
(Histatin-9); His3-(31-43)-peptide
(His3 31/43) (His3-(12-24)-
peptide) (His3 12/24) (Histatin-3
12/24) (Histatin-7); His3-(32-44)-
peptide (His3 32/44) (His3-(13-
25)-peptide) (His3 13/25)
(Histatin-10) (Histatin-3 13/25);
His3-(32-43)-peptide (His3 32-43)
(His3-(13-24)-peptide) (His3
13/24) (Histatin-3 13/24)
(Histatin-8); His3-(33-44)-peptide
(His3 33/44) (His3-(14-25)-
peptide) (His3 14/25) (Histatin-3
14/25); His3-(33-43)-peptide
(His3 33/43) (His3-(14-24)-
peptide) (His3 14/24) (Histatin-3
14/24); His3-(34-44)-peptide
(His3 34/44) (His3-(15-25)-
peptide) (His3 15/25) (Histatin-3
15/25); His3-(34-43)-peptide
(His3 34/43) (His3-(15-24)-
peptide) (His3 15/24) (Histatin-3
15/24); His3-(45-51)-peptide
(His3 45/51) (His3-(26-32)-
peptide) (His3 26/32) (Histatin-3
26/32); His3-(47-51)-peptide
(His3 47/51) (His3-(28-32)-
peptide) (His3 28/32) (Histatin-3
28/32); His3-(48-51)-peptide
207
(His3 48/51) (His3-(29-32)-
peptide) (His3 29/32) (Histatin-3
29/32)]
Q14773 ICAM4 ICAM4 LW Intercellular adhesion molecule 4
(ICAM-4) (Landsteiner-Wiener
blood group glycoprotein) (LW
blood group protein) (CD antigen
CD242)
Q9Y6W8 ICOS ICOS AILIM Inducible T-cell costimulator
(Activation-inducible lymphocyte
immunomediatory molecule) (CD
antigen CD278)
P13284 GILT IFI30 GILT IP30 Gamma-interferon-inducible
lysosomal thiol reductase (EC
1.8.-.-) (Gamma-interferon-
inducible protein IP-30)
(Legumaturain)
P01562 IFNA1 IFNA1; IFNA13 Interferon alpha-1/13 (IFN-alpha-
1/13) (Interferon alpha-D) (LeIF
D)
P01566 IFN10 IFNA10 Interferon alpha-10 (IFN-alpha-
10) (Interferon alpha-6L)
(Interferon alpha-C) (LeIF C)
P01570 IFN14 IFNA14 Interferon alpha-14 (IFN-alpha-
14) (Interferon alpha-H) (LeIF H)
(Interferon lambda-2-H)
P05015 IFN16 IFNA16 Interferon alpha-16 (IFN-alpha-
16) (Interferon alpha-WA)
P01571 IFN17 IFNA17 Interferon alpha-17 (IFN-alpha-
17) (Interferon alpha-88)
(Interferon alpha-I') (LeIF I)
(Interferon alpha-T)
P01563 IFNA2 IFNA2 IFNA2A IFNA2B
IFNA2C
Interferon alpha-2 (IFN-alpha-2)
(Interferon alpha-A) (LeIF A)
P01568 IFN21 IFNA21 Interferon alpha-21 (IFN-alpha-
21) (Interferon alpha-F) (LeIF F)
P05014 IFNA4 IFNA4 Interferon alpha-4 (IFN-alpha-4)
(Interferon alpha-4B) (Interferon
alpha-76) (Interferon alpha-M1)
P05013 IFNA6 IFNA6 Interferon alpha-6 (IFN-alpha-6)
(Interferon alpha-54) (Interferon
alpha-K) (LeIF K)
P01567 IFNA7 IFNA7 Interferon alpha-7 (IFN-alpha-7)
(Interferon alpha-J) (LeIF J)
(Interferon alpha-J1) (IFN-alpha-
J1)
208
P32881 IFNA8 IFNA8 Interferon alpha-8 (IFN-alpha-8)
(Interferon alpha-B) (LeIF B)
(Interferon alpha-B2)
P48551 INAR2 IFNAR2 IFNABR IFNARB Interferon alpha/beta receptor 2
(IFN-R-2) (IFN-alpha binding
protein) (IFN-alpha/beta receptor
2) (Interferon alpha binding
protein) (Type I interferon
receptor 2)
P01574 IFNB IFNB1 IFB IFNB Interferon beta (IFN-beta)
(Fibroblast interferon)
Q86WN2 IFNE IFNE IFNE1 UNQ360/PRO655 Interferon epsilon (IFN-epsilon)
(Interferon epsilon-1)
P01579 IFNG IFNG Interferon gamma (IFN-gamma)
(Immune interferon)
P05000 IFNW1 IFNW1 Interferon omega-1 (Interferon
alpha-II-1)
A6NJS3 IV1U1 IGHV1OR21-1 Putative V-set and
immunoglobulin domain-
containing-like protein
IGHV1OR21-1 (Immunoglobulin
heavy variable 1 orphon 21-1)
A6NJ16 IV4F8 IGHV4OR15-8 VSIG6 Putative V-set and
immunoglobulin domain-
containing-like protein
IGHV4OR15-8 (Immunoglobulin
heavy variable 4 orphon 15-8)
(Putative V-set and
immunoglobulin domain-
containing protein 6)
A6NJ69 IGIP IGIP C5orf53 IgA-inducing protein homolog
A6NGN9 IGLO5 IGLON5 IgLON family member 5
Q96ID5 IGS21 IGSF21 Immunoglobulin superfamily
member 21 (IgSF21)
P29459 IL12A IL12A NKSF1 Interleukin-12 subunit alpha (IL-
12A) (Cytotoxic lymphocyte
maturation factor 35 kDa subunit)
(CLMF p35) (IL-12 subunit p35)
(NK cell stimulatory factor chain
1) (NKSF1)
P29460 IL12B IL12B NKSF2 Interleukin-12 subunit beta (IL-
12B) (Cytotoxic lymphocyte
maturation factor 40 kDa subunit)
(CLMF p40) (IL-12 subunit p40)
(NK cell stimulatory factor chain
2) (NKSF2)
209
P35225 IL13 IL13 NC30 Interleukin-13 (IL-13)
Q16552 IL17 IL17A CTLA8 IL17 Interleukin-17A (IL-17) (IL-17A)
(Cytotoxic T-lymphocyte-
associated antigen 8) (CTLA-8)
Q9NRM6 I17RB IL17RB CRL4 EVI27 IL17BR
UNQ2501/PRO19612
Interleukin-17 receptor B (IL-17
receptor B) (IL-17RB) (Cytokine
receptor-like 4) (IL-17 receptor
homolog 1) (IL-17Rh1) (IL17Rh1)
(Interleukin-17B receptor) (IL-
17B receptor)
O95998 I18BP IL18BP Interleukin-18-binding protein
(IL-18BP) (Tadekinig-alfa)
Q9UHD0 IL19 IL19 ZMDA1 Interleukin-19 (IL-19) (Melanoma
differentiation-associated protein-
like protein) (NG.1)
P14778 IL1R1 IL1R1 IL1R IL1RA IL1RT1 Interleukin-1 receptor type 1 (IL-
1R-1) (IL-1RT-1) (IL-1RT1)
(CD121 antigen-like family
member A) (Interleukin-1 receptor
alpha) (IL-1R-alpha) (Interleukin-
1 receptor type I) (p80) (CD
antigen CD121a) [Cleaved into:
Interleukin-1 receptor type 1,
