Review MALDI-TOF in Leukemia’s Proteomics Studies

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Mass Spectrometry: A Powerful Method forMonitoring Various Type of Leukemia, EspeciallyMALDI-TOF in Leukemia’s Proteomics StudiesReview

Negin Fasih Ramandi , Mohammad Faranoush , Alireza Ghassempour &Hassan Y. Aboul-Enein

To cite this article: Negin Fasih Ramandi , Mohammad Faranoush , Alireza Ghassempour &Hassan Y. Aboul-Enein (2021): Mass Spectrometry: A Powerful Method for Monitoring VariousType of Leukemia, Especially MALDI-TOF in Leukemia’s Proteomics Studies Review, CriticalReviews in Analytical Chemistry, DOI: 10.1080/10408347.2021.1871844

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Mass Spectrometry: A Powerful Method for Monitoring Various Type ofLeukemia, Especially MALDI-TOF in Leukemia’s Proteomics Studies Review

Negin Fasih Ramandia, Mohammad Faranoushb, Alireza Ghassempoura, and Hassan Y. Aboul-Eneinc

aMedicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran; bPediatric Growth and Development ResearchCenter, Institute of Endocrinology, Iran University of Medical Sciences, Tehran, Iran; cPharmaceutical and Medicinal Chemistry Department,Pharmaceutical and Drug Industries Research Division, National Research Center, Cairo, Egypt

ABSTRACTRecent success in studying the proteome, as a source of biomarkers, has completely changed ourunderstanding of leukemia (blood cancer). The identification of differentially expressed proteins,such as relapse and drug resistance proteins involved in leukemia by using various ionization sour-ces and mass analyzers of mass spectrometry techniques, has helped scientists find better diagno-sis, prognosis, and treatment strategies. With the aid of this powerful analytical technique, we caninvestigate the qualification/quantification of proteins, protein-protein interactions, post-transla-tional modifications, and find the correlation between proteins and their genes with the hope offinding the missing parts of the successful therapy puzzle. In this review, we followed different MSsources and analyzers which used for monitoring various type of leukemia, then focused onMALDI-TOF MS as a quick and reliable method for studying proteins. Due to several review pub-lished for other techniques, the present review is the first work in this field. Also, by classifyingmore than 400 proteins, we have found 42 proteins are involved in two or three different stagesof leukemia. Finally, we have suggested six specific biomarkers for AML, one for ALL, three bio-markers with a role in the etiology of leukemia and 13 markers with the potential for fur-ther studies.

KEYWORDSMALDI-TOF MS; ESI MS;leukemia; proteo-mics; biomarker

Introduction

Leukemia is a malignancy resulting from the neoplastic pro-liferation of hematopoietic or lymphoid cells, leading to thedisruption of the normal bone marrow function and to bonemarrow failure. Depending on its presentation and naturalhistory, leukemia is divided into two categories: acute andchronic. In untreated acute leukemia, the chance of survivalwill be weeks or months and in chronic leukemia, thechance of survival will be months or years. On the otherhand, based on the type of leukemia precursors, it can beclassified as lymphoid, myeloid, or mixed lineage (bipheno-typic or bilineage) leukemia. Mixed lineage leukemia usuallyshows both lymphoid and myeloid differentiation.[1–3] Acutelymphoblastic leukemia (ALL), acute myeloid leukemia(AML), chronic lymphoblastic leukemia (CLL), chronic mye-loid leukemia (CML), and mixed lineage leukemia (MLL)are all defined as different types of leukemia. The classifica-tion of leukemia must be distinguished separately by accur-ate tests to give the correct diagnosis. In addition, an earlydiagnosis and the accurate classification of leukemia are keyfactors for an accurate treatment and prognosis. In thisregard, using modern techniques with high accuracy andsensitivity will be useful in the early diagnosis as well as the

prognosis and provide a comprehensive assessment of leuke-mia’s mechanism for scientists who work in the drug discov-ery field.[4]

The modernization of human society has been led toincreasing number of cancer patients.[5] Therefore, treatingit more effectively and with less toxicity is the aim of mod-ern drug discovery studies. Proposing natural products suchas curcumin I-based ligands[6] as an anti-cancer drug, orsynthesizing glutamic acid and its derivatives,[7,8] thalido-mide and its complexes,[9,10] metal complexes,[11] N-substi-tuted rhodanines (RD1-7),[12] naturally occurring and/orsynthetic heterocyclic compounds,[13–15] or even investiga-tion of carcinogenesis and toxicity of enantioselective metab-olisms of the chiral compounds[16] are some excellentexamples of recent efforts in this field. Also, there is a hopefor cancer treatment by nanoparticles- based anti-cancerdrugs due to their effectiveness, low side effects and targetedaction on only cancer cells.[17–20] For further information,please read the valuable review article with the focused oncapillary electrophoresis as an inexpensive and low detectionlimit technique for evaluating proposed anti-can-cer drugs.[21]

Understanding carcinogenesis as well as finding a betterway to cure patients was the goal of many clinical studies.

CONTACT Alireza Ghassempour [email protected] Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran 1983969411,Iran; Hassan Y. Aboul-Enein [email protected] Pharmaceutical and Medicinal Chemistry Department, Pharmaceutical and Drug Industries ResearchDivision, National Research Center, Tahrir Street, Cairo 12622, Egypt� 2021 Taylor & Francis Group, LLC

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However, scientists are aware of the major role of DNAmethylation, genes mutations, and post-translational modifi-cations (PTMs) of proteins (or glycoproteins) that areimportant contributors to cancer.[22,23] Researchers are look-ing to find the best methods for early diagnosis, tumor pre-vention, prognosis, and drug discovery.[24] To this end, massspectrometry (MS) has revolutionized science and com-pletely changed our understanding of cancer.[25]

MS has undoubtedly become an important and necessarytool in proteomics studies and has completely revolutionizedthe modern clinical diagnosis and biological sciences.[26–29]

This powerful analytical technique involves qualitative andquantitative assessments of biomolecules that rely on meas-uring mass-to-charge ratios (m/z) of ions with high specifi-city and accuracy.[30,31] Several reports based on massspectrometry for monitoring of leukemia, inhibitors of leu-kemia, and its mechanism have been published.Development of ionization techniques such as electrospray(ESI) to nanospray and introducing matrix assisted laserdesorption ionization (MALDI) caused to find new bio-markers for different types of leukemia. High resolutionmass analyzer such as orbitrap, with rapid analyzing power,and time-of-flight (TOF), with longer ion pathway andhigher response detector, helped to better follow any proteinchanges in leukemia. In Table 1, the application of thesetwo type of ionization methods (MALDI[32–56] and ESI (inresent 5 years)[57–93]) in different type of leukemiaare summarized.

Several reviews based on ESI in leukemia studies havebeen published. However, there is not any comprehensivereview based on MALDI studies in leukemia. Matrix-assistedlaser-desorption ionization time-of-flight mass spectrometry(MALDI-TOF MS) has the ability to rapidly detect largebiomolecules in trace amounts. MALDI-TOF MS is the bestcandidate for clinical research purposes in the area of bio-macromolecules due to its economical high-throughput ana-lysis and ease of use.[94–98]

The goal of this review is to familiarize investigators inthe biological and medical fields with the applications ofMALDI-TOF MS in proteomics research associated withleukemia. We have summarized these results based on pro-tein identification related to leukemia in different stages, viz.classification, prognosis, effect of leukemia medications onproteins, proteins related to drug resistance, and relapse.Also, by classifying hundreds of proteins, we have found 42proteins are involved in two or three different stages of leu-kemia. Finally, we have suggested six specific biomarkers forAML, one for ALL, three biomarkers with a role in the eti-ology of leukemia and 13 markers with the potential for fur-ther studies.