membrane form (mIL-1R1) (mIL-
1RI); Interleukin-1 receptor type
1, soluble form (sIL-1R1) (sIL-
1RI)]
P27930 IL1R2 IL1R2 IL1RB Interleukin-1 receptor type 2 (IL-
1R-2) (IL-1RT-2) (IL-1RT2)
(CD121 antigen-like family
member B) (CDw121b) (IL-1 type
II receptor) (Interleukin-1 receptor
beta) (IL-1R-beta) (Interleukin-1
receptor type II) (CD antigen
CD121b) [Cleaved into:
Interleukin-1 receptor type 2,
membrane form (mIL-1R2) (mIL-
1RII); Interleukin-1 receptor type
2, soluble form (sIL-1R2) (sIL-
1RII)]
Q9NPH3 IL1AP IL1RAP C3orf13 IL1R3 Interleukin-1 receptor accessory
protein (IL-1 receptor accessory
protein) (IL-1RAcP) (Interleukin-
1 receptor 3) (IL-1R-3) (IL-1R3)
Q01638 ILRL1 IL1RL1 DER4 ST2 T1 Interleukin-1 receptor-like 1
(Protein ST2)
210
P18510 IL1RA IL1RN IL1F3 IL1RA Interleukin-1 receptor antagonist
protein (IL-1RN) (IL-1ra) (IRAP)
(ICIL-1RA) (IL1 inhibitor)
(Anakinra)
P60568 IL2 IL2 Interleukin-2 (IL-2) (T-cell growth
factor) (TCGF) (Aldesleukin)
Q9NYY1 IL20 IL20 ZCYTO10
UNQ852/PRO1801
Interleukin-20 (IL-20) (Cytokine
Zcyto10)
Q9GZX6 IL22 IL22 ILTIF ZCYTO18
UNQ3099/PRO10096
Interleukin-22 (IL-22) (Cytokine
Zcyto18) (IL-10-related T-cell-
derived-inducible factor) (IL-TIF)
Q9NPF7 IL23A IL23A SGRF
UNQ2498/PRO5798
Interleukin-23 subunit alpha (IL-
23 subunit alpha) (IL-23-A)
(Interleukin-23 subunit p19) (IL-
23p19)
Q13007 IL24 IL24 MDA7 ST16 Interleukin-24 (IL-24) (Melanoma
differentiation-associated gene 7
protein) (MDA-7) (Suppression of
tumorigenicity 16 protein)
Q8NEV9 IL27A IL27 IL27A IL30 Interleukin-27 subunit alpha (IL-
27 subunit alpha) (IL-27-A)
(IL27-A) (Interleukin-30) (p28)
Q6EBC2 IL31 IL31 Interleukin-31 (IL-31)
Q6ZMJ4 IL34 IL34 C16orf77 Interleukin-34 (IL-34)
P05113 IL5 IL5 Interleukin-5 (IL-5) (B-cell
differentiation factor I)
(Eosinophil differentiation factor)
(T-cell replacing factor) (TRF)
P13232 IL7 IL7 Interleukin-7 (IL-7)
P16871 IL7RA IL7R Interleukin-7 receptor subunit
alpha (IL-7 receptor subunit alpha)
(IL-7R subunit alpha) (IL-7R-
alpha) (IL-7RA) (CDw127) (CD
antigen CD127)
P15248 IL9 IL9 Interleukin-9 (IL-9) (Cytokine
P40) (T-cell growth factor P40)
Q01113 IL9R IL9R Interleukin-9 receptor (IL-9
receptor) (IL-9R) (CD antigen
CD129)
P08476 INHBA INHBA Inhibin beta A chain (Activin beta-
A chain) (Erythroid differentiation
protein) (EDF)
Q8TB96 TIP ITFG1 LNKN-1 TIP CDA08 T-cell immunomodulatory protein
(Protein TIP) (Integrin-alpha FG-
GAP repeat-containing protein 1)
(Linkin)
211
Q9BX67 JAM3 JAM3 UNQ859/PRO1868 Junctional adhesion molecule C
(JAM-C) (JAM-2) (Junctional
adhesion molecule 3) (JAM-3)
P01591 IGJ JCHAIN IGCJ IGJ Immunoglobulin J chain (Joining
chain of multimeric IgA and IgM)
P21583 SCF KITLG MGF SCF Kit ligand (Mast cell growth
factor) (MGF) (Stem cell factor)
(SCF) (c-Kit ligand) [Cleaved
into: Soluble KIT ligand
(sKITLG)]
Q9UEF7 KLOT KL
P18428 LBP LBP Lipopolysaccharide-binding
protein (LBP)
Q08380 LG3BP LGALS3BP M2BP Galectin-3-binding protein
(Basement membrane autoantigen
p105) (Lectin galactoside-binding
soluble 3-binding protein) (Mac-2-
binding protein) (MAC2BP)
(Mac-2 BP) (Tumor-associated
antigen 90K)
P15018 LIF LIF HILDA Leukemia inhibitory factor (LIF)
(Differentiation-stimulating factor)
(D factor) (Melanoma-derived
LPL inhibitor) (MLPLI)
(Emfilermin)
Q8N149 LIRA2 LILRA2 ILT1 LIR7 Leukocyte immunoglobulin-like
receptor subfamily A member 2
(CD85 antigen-like family
member H) (Immunoglobulin-like
transcript 1) (ILT-1) (Leukocyte
immunoglobulin-like receptor 7)
(LIR-7) (CD antigen CD85h)
Q8N6C8 LIRA3 LILRA3 ILT6 LIR4 Leukocyte immunoglobulin-like
receptor subfamily A member 3
(CD85 antigen-like family
member E) (Immunoglobulin-like
transcript 6) (ILT-6) (Leukocyte
immunoglobulin-like receptor 4)
(LIR-4) (Monocyte inhibitory
receptor HM43/HM31) (CD
antigen CD85e)
P01374 TNFB LTA TNFB TNFSF1 Lymphotoxin-alpha (LT-alpha)
(TNF-beta) (Tumor necrosis factor
ligand superfamily member 1)
Q8NDX9 LY65B LY6G5B C6orf19 G5B Lymphocyte antigen 6 complex
locus protein G5b
212
Q5SRR4 LY65C LY6G5C C6orf20 G5C NG33 Lymphocyte antigen 6 complex
locus protein G5c
O95711 LY86 LY86 MD1 Lymphocyte antigen 86 (Ly-86)
(Protein MD-1)
Q9Y6Y9 LY96 LY96 ESOP1 MD2 Lymphocyte antigen 96 (Ly-96)
(ESOP-1) (Protein MD-2)
Q9BZG9 LYNX1 LYNX1 SLURP2 Ly-6/neurotoxin-like protein 1
(Secreted Ly-6/uPAR domain-
containing protein 2) (Secreted
Ly-6/uPAR-related protein 2)
(SLURP-2)
Q6UX82 LYPD8 LYPD8 UNQ511/PRO1026 Ly6/PLAUR domain-containing
protein 8
Q6UWQ5 LYZL1 LYZL1 LYC2
UNQ648/PRO1278
Lysozyme-like protein 1 (EC
3.2.1.17)
Q7Z4W2 LYZL2 LYZL2 Lysozyme-like protein 2
(Lysozyme-2) (EC 3.2.1.17)
O75951 LYZL6 LYZL6 LYC1
UNQ754/PRO1485
Lysozyme-like protein 6 (EC
3.2.1.17)
Q95460 HMR1 MR1 Major histocompatibility complex
class I-related gene protein (MHC
class I-related gene protein) (Class
I histocompatibility antigen-like
protein)
Q969H8 MYDGF MYDGF C19orf10 IL25 Myeloid-derived growth factor
(MYDGF) (Interleukin-25) (IL-
25) (Stromal cell-derived growth
factor SF20)
P02763 A1AG1 ORM1 AGP1 Alpha-1-acid glycoprotein 1 (AGP
1) (Orosomucoid-1) (OMD 1)
P19652 A1AG2 ORM2 AGP2 Alpha-1-acid glycoprotein 2 (AGP
2) (Orosomucoid-2) (OMD 2)
Q9BQ51 PD1L2 PDCD1LG2 B7DC CD273
PDCD1L2 PDL2
Programmed cell death 1 ligand 2
(PD-1 ligand 2) (PD-L2) (PDCD1
ligand 2) (Programmed death
ligand 2) (Butyrophilin B7-DC)
(B7-DC) (CD antigen CD273)
P02776 PLF4 PF4 CXCL4 SCYB4 Platelet factor 4 (PF-4) (C-X-C
motif chemokine 4) (Iroplact)
(Oncostatin-A) [Cleaved into:
Platelet factor 4, short form]
P10720 PF4V PF4V1 CXCL4V1 SCYB4V1 Platelet factor 4 variant (C-X-C
motif chemokine 4 variant)
(CXCL4L1) (PF4alt) (PF4var1)
[Cleaved into: Platelet factor 4
variant(4-74); Platelet factor 4
213
variant(5-74); Platelet factor 4
variant(6-74)]
Q96LB9 PGRP3 PGLYRP3 PGRPIA Peptidoglycan recognition protein
3 (Peptidoglycan recognition
protein I-alpha) (PGLYRPIalpha)
(PGRP-I-alpha) (Peptidoglycan
recognition protein intermediate
alpha)
Q96LB8 PGRP4 PGLYRP4 PGRPIB SBBI67 Peptidoglycan recognition protein
4 (Peptidoglycan recognition
protein I-beta) (PGLYRPIbeta)
(PGRP-I-beta) (Peptidoglycan
recognition protein intermediate
beta)
P19957 ELAF PI3 WAP3 WFDC14 Elafin (Elastase-specific inhibitor)
(ESI) (Peptidase inhibitor 3) (PI-3)
(Protease inhibitor WAP3) (Skin-
derived antileukoproteinase)
(SKALP) (WAP four-disulfide
core domain protein 14)
Q02325 PLGB PLGLB1 PLGL PRGB;
PLGLB2 PLGP1
Plasminogen-like protein B
(Plasminogen-related protein B)
P14222 PERF PRF1 PFP Perforin-1 (P1) (Cytolysin)
(Lymphocyte pore-forming
protein) (PFP)
P13727 PRG2 PRG2 MBP Bone marrow proteoglycan
(BMPG) (Proteoglycan 2)
[Cleaved into: Eosinophil granule
major basic protein (EMBP)
(MBP) (Pregnancy-associated
major basic protein)]
P01236 PRL PRL Prolactin (PRL)
Q9HC23 PROK2 PROK2 BV8 Prokineticin-2 (PK2) (Protein Bv8
homolog)
P07477 TRY1 PRSS1 TRP1 TRY1 TRYP1 Trypsin-1 (EC 3.4.21.4) (Beta-
trypsin) (Cationic trypsinogen)
(Serine protease 1) (Trypsin I)
[Cleaved into: Alpha-trypsin chain
1; Alpha-trypsin chain 2]
P11465 PSG2 PSG2 PSBG2 Pregnancy-specific beta-1-
glycoprotein 2 (PS-beta-G-2)
(PSBG-2) (Pregnancy-specific
glycoprotein 2) (Pregnancy-
specific beta-1 glycoprotein E)
(PS-beta-E)
214
Q00888 PSG4 PSG4 CGM4 PSG9 Pregnancy-specific beta-1-
glycoprotein 4 (PS-beta-G-4)
(PSBG-4) (Pregnancy-specific
glycoprotein 4) (Pregnancy-
specific beta-1-glycoprotein 9)
(PS-beta-G-9) (PSBG-9)
(Pregnancy-specific glycoprotein
9)
Q15238 PSG5 PSG5 Pregnancy-specific beta-1-
glycoprotein 5 (PS-beta-G-5)
(PSBG-5) (Pregnancy-specific
glycoprotein 5) (Fetal liver non-
specific cross-reactive antigen 3)
(FL-NCA-3)
Q96A99 PTX4 PTX4 C16orf38 Pentraxin-4
Q8TD07 N2DL4 RAET1E LETAL N2DL4
ULBP4 UNQ1867/PRO4303
NKG2D ligand 4 (N2DL-4)
(NKG2DL4) (Lymphocyte
effector toxicity activation ligand)
(RAE-1-like transcript 4) (RL-4)
(Retinoic acid early transcript 1E)
Q99969 RARR2 RARRES2 TIG2 Retinoic acid receptor responder
protein 2 (Chemerin) (RAR-
responsive protein TIG2)
(Tazarotene-induced gene 2
protein)
Q06141 REG3A REG3A HIP PAP PAP1 Regenerating islet-derived protein
3-alpha (REG-3-alpha)
(Hepatointestinal pancreatic
protein) (HIP/PAP) (Human
proislet peptide) (Pancreatitis-
associated protein 1)
(Regenerating islet-derived protein
III-alpha) (Reg III-alpha) [Cleaved
into: Regenerating islet-derived
protein 3-alpha 16.5 kDa form;
Regenerating islet-derived protein
3-alpha 15 kDa form]
P12724 ECP RNASE3 ECP RNS3 Eosinophil cationic protein (ECP)
(EC 3.1.27.-) (Ribonuclease 3)
(RNase 3)
Q96QR1 SG3A1 SCGB3A1 HIN1 PNSP2
UGRP2 UNQ629/PRO1245
Secretoglobin family 3A member
1 (Cytokine HIN-1) (High in
normal 1) (Pneumo secretory
protein 2) (PnSP-2) (Uteroglobin-
related protein 2)
215
Q8WVN6 SCTM1 SECTM1 K12 Secreted and transmembrane
protein 1 (Protein K-12)
O95025 SEM3D SEMA3D UNQ760/PRO1491
P01011 AACT SERPINA3 AACT GIG24
GIG25
Alpha-1-antichymotrypsin (ACT)
(Cell growth-inhibiting gene 24/25
protein) (Serpin A3) [Cleaved
into: Alpha-1-antichymotrypsin
His-Pro-less]
Q13291 SLAF1 SLAMF1 SLAM Signaling lymphocytic activation
molecule (CDw150) (IPO-3)
(SLAM family member 1) (CD
antigen CD150)
Q5VX71 SUSD4 SUSD4 UNQ196/PRO222 Sushi domain-containing protein 4
P61812 TGFB2 TGFB2 Transforming growth factor beta-2
(TGF-beta-2) (BSC-1 cell growth
inhibitor) (Cetermin)
(Glioblastoma-derived T-cell
suppressor factor) (G-TSF)
(Polyergin) [Cleaved into:
Latency-associated peptide (LAP)]
Q15661 TRYB1 TPSAB1 TPS1 TPS2 TPSB1 Tryptase alpha/beta-1 (Tryptase-1)
(EC 3.4.21.59) (Tryptase I)
(Tryptase alpha-1)
P20231 TRYB2 TPSB2 TPS2 Tryptase beta-2 (Tryptase-2) (EC
3.4.21.59) (Tryptase II)
Q9NP99 TREM1 TREM1 Triggering receptor expressed on
myeloid cells 1 (TREM-1)
(Triggering receptor expressed on
monocytes 1) (CD antigen