MALDI-TOF MS technique

In MALDI-TOF MS, to produce ionized molecules, the sam-ple is bombarded by laser light. MALDI is a soft ionizationmethod, for prevention of molecule fragmentation duringthe ionization procedure. The sample is premixed anddiluted with a specific matrix consisting of small organic

compounds and allowed to dry. The matrix must havestrong resonance absorption at the laser wavelength used.When the laser energy is absorbed by the matrix, the analyteacquires translational energy without being internallyexcited. Then, based on the ion mass and charge, in the ion-ization source, an electric field is accelerated by the ions. Inother words, the velocities of the ions that have beenachieved during the acceleration depend on their mass andcharge. Then, they enter a field-free drift tube where there isa detector at its end. The time needed to pass through theDT depends on the ion velocity. The time of flight (TOF) ofions will be recorded when they hit the detector, the massto charge ratio of the ions is calculated, and the mass spec-tra of the analyte will be obtained.[31,99,100] If the fragmenta-tion of the MALDI-generated precursor ions is needed, theMALDI ion source should couple to one of two differentmass analyzers, viz. Q-TOF or TOF-TOF. In these instru-ments, the selected ions of a particular m/z in the first massanalyzer (Q or TOF) fragmented in a collision cell, which isplaced between a Q mass filter and a TOF analyzer (in caseof Q-TOF) or between two TOF mass analyzers (in the caseof TOF-TOF), and the fragment ion masses are “read out”by a TOF analyzer.[101] Improvements in sensitivity, massaccuracy, and resolution of MALDI-TOF,[102] as well as itsability to rapidly detect large biomolecules in trace amountsfor facile and cost-effective analyses have made it the bestcandidate for qualitative and quantitative analysisof biomarkers.

Proteomics research in leukemia

Proteomics is the qualification and quantification of all theproteins of a proteome, including expression, interactions,PTMs, and cellular localization.[101,103] In this regard, thereare two major strategies: bottom-up and top-down. The bot-tom-up approach relies on the digestion of the protein by aprotease and analysis of the peptides formed prior to massspectrometry analysis.[103] In bottom-up proteomics, pro-teins are identified by using two major protein identificationmethods, viz. peptide mass fingerprinting (PMF) byMALDI-MS and/or sequencing by tandem mass spectrom-etry (by LC-MS/MS or MALDI-TOF/TOF).[104] In otherwords, in bottom-up proteomics, the intact protein will beanalyzed indirectly through its formed peptides. In contrast,top-down proteomics is the study of intact proteins withoutdigestion. Thus, the intact protein mass information, theprotein isoform identification, sequence characterization,and PTMs provide valuable information about theproteome.[105,106]

Protein markers related to classification of leukemiabased on proteomics approaches

The French-American-British (FAB) classification system isa common method to classify leukemia based on morph-ology and cytochemistry assessment of cases of suspectedleukemia.[107] The FAB system classifies ALL and AML intodiscrete types designated L1-L3 and M0-M7, respectively.

2 N. FASIH RAMANDI ET AL.

Table 1. Application of MALDI (from the first publication in 2003 until now) and ESI (in resent 5 years).

Type of Leukemia Ionization Technique Inlet Mass Analyzer Target of Analysis Refs.

CLL MALDI 1D gel electrophoresis,In solution digestion,Zip-tip

TOF, Voyager STR,Applied Biosystems

cell-surfacemembrane proteins

[32]

nanospray Nano- LC Q-TOFALL, AML MALDI 2D-gel electrophoresis TOF Classification of acute

leukemia cell

[33]

ESI Q-TOFCML MALDI 2D-gel electrophoresis TOF, Bruker effects of interferon-alpha

(IFN-a) treatment on(CML)-derived K562 cells

[34]

CML MALDI 2D- gel electrophoresis TOF, Bruker Identification of leukemia-associated antigens

[35]

AML MALDI 2D- gel electrophoresis TOF, Bruker interleukin 6-induceddifferentiationin mouse myeloidleukemia cells

[36]

ESI Q-TOF, MicromassAML MALDI 2D- gel electrophoresis TOF, Bruker Identification of differentially

expressed proteins in themulti-drug resistant inHL-60/DOX and cell lineHL-60 cell lines

[37]

CML MALDI 2D- gel electrophoresis TOF,Applied Biosystems

molecular changes inchronic phase

[38]

AML MALDI 2D- gel electrophoresis TOF, Bruker Identification of earlybiomarkers andtherapeutic targets

[39]

AML MALDI 2D- gel electrophoresis TOF, Bruker Finding serum biomarkers forcomplete remission

[122]

ESI Nano-LC Q-TOF, WatersALL,

AMLMALDI bound peptides from

washing the copper-chelatedmagnetic beads

TOF, Bruker Finding biomarker fordiagnosis and minimalresidualdisease assessment

[40]

nanoESI FT-ICR, BrukerAML MALDI 2D- gel electrophoresis TOF, Bruker Investigating the

progression ofmyelodysplastic syndrome(MDS) to AML

[41]

CML MALDI 2D- gel electrophoresis TOF, Bruker Acetylome andphosphoproteomemodifications in imatinibresistant CML cells treatedwith valproic acid

[42]

Acute leukemia MALDI 2D- Differential in gelelectrophoresis

TOF- TOF, Bruker Identification of over-expressed proteins inrelapsed/refractoryacute leukemia

[43]

ALL MALDI 2D- gel electrophoresis TOF-TOF,Applied Biosystems

Identification of prognosticprotein biomarkers

[44]

ALL,AML, HbEb-thalassemia

MALDI 2D- gel electrophoresis TOF-TOF,Applied Biosystems

Fractional precipitation ofplasma proteome fordetection of differentiallyregulated proteins

[45]

AML MALDI 2D- gel electrophoresis TOF-TOF, Bruker Identification of differentiallyexpressed proteins in AMLcells treated with DNMTinhibitors azacitidineand decitabine

[46]

ALL MALDI 2D- gel electrophoresis TOF, GECompany

Identification of differentialproteins between ALL andhealthy children

[47]

AML MALDI 2D- gel electrophoresis TOF Prediction resultsof induction therapy inAML patients by level ofEsterase D and gamma1 actin

[48]

AML MALDI Elution from weakcation exchangemagnetic beads

TOF, Bruker Identification of potentiallybiomarkers forminimal residualdisease assessment

[49]

ESI Nano-LC LTQ Obitrap XL massspectrometer,Thermo Fisher

(continued)

CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY 3

Table 1. Continued.