CD354)
Q6UXN2 TRML4 TREML4 TLT4
UNQ9425/PRO34675
Trem-like transcript 4 protein
(TLT-4) (Triggering receptor
expressed on myeloid cells-like
protein 4)
Q969D9 TSLP TSLP Thymic stromal lymphopoietin
Q96RP3 UCN2 UCN2 SRP URP Urocortin-2 (Stresscopin-related
peptide) (Urocortin II) (Ucn II)
(Urocortin-related peptide)
Q9BZM5 N2DL2 ULBP2 N2DL2 RAET1H
UNQ463/PRO791
NKG2D ligand 2 (N2DL-2)
(NKG2DL2) (ALCAN-alpha)
(Retinoic acid early transcript 1H)
(UL16-binding protein 2)
P01282 VIP VIP VIP peptides [Cleaved into:
Intestinal peptide PHV-42
(Peptide histidine valine 42);
Intestinal peptide PHM-27
216
(Peptide histidine methioninamide
27); Vasoactive intestinal peptide
(VIP) (Vasoactive intestinal
polypeptide)]
Q8IW00 VSTM4 VSTM4 C10orf72 V-set and transmembrane domain-
containing protein 4 [Cleaved into:
Peptide Lv]
P47992 XCL1 XCL1 LTN SCYC1 Lymphotactin (ATAC) (C motif
chemokine 1) (Cytokine SCM-1)
(Lymphotaxin) (SCM-1-alpha)
(Small-inducible cytokine C1)
(XC chemokine ligand 1)
Q9UBD3 XCL2 XCL2 SCYC2 Cytokine SCM-1 beta (C motif
chemokine 2) (XC chemokine
ligand 2)
P04217 A1BG A1BG Alpha-1B-glycoprotein (Alpha-1-
B glycoprotein)
Q86SQ3 AGRE4 ADGRE4P EMR4 EMR4P
GPR127 PGR16
Putative adhesion G protein-
coupled receptor E4P (EGF-like
module receptor 4) (EGF-like
module-containing mucin-like
hormone receptor-like 4) (G-
protein coupled receptor 127) (G-
protein coupled receptor PGR16)
Q92485 ASM3B SMPDL3B ASML3B Acid sphingomyelinase-like
phosphodiesterase 3b (ASM-like
phosphodiesterase 3b) (EC 3.1.4.-)
Proteins functionally related to energy production and metabolism
Q96PD5 PGRP2 PGLYRP2 PGLYRPL PGRPL
UNQ3103/PRO10102
N-acetylmuramoyl-L-alanine
amidase (EC 3.5.1.28)
(Peptidoglycan recognition protein
2) (Peptidoglycan recognition
protein long) (PGRP-L)
P01242 SOM2 GH2 Growth hormone variant (GH-V)
(Growth hormone 2) (Placenta-
specific growth hormone)
P22303 ACES ACHE Acetylcholinesterase (AChE) (EC
3.1.1.7)
Q15848 ADIPO ADIPOQ ACDC ACRP30
APM1 GBP28
Adiponectin (30 kDa adipocyte
complement-related protein)
(Adipocyte complement-related 30
kDa protein) (ACRP30)
(Adipocyte, C1q and collagen
domain-containing protein)
(Adipose most abundant gene
217
transcript 1 protein) (apM-1)
(Gelatin-binding protein)
Q9BRR6 ADPGK ADPGK PSEC0260 ADP-dependent glucokinase
(ADP-GK) (ADPGK) (EC
2.7.1.147) (RbBP-35)
P43652 AFAM AFM ALB2 ALBA Afamin (Alpha-albumin) (Alpha-
Alb)
P04745 AMY1 AMY1A AMY1; AMY1B
AMY1; AMY1C AMY1
Alpha-amylase 1 (EC 3.2.1.1)
(1,4-alpha-D-glucan
glucanohydrolase 1) (Salivary
alpha-amylase)
P19961 AMY2B AMY2B Alpha-amylase 2B (EC 3.2.1.1)
(1,4-alpha-D-glucan
glucanohydrolase 2B) (Carcinoid
alpha-amylase)
P02743 SAMP APCS PTX2 Serum amyloid P-component
(SAP) (9.5S alpha-1-glycoprotein)
[Cleaved into: Serum amyloid P-
component(1-203)]
P02652 APOA2 APOA2 Apolipoprotein A-II (Apo-AII)
(ApoA-II) (Apolipoprotein A2)
[Cleaved into: Proapolipoprotein
A-II (ProapoA-II); Truncated
apolipoprotein A-II
(Apolipoprotein A-II(1-76))]
P04114 APOB APOB
Q13790 APOF APOF Apolipoprotein F (Apo-F) (Lipid
transfer inhibitor protein) (LTIP)
Q9BPW4 APOL4 APOL4 Apolipoprotein L4
(Apolipoprotein L-IV) (ApoL-IV)
O95445 APOM APOM G3A NG20 HSPC336 Apolipoprotein M (Apo-M)
(ApoM) (Protein G3a)
Q9BUR5 MIC26 APOO FAM121B MIC23
MIC26 My025
UNQ1866/PRO4302
MICOS complex subunit MIC26
(Apolipoprotein O) (MICOS
complex subunit MIC23) (Protein
FAM121B)
P54793 ARSF ARSF Arylsulfatase F (ASF) (EC 3.1.6.-)
Q5FYB1 ARSI ARSI Arylsulfatase I (ASI) (EC 3.1.6.-)
Q5FYB0 ARSJ ARSJ UNQ372/PRO708 Arylsulfatase J (ASJ) (EC 3.1.6.-)
Q6UWY0 ARSK ARSK TSULF
UNQ630/PRO1246
Arylsulfatase K (ASK) (EC 3.1.6.-
) (Telethon sulfatase)
P06276 CHLE BCHE CHE1 Cholinesterase (EC 3.1.1.8)
(Acylcholine acylhydrolase)
(Butyrylcholine esterase) (Choline
esterase II) (Pseudocholinesterase)
218
P43251 BTD BTD Biotinidase (Biotinase) (EC
3.5.1.12)
P23280 CAH6 CA6 Carbonic anhydrase 6 (EC 4.2.1.1)
(Carbonate dehydratase VI)
(Carbonic anhydrase VI) (CA-VI)
(Salivary carbonic anhydrase)
(Secreted carbonic anhydrase)
P08218 CEL2B CELA2B ELA2B Chymotrypsin-like elastase family
member 2B (EC 3.4.21.71)
(Elastase-2B)
Q9UKY3 CES1P CES1P1 CES4 Putative inactive carboxylesterase
4 (Inactive carboxylesterase 1
pseudogene 1) (Placental
carboxylesterase 3) (PCE-3)
Q5XG92 EST4A CES4A CES8
UNQ440/PRO873
Carboxylesterase 4A (EC 3.1.1.-)
P11597 CETP CETP Cholesteryl ester transfer protein
(Lipid transfer protein I)
Q92496 FHR4 CFHR4 CFHL4 FHR4 Complement factor H-related
protein 4 (FHR-4)
Q9BZP6 CHIA CHIA Acidic mammalian chitinase
(AMCase) (EC 3.2.1.14) (Lung-
specific protein TSA1902)
Q6UXF7 CL18B CLEC18B MRLP1
UNQ306/PRO347
C-type lectin domain family 18
member B (Mannose receptor-like
protein 1)
Q8NCF0 CL18C CLEC18C MRLP3 C-type lectin domain family 18
member C (Mannose receptor-like
protein 3)
P04118 COL CLPS Colipase
Q6UWE3 COLL2 CLPSL2 C6orf126
UNQ3045/PRO9861
Colipase-like protein 2
Q96KN2 CNDP1 CNDP1 CN1 CPGL2
UNQ1915/PRO4380
Beta-Ala-His dipeptidase (EC
3.4.13.20) (CNDP dipeptidase 1)
(Carnosine dipeptidase 1)
(Glutamate carboxypeptidase-like
protein 2) (Serum carnosinase)
P15085 CBPA1 CPA1 CPA Carboxypeptidase A1 (EC
3.4.17.1)
P48052 CBPA2 CPA2 Carboxypeptidase A2 (EC
3.4.17.15)
Q8N4T0 CBPA6 CPA6 CPAH Carboxypeptidase A6 (EC 3.4.17.-
)
P15086 CBPB1 CPB1 CPB PCPB Carboxypeptidase B (EC 3.4.17.2)
(Pancreas-specific protein)
(PASP)
219
Q96IY4 CBPB2 CPB2 Carboxypeptidase B2 (EC
3.4.17.20) (Carboxypeptidase U)
(CPU) (Plasma carboxypeptidase
B) (pCPB) (Thrombin-activable
fibrinolysis inhibitor) (TAFI)
P16870 CBPE CPE Carboxypeptidase E (CPE) (EC
3.4.17.10) (Carboxypeptidase H)
(CPH) (Enkephalin convertase)
(Prohormone-processing
carboxypeptidase)
P17538 CTRB1 CTRB1 CTRB Chymotrypsinogen B (EC
3.4.21.1) [Cleaved into:
Chymotrypsin B chain A;
Chymotrypsin B chain B;
Chymotrypsin B chain C]
Q6PKH6 DR4L2 DHRS4L2 SDR25C3 Dehydrogenase/reductase SDR
family member 4-like 2 (EC 1.1.-.-
) (Short chain
dehydrogenase/reductase family
25C member 3)
A6NNS2 DRS7C DHRS7C SDR32C2 Dehydrogenase/reductase SDR
family member 7C (EC 1.1.-.-)
(Short-chain
dehydrogenase/reductase family
32C member 2)
Q9UGM3 DMBT1 DMBT1 GP340
O75356 ENTP5 ENTPD5 CD39L4 PCPH Ectonucleoside triphosphate
diphosphohydrolase 5 (NTPDase
5) (EC 3.6.1.6) (CD39 antigen-like
4) (ER-UDPase) (Guanosine-
diphosphatase ENTPD5) (GDPase
ENTPD5) (EC 3.6.1.42)
(Nucleoside diphosphatase)
(Uridine-diphosphatase ENTPD5)
(UDPase ENTPD5)
Q8NAU1 FNDC5 FNDC5 FRCP2 Fibronectin type III domain-
containing protein 5 (Fibronectin
type III repeat-containing protein
2) [Cleaved into: Irisin]
Q92820 GGH GGH Gamma-glutamyl hydrolase (EC
3.4.19.9) (Conjugase) (GH)
(Gamma-Glu-X carboxypeptidase)
P27352 IF GIF IFMH Gastric intrinsic factor (Intrinsic
factor) (IF) (INF)
Q6UWU2 GLB1L GLB1L UNQ229/PRO262 Beta-galactosidase-1-like protein
(EC 3.2.1.-)
220
Q96SL4 GPX7 GPX7 GPX6 UNQ469/PRO828 Glutathione peroxidase 7 (GPx-7)
(GSHPx-7) (EC 1.11.1.9) (CL683)
Q16661 GUC2B GUCA2B Guanylate cyclase activator 2B
[Cleaved into: Guanylate cyclase
C-activating peptide 2 (Guanylate
cyclase C-activating peptide II)
(GCAP-II); Uroguanylin (UGN)]
P81172 HEPC HAMP HEPC LEAP1
UNQ487/PRO1003
Hepcidin (Liver-expressed
antimicrobial peptide 1) (LEAP-1)
(Putative liver tumor regressor)
(PLTR) [Cleaved into: Hepcidin-
25 (Hepc25); Hepcidin-20
(Hepc20)]
P10997 IAPP IAPP Islet amyloid polypeptide
(Amylin) (Diabetes-associated
peptide) (DAP) (Insulinoma
amyloid peptide)
P01569 IFNA5 IFNA5 Interferon alpha-5 (IFN-alpha-5)
(Interferon alpha-61) (Interferon
alpha-G) (LeIF G)
Q6UW32 IGFL1 IGFL1 UNQ644/PRO1274 Insulin growth factor-like family
member 2
Q6UWQ7 IGFL2 IGFL2 UNQ645/PRO1275 Insulin growth factor-like family
member 3
Q6UXB1 IGFL3 IGFL3 UNQ483/PRO982
P01308 INS INS Insulin [Cleaved into: Insulin B
chain; Insulin A chain]
P04180 LCAT LCAT Phosphatidylcholine-sterol
acyltransferase (EC 2.3.1.43)
(Lecithin-cholesterol
acyltransferase) (Phospholipid-
cholesterol acyltransferase)
Q6JVE5 LCN12 LCN12 Epididymal-specific lipocalin-12
Q5VSP4 LC1L1 LCN1P1 LCN1L1 Putative lipocalin 1-like protein 1
(Lipocalin 1-like pseudogene 1)
Q8WX39 LCN9 LCN9 Epididymal-specific lipocalin-9
(MUP-like lipocalin)
P48357 LEPR LEPR DB OBR
P11150 LIPC LIPC HTGL Hepatic triacylglycerol lipase (HL)
(Hepatic lipase) (EC 3.1.1.3)
(Lipase member C)
P07098 LIPG LIPF Gastric triacylglycerol lipase (GL)
(Gastric lipase) (EC 3.1.1.3)
Q9Y5X9 LIPE LIPG UNQ387/PRO719 Endothelial lipase (EC 3.1.1.3)
(Endothelial cell-derived lipase)
(EDL) (EL)
221
Q8WWY
8
LIPH LIPH LPDLR MPAPLA1
PLA1B
Lipase member H (LIPH) (EC
3.1.1.-) (LPD lipase-related
protein) (Membrane-associated
phosphatidic acid-selective
phospholipase A1-alpha) (mPA-
PLA1 alpha) (Phospholipase A1
member B)
Q6XZB0 LIPI LIPI LPDL PRED5 Lipase member I (LIPI) (EC
3.1.1.-) (Cancer/testis antigen 17)
(CT17) (LPD lipase) (Membrane-
associated phosphatidic acid-
selective phospholipase A1-beta)
(mPA-PLA1 beta)
Q5VXJ0 LIPK LIPK LIPL2 Lipase member K (EC 3.1.1.-)
(Lipase-like abhydrolase domain-
containing protein 2)
Q5VYY2 LIPM LIPM LIPL3 Lipase member M (EC 3.1.1.-)
(Lipase-like abhydrolase domain-
containing protein 3)
Q5VXI9 LIPN LIPN LIPL4 Lipase member N (EC 3.1.1.-)
(Lipase-like abhydrolase domain-
containing protein 4)
P06858 LIPL LPL LIPD Lipoprotein lipase (LPL) (EC
3.1.1.34)
Q9Y2E5 MA2B2 MAN2B2 KIAA0935
Q08431 MFGM MFGE8
A6NG13 MGT4D MGAT4D Alpha-1,3-mannosyl-glycoprotein
4-beta-N-
acetylglucosaminyltransferase-like
protein MGAT4D (EC 2.4.1.-)
O96009 NAPSA NAPSA NAP1 NAPA Napsin-A (EC 3.4.23.-) (Aspartyl
protease 4) (ASP4) (Asp 4)
(Napsin-1) (TA01/TA02)
P61916 NPC2 NPC2 HE1 Epididymal secretory protein E1
(Human epididymis-specific
protein 1) (He1) (Niemann-Pick
disease type C2 protein)