ALL MALDI 2D- gel electrophoresis TOF-TOF, AppliedBiosystems

Defining potential diagnosticand/or therapeutic markers

[50]

CLL MALDI 2D-nano� LC TOF-TOF,Applied Biosystems

Identification of proteinsrelevant to CLL

[51]

CML MALDI 2D- gel electrophoresis TOF,Applied Biosystems

Identification of differentialproteins in respond todoxorubicin resistancecell lines

[52]

Nano-ESI LC Q-TOF,Applied Biosystems

AML ESI LCandUPLC

Quadrupole MS, SciexandQTrap 5500,AB Sciex

Vosaroxin and its metabolites [57]

AML ESI LC andElectrophoresis

QQQa Single cell proteomics [58]

AML ESIMALDI

LC QQQ,TOF, BrukerandOrbitrap (QExactive),Thermo Fisher

Rev: Comparison FASPb filterand metal affinityenrichment forphosphopeptides

[59]

AML ESI, SELDIc and MALDI 1D-Page, In solutiondigestion, Zip-tipand LC

TOF, OrbitrapandQQQ

Rev: Proteome profiling,phospho-signaling andquantitativeProteomics

[60]

CLL Easy-Spray nanoflow nano-UPLC Orbitrap Velos Prohybrid,Thermo Fisher

Biomarkers fordisease prognosis

[61]

CLL nanospray nanoUPLC LTQ-Orbitrap Velos,Thermo Fisher

Combination of affinity(antibody microarray) andMS/MS for cellsignaling pathways

[62]

CML ESI LC QQQ API3000, AB Sciex Detectionof Nilotinib inHuman Plasma

[63]

Mouse leukemia cell ESI Electrophoresis andcapillary LC

LTQ-Orbitrap Velos,Thermo Finnigan

Permeable Probe foridentification proteins inlysosome of Tumor

[64]

AML nanospray nanoLC OrbitrapVelos, ThermoElectron

ETV6and IKZF1 as newregulators of anERG-driven

[65]

AML MALDI, ESI and CId ESI TOF, quadrupole, iontrap and Orbitrap

Rev: Leukemiadrug discovery

[66]

Leukemia MALDI 2D-gel electrophoresis TOF Rev: Differentiation ofleukemia cells bytranscriptomics andquantitativeproteomics (SILACe)

[67]

CML nanospray LC and nanoLC LTQ Orbitrap XL,Thermo Fisher

Book: Chronic MyelogenousLeukemia, Chapter 12Quantitative proteomics

[68]

AML MALDI 2D- gel electrophoresis TOF-TOF, Bruker Identification of differentialproteins between AML andhealthy plasma samples

[53]

ALL MALDI 2D- gel electrophoresis TOF, Bruker Identification of the bloodproteins by the methodsof MALDI-TOF mass

[54]

ALL nanospray nanoLC Orbitrap Q Exactive,Thermo Fisher

Label-freequantitative proteomics

[69]

AML nanospray nanoUPLC Orbitrap Q Exactive,Thermo Fisher

Proteomics andtranscriptomics forChimericAntigen Receptor

[70]

CLL nanospray nanoUPLC Orbitrap Elite Velos Pro,Thermo Fisher

Quantitative proteomics forfollowing promotorof cancer

[71]

AML ESI LC QQQ, Waters Dried blood sport(DBS) monitoring

[72]

Leukemiainhibitory factor

nanospray nanoLC QTOF, Bruker Label free proteomics forfollowing inhibitory factor

[73]

AML nanospray LC LTQ-OrbitrapElite and

Proteomics profiling ofhuman monocytes

[74]

(continued)


Table 1. Continued.


LTQ-Orbitrap Velos,Thermo Fisher

CML nanospray Nano UPLC Orbitrap Q Exactive,Thermo Fischer

Differential signalingnetworks of Bcr–Abl p210and p190 kinases

[75]

AML nanospray nanoLC LTQ OrbitrapVelos, Thermo Fisher

Quantitative proteomics,SILAC, for regulatedproteins uponproteasome inhibition

[76]

AML MALDI PermethylatedGlycans resultedfrom PNGaseF treatment

TOF, Bruker Aberrant mannosylationprofile and FTX/miR-342/ALG3-axiscontribute todevelopment of drugresistance in AML

[55]

AML A signaling Boolean networkmodel to detect the mostrelevant proteins

[77]

CLL ESI LC LQT-Orbitrap Elite VelosPro, Thermo Fisher

Proteomics in theidentification of putativeCLL therapeutic targetsandreveals a subtype-independent proteinexpression signature

[78]

CML nanospray nanoUPLC Triple-TOF. AB Sciex Proteomics study ofregulation of functionaland structuralmitochondrial proteinsafter treatmentby curcumin

[79]

SRSF2 mutantleukemia cells

nanospray nanoLC Orbitrap Fusion massspectrometerThermo Fisher

Combination of proteomicsand trascriptomics(ProteomeGenerator) toidentification of non-canonical protein sequence

[80]

Graft-versusleukemiaactivity

ESI LC QQQ Proteomics of ST2 inhibitors [81]

AML nanospray nanoLC Q-Exactive HF,Thermo Fisher

CD34þ CD123þProgenitor Cells

[82]

CLL nanospray nanoLC Orbitrap Q Exactive,Thermo Fisher

Proteomics and metabolomicsof aging role

[83]

PML ESI nanoLC Orbitrap Fusion and Q-Exactive,Thermo Fisher

Role of protein SUMOylationon association of PMLf-nuclear body

[91]

APLg ESI UPLC QQQ, Waters Determination of AM80(tamibarotene) and WJD-A-1

[85]

ALL MALDI 2D- Differential in gelelectrophoresis

TOF, Bruker Differential seminal plasmaproteome signatures ofALL survivors

[56]

AML ESI UHPLC Q-TOF, Agilent Novel serum biomarker,especially amino acids andfatty acids

[86]

Graft-versus-leukemia

ESI LC Flow Cytometry-Timeof flight (CyTOF)and MS/MS

Rev: Proteomics of tumorimmunotherapy byallogeneic hematopoieticstem cell transplantation

[87]

AML nanospry nanoUPLC Orbitrap Fusion LumosTribrid,Thermo Fisher

Optimizing the boostingratios and MS dataacquisition for achievingbetter isobaric labelingquantitative analysis ofsingle cells

[88]

CLL nanospray nanoUHPLC LTQ Orbitrap XL hybrid,Thermo Fisher

Guideline for UPLC-MS/MSidentification for humanleukocyte antigen

[89]

CML ESI UHPLC Orbitrap Q exactive,Thermo Fisher

Ponatinib metabolites inhumanliver microsomes

[90]

PML ESI LC Orbitrap Fusion,Thermo Fisher

QuantitativeSUMO proteomics to

[91]

(continued)


For instance, AML without maturation and with maturationis considered as M1 and M2, respectively. AML with specificcytological features of acute promyelocytic leukemia or itsvariant form is considered as M3.[107,108] Interpretation ofthe data is relatively user-dependent; in such classificationcriteria, indeed some mistake can happen (for instance, thedifficulty in distinguishing M1 from L2 or M1 from M2, orM2 from M4[109]). In contrast, proteomics can classify leu-kemia without any mistakes; in this methodology, molecularmasses are the key factors of classification. In proteomics,different protein profiles between healthy samples and thedifferent types leukemia samples are used for the identifica-tion and classification of leukemia.[47,110]