P0DJD8 PEPA3 PGA3 Pepsin A-3 (EC 3.4.23.1)
(Pepsinogen-3)
P0DJD7 PEPA4 PGA4 Pepsin A-4 (EC 3.4.23.1)
(Pepsinogen-4)
P0DJD9 PEPA5 PGA5 Pepsin A-5 (EC 3.4.23.1)
(Pepsinogen-5)
P20142 PEPC PGC Gastricsin (EC 3.4.23.3)
(Pepsinogen C)
222
O43692 PI15 PI15 CRISP8 P25TI Peptidase inhibitor 15 (PI-15) (25
kDa trypsin inhibitor) (p25TI)
(Cysteine-rich secretory protein 8)
(CRISP-8) (SugarCrisp)
Q53H76 PLA1A PLA1A NMD PSPLA1 Phospholipase A1 member A (EC
3.1.1.-) (Phosphatidylserine-
specific phospholipase A1) (PS-
PLA1)
H3BRW4 H3BRW
4
PLA2G10 hCG_1746186 Phospholipase A(2) (EC 3.1.1.4)
Q8NCC3 PAG15 PLA2G15 LYPLA3
UNQ341/PRO540
Group XV phospholipase A2 (EC
2.3.1.-) (1-O-acylceramide
synthase) (ACS) (LCAT-like
lysophospholipase) (LLPL)
(Lysophospholipase 3)
(Lysosomal phospholipase A2)
(LPLA2)
P04054 PA21B PLA2G1B PLA2 PLA2A
PPLA2
Phospholipase A2 (EC 3.1.1.4)
(Group IB phospholipase A2)
(Phosphatidylcholine 2-
acylhydrolase 1B)
Q5R387 PA2GC PLA2G2C Putative inactive group IIC
secretory phospholipase A2
(Phosphatidylcholine 2-
acylhydrolase-like protein GIIC)
Q13093 PAFA PLA2G7 PAFAH Platelet-activating factor
acetylhydrolase (PAF
acetylhydrolase) (EC 3.1.1.47) (1-
alkyl-2-
acetylglycerophosphocholine
esterase) (2-acetyl-1-
alkylglycerophosphocholine
esterase) (Group-VIIA
phospholipase A2) (gVIIA-PLA2)
(LDL-associated phospholipase
A2) (LDL-PLA(2)) (PAF 2-
acylhydrolase)
P55058 PLTP PLTP Phospholipid transfer protein
(Lipid transfer protein II)
P16233 LIPP PNLIP Pancreatic triacylglycerol lipase
(PL) (PTL) (Pancreatic lipase)
(EC 3.1.1.3)
P54315 LIPR1 PNLIPRP1 PLRP1 Inactive pancreatic lipase-related
protein 1 (PL-RP1)
223
P54317 LIPR2 PNLIPRP2 PLRP2 Pancreatic lipase-related protein 2
(PL-RP2) (EC 3.1.1.26) (EC
3.1.1.3) (Galactolipase)
Q17RR3 LIPR3 PNLIPRP3 Pancreatic lipase-related protein 3
(PL-RP3) (EC 3.1.1.3)
P27169 PON1 PON1 PON Serum paraoxonase/arylesterase 1
(PON 1) (EC 3.1.1.2) (EC
3.1.1.81) (EC 3.1.8.1) (Aromatic
esterase 1) (A-esterase 1) (K-45)
(Serum aryldialkylphosphatase 1)
Q15166 PON3 PON3 Serum paraoxonase/lactonase 3
(EC 3.1.1.2) (EC 3.1.1.81) (EC
3.1.8.1)
Q13162 PRDX4 PRDX4 Peroxiredoxin-4 (EC 1.11.1.15)
(Antioxidant enzyme AOE372)
(AOE37-2) (Peroxiredoxin IV)
(Prx-IV) (Thioredoxin peroxidase
AO372) (Thioredoxin-dependent
peroxide reductase A0372)
P04070 PROC PROC Vitamin K-dependent protein C
Q9BQR3 PRS27 PRSS27 MPN
UNQ1884/PRO4327
Serine protease 27 (EC 3.4.21.-)
(Marapsin) (Pancreasin)
P07602 SAP PSAP GLBA SAP1 Prosaposin (Proactivator
polypeptide) [Cleaved into:
Saposin-A (Protein A); Saposin-B-
Val; Saposin-B (Cerebroside
sulfate activator) (CSAct)
(Dispersin) (Sphingolipid activator
protein 1) (SAP-1)
(Sulfatide/GM1 activator);
Saposin-C (A1 activator) (Co-
beta-glucosidase)
(Glucosylceramidase activator)
(Sphingolipid activator protein 2)
(SAP-2); Saposin-D (Component
C) (Protein C)]
Q6NUJ1 SAPL1 PSAPL1 Proactivator polypeptide-like 1
[Cleaved into: Saposin A-like;
Saposin B-Val-like; Saposin B-
like; Saposin C-like; Saposin D-
like]
Q9NRI6 PYY2 PYY2 Putative peptide YY-2 (Putative
peptide YY2)
P02753 RET4 RBP4 PRO2222 Retinol-binding protein 4 (Plasma
retinol-binding protein) (PRBP)
(RBP) [Cleaved into: Plasma
224
retinol-binding protein(1-182);
Plasma retinol-binding protein(1-
181); Plasma retinol-binding
protein(1-179); Plasma retinol-
binding protein(1-176)]
P05451 REG1A REG1A PSPS PSPS1 REG Lithostathine-1-alpha (Islet cells
regeneration factor) (ICRF) (Islet
of Langerhans regenerating
protein) (REG) (Pancreatic stone
protein) (PSP) (Pancreatic thread
protein) (PTP) (Regenerating islet-
derived protein 1-alpha) (REG-1-
alpha) (Regenerating protein I
alpha)
P35542 SAA4 SAA4 CSAA Serum amyloid A-4 protein
(Constitutively expressed serum
amyloid A protein) (C-SAA)
Q8IW75 SPA12 SERPINA12 Serpin A12 (OL-64) (Visceral
adipose tissue-derived serine
protease inhibitor) (Vaspin)
(Visceral adipose-specific serpin)
Q9HAT2 SIAE SIAE YSG2 Sialate O-acetylesterase (EC
3.1.1.53) (H-Lse) (Sialic acid-
specific 9-O-acetylesterase)
P17405 ASM SMPD1 ASM Sphingomyelin phosphodiesterase
(EC 3.1.4.12) (Acid
sphingomyelinase) (aSMase)
Q92484 ASM3A SMPDL3A ASML3A Acid sphingomyelinase-like
phosphodiesterase 3a (ASM-like
phosphodiesterase 3a) (EC 3.1.4.-)
P00995 ISK1 SPINK1 PSTI Serine protease inhibitor Kazal-
type 1 (Pancreatic secretory
trypsin inhibitor) (Tumor-
associated trypsin inhibitor)
(TATI)
P20061 TCO1 TCN1 TC1 Transcobalamin-1 (TC-1)
(Haptocorrin) (HC) (Protein R)
(Transcobalamin I) (TC I) (TCI)
P20062 TCO2 TCN2 TC2 Transcobalamin-2 (TC-2)
(Transcobalamin II) (TC II) (TCII)
P01222 TSHB TSHB Thyrotropin subunit beta
(Thyroid-stimulating hormone
subunit beta) (TSH-B) (TSH-beta)
(Thyrotropin beta chain)
(Thyrotropin alfa)
225
P02766 TTHY TTR PALB Transthyretin (ATTR)
(Prealbumin) (TBPA)
P55089 UCN1 UCN Urocortin
O95399 UTS2 UTS2 UNQ525/PRO1068 Urotensin-2 (Urotensin II) (U-II)
(UII)
Q765I0 UTS2B UTS2B URP UTS2D Urotensin-2B (Urotensin II-related
peptide) (Urotensin IIB) (U-IIB)
(UIIB) (Urotensin-2 domain-
containing protein)
Q13231 CHIT1 CHIT1 Chitotriosidase-1 (EC 3.2.1.14)
(Chitinase-1)
A5D8T8 CL18A CLEC18A MRLP2 C-type lectin domain family 18
member A (Mannose receptor-like
protein 2)
P04746 AMYP AMY2A Pancreatic alpha-amylase (PA)
(EC 3.2.1.1) (1,4-alpha-D-glucan
glucanohydrolase)
P80188 NGAL LCN2 HNL NGAL Neutrophil gelatinase-associated
lipocalin (NGAL) (25 kDa alpha-
2-microglobulin-related subunit of
MMP-9) (Lipocalin-2) (Oncogene
24p3) (Siderocalin LCN2) (p25)
Q9NZK5 CECR1 CECR1 ADA2 ADGF IDGFL Adenosine deaminase CECR1 (EC
3.5.4.4) (Cat eye syndrome critical
region protein 1)
Proteins of unknown function
Q6P093 ADCL2 AADACL2 Arylacetamide deacetylase-like 2
(EC 3.1.1.-)
Q5VST6 AB17B ABHD17B C9orf77 FAM108B1
CGI-67
Protein ABHD17B (EC 3.-.-.-)
(Alpha/beta hydrolase domain-
containing protein 17B)
(Abhydrolase domain-containing
protein 17B)
O43827 ANGL7 ANGPTL7 CDT6
UNQ313/PRO356
Angiopoietin-related protein 7
(Angiopoietin-like factor)
(Angiopoietin-like protein 7)
(Cornea-derived transcript 6
protein)
P0DMC3 ELA APELA ELA TDL Apelin receptor early endogenous
ligand (Protein Elabela) (Protein
Toddler)
Q86Y30 BAGE2 BAGE2 B melanoma antigen 2
(Cancer/testis antigen 2.2) (CT2.2)
Q86Y29 BAGE3 BAGE3 B melanoma antigen 3
(Cancer/testis antigen 2.3) (CT2.3)
226
Q86Y28 BAGE4 BAGE4 MLL3P B melanoma antigen 4
(Cancer/testis antigen 2.4) (CT2.4)
Q7Z5Y6 BMP8A BMP8A Bone morphogenetic protein 8A
(BMP-8A)
P34820 BMP8B BMP8B BMP8 Bone morphogenetic protein 8B
(BMP-8) (BMP-8B) (Osteogenic
protein 2) (OP-2)
Q86YQ2 LATH BPIFA4P BASE LATH Putative BPIFA4P protein (BPI
fold containing family A, member
4, pseudogene) (Breast cancer and
salivary gland-expressed protein)
(Putative latherin)
Q8N4F0 BPIB2 BPIFB2 BPIL1 C20orf184
LPLUNC2 UNQ2489/PRO5776
BPI fold-containing family B
member 2
(Bactericidal/permeability-
increasing protein-like 1) (BPI-
like 1) (Long palate, lung and
nasal epithelium carcinoma-
associated protein 2) (RYSR)
P59827 BPIB4 BPIFB4 C20orf186 LPLUNC4 BPI fold-containing family B
member 4 (Ligand-binding protein
RY2G5) (Long palate, lung and
nasal epithelium carcinoma-
associated protein 4)
Q8NFQ5 BPIB6 BPIFB6 BPIL3 BPI fold-containing family B
member 6
(Bactericidal/permeability-
increasing protein-like 3)
Q8N8R5 CB069 C2orf69 UPF0565 protein C2orf69
P23435 CBLN1 CBLN1 Cerebellin-1 (Precerebellin)
[Cleaved into: Cerebellin (CER);
[des-Ser1]-cerebellin]
Q6UW01 CBLN3 CBLN3 UNQ755/PRO1486 Cerebellin-3
Q9NTU7 CBLN4 CBLN4 CBLNL1
UNQ718/PRO1382
Cerebellin-4 (Cerebellin-like
glycoprotein 1)
Q9H6E4 CC134 CCDC134 Coiled-coil domain-containing
protein 134
Q04900 MUC24 CD164 Sialomucin core protein 24
(MUC-24) (Endolyn) (Multi-
glycosylated core protein 24)
(MGC-24) (MGC-24v) (CD
antigen CD164)
Q5VXM1 CDCP2 CDCP2 CUB domain-containing protein 2
Q6NT32 EST5A CES5A CES7 Carboxylesterase 5A (EC 3.1.1.1)
(Carboxylesterase-like urinary
227
excreted protein homolog)
(Cauxin)
P01215 GLHA CGA Glycoprotein hormones alpha
chain (Anterior pituitary
glycoprotein hormones common
subunit alpha)
(Choriogonadotropin alpha chain)
(Chorionic gonadotrophin subunit
alpha) (CG-alpha) (Follicle-
stimulating hormone alpha chain)
(FSH-alpha) (Follitropin alpha
chain) (Luteinizing hormone alpha
chain) (LSH-alpha) (Lutropin
alpha chain) (Thyroid-stimulating
hormone alpha chain) (TSH-alpha)
(Thyrotropin alpha chain)
H9KV56 H9KV56 CGB2 Choriogonadotropin subunit beta
variant 2
P0DN87 CGB7 CGB7 Choriogonadotropin subunit beta 7
Q9BWS9 CHID1 CHID1 GL008 PSEC0104
SB139
Chitinase domain-containing
protein 1 (Stabilin-1-interacting
chitinase-like protein) (SI-CLP)
Q9Y6N3 CLCA3 CLCA3P CLCA3 Calcium-activated chloride
channel regulator family member
3 (Calcium-activated chloride
channel family member 3)
(hCLCA3)
A2RUU4 COLL1 CLPSL1 C6orf127 Colipase-like protein 1
Q9UI42 CBPA4 CPA4 CPA3
UNQ694/PRO1339
Carboxypeptidase A4 (EC 3.4.17.-
) (Carboxypeptidase A3)
Q8WXQ8 CBPA5 CPA5 Carboxypeptidase A5 (EC 3.4.17.-
)
P15169 CBPN CPN1 ACBP Carboxypeptidase N catalytic
chain (CPN) (EC 3.4.17.3)
(Anaphylatoxin inactivator)
(Arginine carboxypeptidase)
(Carboxypeptidase N polypeptide
1) (Carboxypeptidase N small
subunit) (Kininase-1) (Lysine
carboxypeptidase) (Plasma
carboxypeptidase B) (Serum
carboxypeptidase N) (SCPN)
P22792 CPN2 CPN2 ACBP Carboxypeptidase N subunit 2
(Carboxypeptidase N 83 kDa
chain) (Carboxypeptidase N large
subunit) (Carboxypeptidase N
228
polypeptide 2) (Carboxypeptidase
N regulatory subunit)
P24387 CRHBP CRHBP CRFBP Corticotropin-releasing factor-
binding protein (CRF-BP) (CRF-
binding protein) (Corticotropin-
releasing hormone-binding
protein) (CRH-BP)
P07498 CASK CSN3 CASK CSN10 CSNK Granulocyte colony-stimulating
factor (G-CSF) (Pluripoietin)