In this regard, the first application of MALDI-TOF MSin the classification of leukemia was performed in 2004 byCui et al. They used two-dimensional gel electrophoresis (2-DE) to recognize different protein profiles that contribute tothe identification of ALL and AML, as well as the classifica-tion of AML subtypes. Based on their research, acute leuke-mia cells in comparison with normal white blood cellsshowed 29 proteins with different expression levels, all ofwhich were identified by MALDI- TOF MS, making themgood candidates for the identification of leukemia cases(Table 2). Some proteins identified by MALDI-TOF MSwere differentially expressed between ALL and AML, whichmakes them good molecular candidates for clinically distin-guishing AML from ALL. Some proteins were highly or spe-cifically expressed in M2 and/or M3 (subtype of AML),which can be concluded from Table 2.[33] In addition, prote-omic analysis of CML patients and healthy donor serumsamples based on 2-DE and MALDI-TOF analysis revealedthat some leukemia-associated antigens (LAAs) were goodcandidates for the diagnosis of CML, but further researchon these proteins could be useful for immunotherapy of thistype of leukemia (Table 2).[35] By comparing healthy andCML patients’ bone marrow samples (in the chronic phase)using 2-DE followed by MALDI-TOF analysis, 31 differen-tially proteins were identified (Table 2). Most of these pro-teins (except for six of them) are caused by proliferation,apoptosis, and cell survival, which could contribute to themaintenance of the chronic phase, or are simply part of theordinary metabolism and synthesis. However, c-Myc binding

protein 1, BP531, Mdm4, OSBP3, CASC3, and Mortalin canbe good candidates for CML markers in the chronic phase,because they are more prone to participating in the networkinduced by BCR-ABL (cytogenetic lesion of Philadelphiachromosome, which is detected in virtually all cases ofCML) and play a role in chronic phase phenotype.[38]

Although human plasma proteome is considered a wor-thy source of disease biomarkers, the detection of low-abun-dance plasma proteins in the presence of proteins such asalbumin, immunoglobins (IgGs), and lipoproteins is anextremely challenging issue. There are different methods fordepleting abundant proteins, including affinity removal,membrane filtration, multidimensional chromatographicfractionation, and precipitation.[111–114] However, by usingthese methods, loss of medium and low-abundance proteinscan occur. By comparing bone marrow (BM) and peripheralblood (PB) plasma, before and after using the VivapureAnti-HSA kit for depletion of albumin, and cell lysates ofpatients with AML and healthy samples, some proteins weresuggested as diagnosis markers after using 2-DE andMALDI-TOF analysis (Table 2).[41] Although using ammo-nium sulfate for precipitation of abundant proteins does notprovide high-resolution fractionation of the plasma samplecompared to protein chromatography and isoelectric focus-ing,[113] its simplicity makes it a rapid method for the deple-tion of abundant plasma proteins before using 2-DE andMALDI-TOF analysis. As indicated in Table 2, by compar-ing the ALL and AML blood samples with healthy samples,there were differentially expressed proteins.[45] In 2013, thesame research was conducted on bone marrow samples.After using a lymphocyte separation kit to isolate ALL cellsand normal lymphocytes, by comparing 2-DE of healthy anddisease samples, by using the bottom-up proteomicsapproach, fifteen differentially expressed proteins were iden-tified. However, due to limitations in human protein data-bases and the lack of the complete digestion of sevenproteins, only eight proteins were identified by using trypsindigestion and identification by using MALDI-TOF, whichare summarized in Table 2. They did not report any infor-mation about the seven unknown proteins in their researchpaper.[47] As can be seen in Table 2, glutathione S-transfer-ase P (GST-P) is not only considered as an ALL

Table 1. Continued.


identify potentialsubstrates of PIAS1

AML nanospray nanoUHPLC Triple TOF, AB SciexOr LTQ Orbitrap

Integrated nuclear proteomicsand transcriptomics toidentify S100A4

[92]

AML nanospray nanoLC Triple TOF 5600, Sciex Quantitative proteomicsbased on SWATH beforeand after treatment byibrutinib drug

[93]

aQQQ: triple-quadrupole.bFASP: filter-aided sample preparation.cSELDI: Surface-enhanced laser desorption/ionization.dCI: chemical ionization.eSILAC: isotope labeling of amino acids in cell culture.fPML: Promyelocytic leukemia (PML).gAPL: Acute Promyelocytic leukemia.


Table 2. Proteins related to identification and classification of leukemia based on proteomics approaches.

M.Wt. (Da) pI Protein related to:

Protein Name Theo. Obs. Theo Obs. Id. Dist. Class. Ref.

Proliferating cell nuclear antigen(PCNA)

28,750 29,465 4.57 4.49 ALL/AML/ þ [33]

– 28.769� 103a – 4.6 CML [38]

– 28.75� 103a – 4.57 ALL / -2.38 [50]

PTK9L protein tyrosine kinase 9-like(A6-related protein)

39,523 35,674 6.37 6.89 ALL / AML/ þ [33]

Pyridoxine 5-phosphate oxidase 29,969 22,325 6.62 6.76 ALL / AML/ þ [33]

– 30,311 – 6.62 ALL/- [47]

Uracil DNA glycosylase 35,470 38,666 8.22 6.67 ALL / AML/ þ [33]

Stathmin; leukemia-associatedphosphoprotein p18, (Op18)

17,292 19,231 5.76 5.55 ALL / AML/ þ [33]

17,292 19,699 5.76 4.98 ALL [33]

Stathmin (phosphoprotein p19) – 17.16� 103a – 5.77 ALL / þ7.29 [50]

Guanine nucleotide binding protein(G protein)

35,055 35,103 7.60 7.87 ALL / AML/ þ [33]

Rho GDP dissociation inhibitor (GDI) 23,193 23,367 5.02 5.00 ALL / AML/ þ [33]

Rho GDP dissociation inhibitors Ly-GDIþ Rho GDI 1þ Rho GDI 2

--

23.2� 103a

22.97� 103a-

-5.02

5.1ALL / þ 3.2 [50]

TNF inhibitory protein (TIP) 10,431 10,531 4.82 4.78 ALL / AML/ þ [33]

Voltage-dependent anion channel 2 30,393 30,034 6.81 6.72 ALL / AML/ þ [33]

Mitochondrial short-chainenoylcoenzymeA hydratase 1 precursor

31,351 28,564 8.34 6.01 ALL/ AML/ þ [33]

NM23-H1 19,641 21,045 5.83 5.52 ALL / AML/ þ [33]

Apoptosis inhibitor homolog 56,617 29,976 6.22 5.87 ALL / AML/ þ [33]

Similar to proteasome (prosome,macropain) subunit, _ type, 2

25,138 24,175 7.92 7.68 ALL / AML/ þ [33]

Proteasome _ subunit 25,893 28,912 5.70 5.78 AML [33]

Peroxisomal enoyl-CoA hydratase 1 35,971 38,456 6.61 6.87 ALL / AML/ þ [33]

High mobility group box 1 (HMGB 1) 24,878 22,987 5.62 6.00 ALL / AML/ þ [33]

Glutathione S-transferase (GST) – – – – ALL,AML/þ [45]

– 23.2� 103a – 5.44 ALL / þ3.14 [50]

Glutathione S-transferase–like;glutathione S-transferase omega

25,019 25,132 6.00 6.21 ALL / AML/ - [33]

GlutathioneS-transferase P (GST-P)

– 23,356 – 5.4 CML [38]

– 23,438 – 5.44 ALL / þ [47]

Chain A, manganese superoxidedismutase (MnSOD)

22,176 23,218 6.86 7.01 ALL / AML/ - [33]

Similar to homo sapiens mRNA forKIAA0120 gene with GenBankaccession no. D21261.1

24,438 21,465 8.41 5.23 ALL / AML/ - [33]

Myosin regulatory light chain 3 19,781 20,974 4.67 4.40 ALL / AML/ - [33]

19,767 20,196 4.67 4.61 ALL / AML/ - [33]

Ras suppressor protein 1 31,521 30,567 8.57 7.98 ALL / AML/ - [33]

Chain A, dimeric form of thehaemopexin domain of Mmp9

22,407 20,869 8.76 8.56 ALL / AML/ - [33]

Chain B, crystal structure of A Rac-Rho gdi complex

20,464 15,687 6.16 5.64 ALL / AML/ - [33]

Lactoferrin 30,563 23,695 8.14 8.01 ALL / AML/ - [33]

S100 calcium-binding protein A12 10,569 11,536 5.83 5.42 ALL / AML/ - [33]

Chain A, carboxylic ester hydrolase, P1 21 1 space group

13,622 12,542 5.10 5.13 ALL / AML/ - [33]

Chain A, crystal structure of theMrp14 complexed with CHAPS

13,102 14,231 5.71 5.45 ALL / AML/ - [33]

[33]

13,102 14,821 5.71 5.46 AML [33]

CFAg (AA 1–94) 10,931 13,895 9.19 5.92 ALL / AML/ - [33]

S100 calcium-binding proteinA8 (Mrp8)

10,828 12,950 6.51 7.27 AML [33]

10,828 10,732 6.51 7.06 ALL / AML/ - [33]

– – – – CLL [51]

Chain A, structure of up1-telomericDNA complex

20,831 22,645 6.79 6.89 AML [33]

Unnamed protein product 20,611 21,435 9.60 5.01 AML [33]

Unnamed protein product 32,530 32,790 6.71 6.70 AML [33]

Unnamed protein product 52,406 30,155 4.99 4.64 AML (M3) [33]

Unnamed protein product 32,530 32,450 6.71 6.80 AML (M2 &M3) [33]

Coactosin-like 15,935 16,887 5.54 5.39 AML [33]

Cytochrome c oxidase subunit VIb 10,186 12,201 6.30 6.54 AML [33]

F-actin capping protein _ subunit;Cap Z

30,609 34,076 5.69 5.52 AML [33]

F-actin capping protein alpha 1 – – – – AML (M1, M2) [48]

(continued)


Table 2. Continued.



– 32.9� 103a – 5.54 ALL/ -1.76 [50]

F-actin capping protein subunitbeta (CAPZB)

– – – – AML/þ [41]

Actin beta – 40.98� 103a – 5.56 ALL / þ1.01 [50]

41,635 40,193 5.31 5.49 AML [33]

c actin 25,862 30,616 5.65 5.41 AML [33]

Nit protein 2 30,589 35,159 6.82 7.12 AML [33]

Similar to ATP synthase, Htransporting, mitochondrialF1 complex

40,260 33,420 8.94 7.11 AML [33]

Glyoxalase I 20,764 27,988 5.12 4.93 AML [33]

Phosphoglycerate kinase 1 44,586 38,698 8.30 6.89 AML [33]

UP1, the two RNA-recognition motifdomain of HnrnpA1

20,784 20,346 7.26 7.46 AML [33]

Adenylate kinase, mitochondrial; ATP-AMP transphosphorylase

25,598 33,193 7.71 8.21 AML [33]

Adenylate kinase-1 (AK-1) – – – – ALL/þ [45]

Aldolase A 39,264 31,726 8.39 7.43 AML [33]

– – – – CML [35]

Chain A, myloperoxidase 12,311 15,363 5.77 6.47 AML [33]

Triosephosphate isomerase 26,221 26,899 6.51 7.10 AML [33, 50]

– 26,981 – 6.9 ALL/- [47]

– 26.52� 103a – 6.51 ALL/ -2.42 [50]

Ubiquinol-cytochrome c reductase 29,633 26,324 8.55 7.12 AML [33]

Cathepsin D 26,229 30,958 5.31 5.80 AML [33]

Similar to ribosomal protein S12; 40Sribosomal protein S12

14,589 14,390 6.43 6.40 AML [33]

CDC42 GAP-related protein 25,209 32,661 9.38 7.60 AML [33]

Fatty acid binding protein 5(psoriasis-associated); E-FABP

15,155 16,480 6.60 6.41 AML [33]

a enolase 36,286 37,635 6.53 6.68 AML [33]

– – – – CML [35]

– – – – AML/þ [41]

Phosphopyruvate hydratase(Alpha enolase)

– 47� 103a – 6.99 ALL / þ 4.82 [50]

b5-tubulin 49,640 40,822 4.75 5.78 AML [33]

Tubulin b – – – – AML (M1, M2),healthy blood celland healthybone marrow

[48]

– 49.6� 103a – 4.78 ALL/ þ3.49 [50]

– – – – CML [35]

Similar to VENT-like homeobox 2;haemopoieticprogenitor homeobox

36,677 36,889 8.98 7.58 AML [33]

3-Hydroxyacyl-CoA dehydrogenase,isoform 2

42,097 36,967 9.34 7.65 AML [33]

Peroxiredoxin 2 (PRDX) 21,878 25,676 5.66 5.23 AML [33]

– – – – AML/þ [41]

– – – – AML (M1, M2),healthy blood celland healthybone marrow

[48]

– 21.75� 103a – 5.67 ALL/ þ8.25 [50]

Peroxiredoxin 4 – 30,749 – 5.86 ALL / - [47]

Peroxiredoxin 3 þ peroxiredoxin 6 – 27.6� 103 a

24.9� 103a– 7.67

6.02ALL / þ 5.08 [50]

peroxiredoxin 6 – – – – AML (in M2higherthan M1)

[48]

P47 40,547 37,710 5.03 5.32 AML [33]

Adenine phosphoribosyltransferase;AMP pyrophosphorylase

19,595 18,372 5.78 5.24 AML [33]

Annexin I; lipocortin I 38,690 34,692 6.57 5.65 AML (M2and M3)

[33]


[48]

Annexin A2; annexin II (lipocortin II) 38,594 37,731 7.57 7.02 AML [33]

Annexin III – – – – AML (M2) [48]

HSPC124 36,519 36,689 5.60 6.06 AML [33]

(continued)


Table 2. Continued.