(Filgrastim) (Lenograstim)
P09228 CYTT CST2 Cystatin-SA (Cystatin-2)
(Cystatin-S5)
Q5W188 CST9P CST9LP1 Putative cystatin-9-like protein
CST9LP1 (Cystatin-9-like
pseudogene 1)
Q6UWP2 DHR11 DHRS11 SDR24C1
UNQ836/PRO1774
Dehydrogenase/reductase SDR
family member 11 (EC 1.1.-.-)
(Short-chain
dehydrogenase/reductase family
24C member 1)
P24855 DNAS1 DNASE1 DNL1 DRNI Deoxyribonuclease-1 (EC
3.1.21.1) (Deoxyribonuclease I)
(DNase I) (Dornase alfa)
Q7Z5P4 DHB13 HSD17B13 SCDR9 SDR16C3
HMFN0376 UNQ497/PRO1014
17-beta-hydroxysteroid
dehydrogenase 13 (17-beta-HSD
13) (EC 1.1.-.-) (Short chain
dehydrogenase/reductase family
16C member 3) (Short-chain
dehydrogenase/reductase 9)
Q8N0V4 LGI2 LGI2 KIAA1916 LGIL2 Leucine-rich repeat LGI family
member 2 (LGI1-like protein 2)
(Leucine-rich glioma-inactivated
protein 2)
Q6UXI9 NPNT NPNT EGFL6L POEM
UNQ295/PRO334
Nephronectin (Preosteoblast EGF-
like repeat protein with MAM
domain) (Protein EGFL6-like)
Q9GZU5 NYX NYX CLRP Nyctalopin
O95897 NOE2 OLFM2 NOE2 Noelin-2 (Olfactomedin-2)
Q96PB7 NOE3 OLFM3 NOE3
UNQ1924/PRO4399
Noelin-3 (Olfactomedin-3)
(Optimedin)
A8MZH6 OOSP1 OOSP1 Putative oocyte-secreted protein 1
homolog
Q3ZCN5 OTOGL OTOGL C12orf64
Q7RTZ1 OVCH2 OVCH2 OVTN Ovochymase-2 (EC 3.4.21.-)
(Oviductin)
229
Q8WXA2 PATE1 PATE1 PATE Prostate and testis expressed
protein 1
Q6UY27 PATE2 PATE2 C11orf38
UNQ3112/PRO10144
Prostate and testis expressed
protein 2 (PATE-like protein M)
(PATE-M)
B3GLJ2 PATE3 PATE3 Prostate and testis expressed
protein 3 (Acrosomal vesicle
protein HEL-127) (PATE-like
protein DJ) (PATE-DJ)
E5RFR1 E5RFR1 PENK Proenkephalin-A (Fragment)
Q9HBJ0 PLAC1 PLAC1 Placenta-specific protein 1
Q6GTS8 P20D1 PM20D1 Probable carboxypeptidase
PM20D1 (EC 3.4.17.-) (Peptidase
M20 domain-containing protein 1)
Q9BSG0 PADC1 PRADC1 C2orf7 PAP21 UNQ833/PRO1760
Q9P1C3 YN010 PRO2829 Putative uncharacterized protein
PRO2829
A6NIE9 PRS29 PRSS29P ISP2 Putative serine protease 29 (EC
3.4.21.-) (Implantation serine
proteinase 2-like protein) (ISP2-
like protein)
A4D1T9 PRS37 PRSS37 TRYX2 Probable inactive serine protease
37 (Probable inactive trypsin-X2)
A1L453 PRS38 PRSS38 MPN2 Serine protease 38 (EC 3.4.21.-)
(Marapsin-2)
Q7Z5A4 PRS42 PRSS42 TESSP2 Serine protease 42 (EC 3.4.21.-)
(Testis serine protease 2)
E7EML9 PRS44 PRSS44 TESSP4 Serine protease 44 (EC 3.4.21.-)
(Testis serine protease 4) (TESSP-
4)
A8MTI9 PRS47 PRSS47 Putative serine protease 47 (EC
3.4.21.-)
Q7RTY5 PRS48 PRSS48 ESSPL Serine protease 48 (EC 3.4.21.-)
(Epidermis-specific serine
protease-like protein)
Q6PEW0 PRS54 PRSS54 KLKBL4 Inactive serine protease 54
(Cancer/testis antigen 67) (CT67)
(Plasma kallikrein-like protein 4)
Q8IYP2 PRS58 PRSS58 TRY1 TRYX3
UNQ2540/PRO6090
Serine protease 58 (EC 3.4.21.4)
(Trypsin-X3)
Q5JQD4 PYY3 PYY3 Putative peptide YY-3 (Putative
peptide YY3) (PYY-III)
P00797 RENI REN Renin (EC 3.4.23.15)
(Angiotensinogenase)
230
P34096 RNAS4 RNASE4 RNS4 Ribonuclease 4 (RNase 4) (EC
3.1.27.-)
P11684 UTER SCGB1A1 CC10 CCSP UGB Uteroglobin (Clara cell
phospholipid-binding protein)
(CCPBP) (Clara cells 10 kDa
secretory protein) (CC10)
(Secretoglobin family 1A member
1) (Urinary protein 1) (UP-1)
(UP1) (Urine protein 1)
Q8TD33 SG1C1 SCGB1C1 Secretoglobin family 1C member
1 (Secretoglobin RYD5)
P0DMR2 SG1C2 SCGB1C2 Secretoglobin family 1C member
2
Q9HB40 RISC SCPEP1 RISC SCP1 MSTP034
UNQ265/PRO302
Retinoid-inducible serine
carboxypeptidase (EC 3.4.16.-)
(Serine carboxypeptidase 1)
Q9UK55 ZPI SERPINA10 ZPI
UNQ707/PRO1358
Protein Z-dependent protease
inhibitor (PZ-dependent protease
inhibitor) (PZI) (Serpin A10)
P08185 CBG SERPINA6 CBG Corticosteroid-binding globulin
(CBG) (Serpin A6) (Transcortin)
P05543 THBG SERPINA7 TBG Thyroxine-binding globulin
(Serpin A7) (T4-binding globulin)
P05120 PAI2 SERPINB2 PAI2 PLANH2 Plasminogen activator inhibitor 2
(PAI-2) (Monocyte Arg-serpin)
(Placental plasminogen activator
inhibitor) (Serpin B2) (Urokinase
inhibitor)
P0C7L1 ISK8 SPINK8 Serine protease inhibitor Kazal-
type 8
Q5DT21 ISK9 SPINK9 LEKTI2 Serine protease inhibitor Kazal-
type 9 (Lymphoepithelial Kazal-
type-related inhibitor 2)
P49223 SPIT3 SPINT3 Kunitz-type protease inhibitor 3
(HKIB9)
Q6UDR6 SPIT4 SPINT4 C20orf137 Kunitz-type protease inhibitor 4
P01266 THYG TG
Q9P2K2 TXD16 TXNDC16 KIAA1344
P07911 UROM UMOD Uromodulin (Tamm-Horsfall
urinary glycoprotein) (THP)
[Cleaved into: Uromodulin,
secreted form]
Q6UY13 YB003 UNQ5830/PRO19650/PRO1981
6
Putative uncharacterized protein
UNQ5830/PRO19650/PRO19816
231
Q9H1F0 WF10A WFDC10A C20orf146 WAP10 WAP four-disulfide core domain
protein 10A (Putative protease
inhibitor WAP10A)
Q8IUB3 WF10B WFDC10B WAP12 Protein WFDC10B
Q8NEX5 WFDC9 WFDC9 WAP9 Protein WFDC9
P60852 ZP1 ZP1 Zona pellucida sperm-binding
protein 1 (Zona pellucida
glycoprotein 1) (Zp-1) [Cleaved
into: Processed zona pellucida
sperm-binding protein 1]