Sulfotransferase family, cytosolic, 1A 34,174 36,689 5.68 5.65 AML [33]

Inorganic pyrophosphatase 2isoform 2

31,556 36,689 7.04 6.21 AML [33]

Inorganic pyrophosphatase – 32.64� 103a – 5.54 ALL / - 1.94 [50]

Hypothetical protein 36,541 36,689 5.96 6.54 AML [33]

– 37,147 – 5.69 ALL/ - [47]

Unknown (protein for MGC:27221) 35,735 34,986 8.47 6.70 AML [33]

Transgelin 2 22,392 22,875 8.41 8.45 AML [33]

Immunoglobulin heavy chain 13,704 14,214 8.57 7.05 ALL [33]

– – – – ALL/ - [45]

Heat shock 27kDa protein 1 22,768 25,534 5.98 6.01 ALL [33]

Aldehyde reductase 36,419 35,256 6.34 6.64 ALL [33]

Endoplasmic reticulum protein29 precursor

28,975 23,864 6.77 6.76 ALL [33]

Cofilin 1 (non-muscle) 18,491 20,321 8.22 6.41 ALL [33]

Elongation factor 2 39,750 38,978 5.81 5.73 AML (M2) [33]

Small GTP binding protein RhoG 21,261 21,301 8.41 8.12 AML (M2) [33]

S100 calcium binding protein A 11(calgizzarin)

11,733 12,437 6.56 6.12 AML (M3) [33]

RNA-binding protein regulatorysubunit; oncogene DJ1

19,878 19,987 6.33 6.04 AML (M3) [33]

RNA-binding proteinY14, binder of OVCA1-1

– 19.889� 103a – 5.5 CML [38]

Chain A, human cathepsin G 25,423 14,568 11.51 9.98 AML (M3) [33]

Human heparin binding protein 24,261 24,765 9.53 9.07 AML (M3) [33]

Atomic resolution structure of Hbp 23,804 24,302 9.53 9.07 AML (M3) [33]

Azurocidin 26,637 25,578 9.75 9.07 AML (M3) [33]

Similar to high mobility groupprotein 2 (HMG-2)

18,147 17,100 5.17 4.96 AML (M3) [33]

Similar to retinoic acid, EGF, and NGFup-regulated; REN

29,624 25,875 6.09 5.48 AML (M3) [33]

Human protein disulfide isomerase 13,249 21,054 5.94 4.87 AML (M3) [33]

protein disulfide isomerase – – – – AML (M1, M2),healthy blood celland healthybone marrow

[48]

– 57� 103a – 6.1 ALL/ þ3 [50]

– 57� 103a – 4.75 ALL/ þ2.71 [50]

Chain A, cyclophilin B 19,648 29,715 9.18 6.47 AML (M2and M3)

[33]

Chain A, protease 3, myeloblastin 24,245 30,613 7.79 8.65 AML (M2and M3)

[33]

Chain c, cryogenic crystal structure ofhumanmyeloperoxidase isoform c (MPO)

53,130 32,231 9.48 8.21 AML (M2and M3)

[33]

HSP70 protein8 – – – – CML [35]

Tropomyosin isoforms – – – – CML [35]

Tropomyosin 3 þ Tropomyosin2 (beta)

– 28.91� 103a

32.76� 103a– 4.72

4.64ALL / - 2.88 [50]

C-myc binding protein,Myc modulator 1Cytoplasmic

– 17,328 – 5.9 CML [38]

Apoptosis regulatorBcl-W

– 20,775 – 5.4 CML [38]

Tumor suppressorp53-binding protein 1

– 10,946 – 4.7 CML [38]

p53-binding proteinMdm4

– 54,864 – 4.9 CML [38]

Leukocyte L1 complex heavychain belongs to the S-100 family

– 13,242 – 5.7 CML [38]

Rap guanine nucleotideexchange factor 4

– 115,523 – 6.4 CML [38]

StAR-related lipid transfer protein 5 – 23,794 – 6.2 CML [38]

A-kinase anchor protein 2 (AKAP-2) – 122,072 – 5.0 CML [38]

MAD2-like2 – 24,335 – 6.0 CML [38]

Tumor Protein D53 – 14,715 – 5.4 CML [38]

Mortalin – 73,681 – 5.9 CML [38]

MAP Kinase phosphatase (Mpk-7) – 73,102 – 7.2 CML [38]

Leukemia associated protein 5 – 47,002 – 5.7 CML [38]

Caspase-3 – 31,594 – 6.1 CML [38]

DEAD-boxprotein-retinoblastoma

– 82,433 – 6.8 CML [38]

(continued)


Table 2. Continued.



Cancer susceptibilitycandidate gene 3 protein

– 76,279 – 6.1 CML [38]

Negative regulator of IL2 – 124,075 – 4.9 CML [38]

Oxysterol bindingprotein-related protein 3

– 101,224 – 6.4 CML [38]

Eosinophil lysophospholipase,Galactin-10

– 16,481 – 6.8 CML [38]

L-Plastin – 68,191 – 5.7 CML [38]

– – – – AMl (M2) [48]

L-plastin(lymphocyte cytosolicprotein 1)

– 70.25� 103a – 5.2 ALL/ -5.85 [50]

Calcium-bindingprotein p22

– 22,456 – 5.0 CML [38]

Adapter-related proteincomplex 3 mu1 subunit

– 49,940 – 6.5 CML [38]

Aldose reductase – 35,722 – 6.5 CML [38]

Receptor protein-tyrosinekinase ErbB-1

– 134,279 – 6.3 CML [38]

Truncated epidermal growthfactor receptor

– 69,229 – 6.8 CML [38]

Ig heavy chain V-I regionND precursor

– 16,504 – 6.8 CML [38]

DNA-directed RNApolymerase III subunit

– 22,918 – 4.5 CML [38]

ATP synthase beta chain,mitochondrial [precursor]

– 56,560 – 5.3 CML [38]

– 56.5� 103a – 5.26 ALL/ þ2.98 [50]

Clusterin (CLUS) – – – – AML /þ [41]

Serum amyloid P component (SAMP) – – – – AML /þ [41]

Zinc-alpha-2 glycoprotein – – – – AML /þ [41]

Moesin (MOES) – – – – AML /þ [41]

– – – – AML (M1, M2) [48]

Apoptosis inducing factor 1 (AIFM1) – – – – AML /þ [41]

ezrin (EZRI) – – – – AML /þ [41]

– 69.23� 103a – 5.95 ALL/ -3.76 [50]

60 kDa heat shock protein (HSP60) – – – – AML /þ [41]

– 61� 103a – 5.7 ALL/ þ4.64 [50]

Heat shock 70 kDa protein 1 (HSP70), – – – AML /þ [41]

heat shock cognate 71 kDa protein1 (HSP71)

– 71000 – – AML /þ [41]

protein (Heat shock 70 kDa protein 8) – 71� 103a – 5.37 ALL/ þ2.7 [50]

78 kDa glucose regulatedprotein (HSP78)

– 78,000 – – AML /þ [41]

Gelsolin (GELS) – – – – AML / - [41]

– 86,043 – 5.90 AML [53]

Catalase CATA 59 kDa 59,000 65,000 7 5 AML /þ [41]


[48]]

– – – – AML (in M2higherthan M1)

[48]

Complement C3 precursor (CO3) – – – – AML / Not mentioned [41]

Ceruloplasmin (CERU) – – – – AML / Not mentioned [41]

– 122,983 – 5.44 AML [53]

complement factor H-related protein1 (FHR1)

– – – – AML / Not mentioned [41]

Complement factor H – 143,680 – 6.21 AML [53]

Disulfide isomerase 3 – – – – AML /þ [41]

Ig mu chain C region (IGHM) – – – – AML /þ [41]

Ig mu heavy chain diseaseprotein (MUCB)

– – – – AML /þ [41]

Leucine-rich alpha-2-glycoproteinprecursor (A2GL)


N-acetyl D glucosaminekinase (NAGK)


Serum amyloid A precursor (SAA) – – – – AML / Not mentioned [41]

Transferrin – – – – ALL / -AML / þ

[45]

Albumin – – – – ALL / -AML / þ

[45]

(continued)


Table 2. Continued.



– 69.32� 103a – 5.92 ALL / - 4.98 [50]

– 71,317 – 5.92 AML [53]

Apolipoprotein A1 (Apo-A1) – – – – ALL / -AML / þ

[45]

Chain A, Apolipoprotein A-1 – 28,061 – 5.27 AML [53]

Apolipoprotein A-IV – 45,371 – 5.28 AML [53]

Apolipoprotein E – – – – ALL, AML/ - [45]

– 36,246 – 5.65 AML [53]

Transthyretin – – – – ALL / -AML / þ

[45]

– 15,991 – 5.52 AML [53]

Chain A, Transthyretin – 12,671 – 5.26 AML [53]

a1-B-glycoprotein – – – – ALL / -AML / þ

[45]

a2-HS-glycoprotein precursor (AHSG) – – – – ALL / -AML / þ

[45]

a1-antitrypsin – – – – ALL, AML / þ [45]

Haptoglobin a chain – – – – ALL / -AML / þ

[45]

Haptoglobin – 45,861 – 6.13 AML [53]

Interferon-b (INF-b) – – – – ALL / þ [45]

SET domain bifurcated (SETDB) – – – – ALL / þ [45]

T-cell receptor-b (TCR-b) – – – – ALL / þAML / -

[45]

Prohibitin – 29,843 – 5.57 ALL / þ [47]

– 30� 103a – 5.57 ALL/ þ3.75 [50]

60S acidic ribosomal protein P0 – 34,423 – 5.71 ALL / - [47]

Cytoplasmic actin – 42,052 – 5.29 ALL / - [47]

Tumor rejection antigen/Gp96/


[48]

Purine nucleoside phosphorylase – – – – AML (M1, M2),healthy blood celland healthybone marrow

[48]

Vinculin – – – – AML (M1, M2),healthy blood celland healthybone marrow

[48]

– 123.59� 103a – 5.51 ALL/ -4.72 [50]

Histonebinding protein RBBP4 – – – – AML (M1, M2) [48]

a-actinin 1 – – – – AML (M1, M2) [48]

14-3-3 protein – – – – AML (M1, M2) [48]

Pyruvate kinase – – – – AML (M1, M2) [48]

DJ-1 protein – – – – AML (M1, M2) [48]

Protein PP4-X – – – – AML (M1, M2) [48]

Transketolase – – – – AML (M1, M2) [48]

6-phosphogluconate dehydrogenase – – – – AML (M2) [48]

Dna K-type molecularchaperone HSPA5

– 72� 103a – 5.03 ALL / þ2.74 [50]

Haematopoietic lineage cell specificprotein HS1

– 54� 103a – 4.74 ALL / þ3.04 [50]

Ubiquilin 1 – 62.5� 103a – 5.02 ALL / þ 3.26 [50]

Heterogeneous nuclearribonucleoprotein F

– 46� 103a – 5.38 ALL / þ 2.7 [50]

Heterogeneous nuclearribonucleoprotein H3

– 49� 103a – 5.89 ALL / þ3.75 [50]

Heterogeneous nuclearribonucleoprotein C1/C2

– 32� 103a – 5.1 ALL / þ2.67 [50]

D-3-phosphoglycerate dehydrogenase – 56.5� 103a – 6.31 ALL / þ 2.68 [50]

Growth factor receptor-boundprotein 2 (SH2/SH3 adaptor GRB2)

– 25.19� 103a – 5.89 ALL / þ 2.14 [50]

Alternative splicing factor ASF-1 – 22.5� 103a – 7.72 ALL / þ 4.98 [50]

Vimentin – 53.5� 103a – 5.06 ALL / þ 7.56 [50]

Fascin 2 – 55� 103a – 7.95 ALL / þ 4.92 [50]

Ubiquitin carboxyl terminal hydrolase – 26.2� 103a – 4.84 ALL / þ 6.6 [50]

HSP 27þ proteasome subunit b3 þproteasomeendopeptidase complex

--

22.93� 103a

25.88� 103a-

-6.14

6.92ALL / þ 5.6 [50]

GrpE protein homolog 1,mitochondrial precursor

– 24.26� 103a – 8.24 ALL / þ 7.36 [50]

(continued)


Table 2. Continued.



Nucleoside diphosphate kinase A – 17.14� 103a – 5.83 ALL / þ 5.69 [50]

Prefoldin subunit 2 – 16.64� 103a – 6.2 ALL / þ 3.54 [50]

Translationally controlled tumourprotein (TCTP)

– 19.58� 103a – 4.84 ALL / þ 4.12 [[50]

TGFb- induced anti-apoptotic factor 1 – 12.41� 103a – 8.35 ALL / þ 6.22 [50]

SH3 domain binding protein SH3BP-1 – 10.43� 103a – 4.82 ALL / þ 3.34 [50]

Glutaredoxin-related protein C14or f87

– 16.62� 103a – 6.28 ALL / þ 6.78 [50]

Enhancer of rudimentary homolog – 12.25� 103a – 5.63 ALL / þ 3.61 [50]

Superoxide Dismutase (SOD) – 15.8� 103a – 5.7 ALL / þ 3.82 [50]

Endoplasmin precursorþHSP90 beta

– 92.41� 103a – 4.76 ALL / - 2.66 [50]

Mitofilin – 83.63� 103a – 6.08 ALL / - 2.33 [50]

UV excision repair proteinRAD23 homolog

– 43.15� 103a – 4.79 ALL / - 2.2 [50]

T-complex protein 1 subunit beta(TCP-1- b)

– 57.32� 103a – 6.02 ALL / - 4.91 [50]

Stomatin like protein 2 SLP-2 – 38.52� 103a – 6.88 ALL / - 3.11 [50]

Nucleophosmin – 32.44� 103a – 4.67 ALL / - 2.97 [50]

Annexin A5 – 35.78� 103a – 4.94 ALL / - 2.03 [50]

Clathrin light polypeptide (Lca) – 23.65� 103a – 4.45 ALL / - 3.02 [50]

Lactoylglutathione lyase(Methylglyoxalase,Aldoketomutase)

– 20.58� 103a – 5.25 ALL / - 3.2 [50]

PACAP protein (Pituitary adenylatecyclase-activatingpolypeptide receptor)

– 20.68� 103a – 5.37 ALL / - 2.56 [50]

Transcription elongation factor Bpolypeptide 2

– 13.13� 103a – 4.73 ALL / - 2.72 [50]

Programmed cell death protein 5(TFAR19 protein)

– 14.15� 103a – 5.78 ALL / - 6.28 [50]

Translation initiation factor eIF-2Balpha subunit (eIF-2B GDP-GTPexchange factor)

– 33.69� 103a – 6.9 ALL / - 3.78 [50]

Cytochrome c oxidase subunit Va(COX5A protein)

– 16.75� 103a – 6.3 ALL / - 3.32 [50]

Peptidylprolyl isomerase – 17.87� 103a – 7.82 ALL / - 1.93 [50]

Fatty acid binding protein (FABP) – 15.02� 103a – 6.84 ALL / - 2.84 [50]

Thyroid hormone receptor-associatedprotein 3

– – – – CLL [51]

T cell leukemia/ lymphomaprotein 1A

– – – – CLL / þ [51]

Myosin-9 – – – – CLL /- [51]

Alpha-2-macroglobulin – 162072 – 5.92 AML [53]

Complement factor B – 86847 – 6.67 AML [53]

Complement C1r subcomponent – 81606 – 5.82 AML [53]

Prothrombin – 71,475 – 5.64 AML [53]

C4b-binding protein alpha chain – 69,042 – 7.15 AML [53]

Chain C, Complement C4 InComplex with Masp-2

– 33,737 – 6.37 AML [53]

Alpha-1b-glycoprotein – 54,790 – 5.56 AML [53]

Vitamin D-binding protein – 54,526 – 5.40 AML [53]

Hemopexin – 52,385 – 6.55 AML [53]

Fibrinogen gamma chain – 46,823 – 5.54 AML [53]

Fibrinogen beta chain 56,577 8.54 AML [53]

Fibrinogen alpha chain precursor 70,227 8.23 AML [53]

Kininogen 1 – 48,954 – 6.29 AML [53]

Fibronectin – 266,052 – 5.46 AML [53]

CD5 antigen-like – 39,603 – 5.28 AML [53]

Serum amyloid P-component – 25,485 – 6.10 AML [53]

Complement component C7 – 96,550 – 9.06 AML [53]

Haemoglobin subunit beta – 16,102 – 6.75 AML [53]

Serum amyloid A-1 – 13,581 – 6.28 AML [53]

14,851 9.17 AML [53]

Plasminogen – 93,247 – 7.04 AML [53]

Retinol-binding protein – 23,337 – 5.76 AML [53]

Complement C1s subcomponent – 78,174 – 4.86 AML [53]

Alpha-1-microglobulin – 22,030 – 6.25 AML [53]

M.Wt., Molecular weight; pI, Isoelectric point; Theo., Theory; Obs., Observed; Id., Identification; Dist., Distinguishing; Class., Classification; þ, Increasing;-, Decreasing.

aIn the original paper, k Da was reported as molecular weight unit.


biomarker,[47] but it is also observed in CML samples.[38] Inaddition, there is evidence of a relationship between theexpression of GST-P1 and cancer drug resistance.[115–117]

GST-P belongs to a superfamily of Phase II detoxificationenzymes that catalyze the conjugation of glutathione (GSH)to the electrophilic center of toxic and carcinogen com-pounds through thioether linkages.[118] GST-P1 is composedof two domains. The smaller N-terminal a/b helix domainincludes residues 1–74, and is responsible for GSH binding(G-site),[119] and the larger a helix C-terminal domainincludes residues 81–207, and contains a hydrophobic sub-strate binding site (H-site).[115,119,120] In addition, GST-P1interacts with the C-terminus of Jun-kinase and suppressesthe enzyme activity through an allosteric inhibition mechan-ism and modulates the induction of apoptosis.[121]

Therefore, it will be worthwhile to conduct a comprehensivestudy on GST-P1 in leukemia. Another research group hasperformed the same experiment on bone marrow (BMMC)and peripheral blood (PBMC) mononuclear cells of AML(M1 and M2 subtypes) patients and healthy volunteers.They found that there was no significant difference betweenthe results of BMMC and PBMC. In addition, their resultsrevealed that 25 proteins were differentiating between AMLand healthy samples (Table 2). Their results were not lim-ited to the above-mentioned proteins, as they comparedAML bone marrow and peripheral blood mononuclear cellsamples at different stages of the disease, which will be dis-cussed in the next section.[48] By using 2-DE on CD19þ Blymphoblast cells of healthy and ALL patients, 61 differen-tially regulated proteins were identified and analyzed byMALDI-TOF MS. The fold changes in these proteins on 2-DE were determined based on the density tool of PDQuest(Table 2).[50] In 2014, a comprehensive study was conductedto identify disease-related proteins in CLL. First, they identi-fied the differences between healthy and CLL samples. Theheterogeneously expressed proteins were then identified. Inthe next step, the identification of proteins with differentialabundance in poor prognosis CLL was performed. Finally,using hierarchical cluster analysis, the hidden patterns ofprotein expression were identified. In this regard, they found63 proteins related to CLL (all protein names were not men-tioned). However, changes in only four proteins were identi-fied as high risk factors for CLL (Table 2).[51] As mentionedbefore, depletion of highly abundant proteins can be usefulfor the detection of low-abundance proteins in blood sam-ples. Using the Multiple Affinity Removal System (MARS)LC column Hu-7, seven high-abundance proteins such asalbumin, IgG, IgA, transferrin, antitrypsin, haptoglobin(HPT), and fibrinogen in plasma samples were depleted. Bycomparing healthy and AML plasma samples on 2-DE, 34proteins were identified (Table 2).[53]

Protein markers related to the prognosis of leukemiabased on proteomics approaches

Studying cell surface membrane proteins may be useful fornew prognostic and therapeutic methods. However, theirhydrophobicity and high molecular weight are the two main

obstacles in achieving the desired results of studying themby using 2-DE followed by mass spectrometry. However,one-dimensional gel electrophoresis can successfully separateproteins from plasma-membrane isolated cells, followed byMALDI-TOF identification. Two proteins, namely MIG2Band B-cell novel protein #1 (BCNP1), were identified andexpressed in B-cells of patients with CLL.[32] Furthermore,by comparing mononuclear cells of AML serum blood sam-ples with normal samples based on 2-DE and MALDI-TOFanalysis, 28 proteins were identified, which are highlyexpressed in AML samples (Table 3). Among them, sevenproteins, namely enolase 1, Rho-GDI beta, annexin A10,catalase, peroxiredoxin 2, tropomyosin 3, and lipocortinwere selected as AML prognosis markers. The selection wasperformed based on the role of these proteins (glycolysis,tumor suppression, apoptosis, angiogenesis, and metasta-sis).[39] In 2008, an investigation of serum biomarkers wasperformed. First, they used the MARS column and concen-trated the flow-through fraction using a 5000 molecularweight cutoff concentrator. Then, the samples were fractio-nated by elution from a copper-MB preparation with anMB-IMAC copper kit. Next, the concentrated copper-puri-fied fractions of protein were loaded onto a C8 column andanalyzed by MALDI-TOF MS. The results indicated thatplatelet factor-4 (PF4) is a protein that is upregulated incomplete remission AML patients and could be a good indi-cator for a successful treatment.[122] In this regard, there is agood article that used MALDI-FT- ICR MS for analysis ofplatelet factor 4 from human serum, for further information,please see the related article.[123]

In childhood ALL, the early response to 7 days of pred-nisolone treatment is an important prognostic factor for pre-dicting the final result of treatment. Using 2-DE andMALDI-TOF/TOF MS in PRED-sensitive and PRED-resist-ant cell lines, 94 differentially expressed proteins were iden-tified (77 in PRED-sensitive and 17 in PRED-resistant celllines), as shown in Table 3. Among these 94 proteins, prolif-erating cell nuclear antigen (PCNA) was found to be highlypredictive of PRED response in childhood leukemia.[44] Asmentioned in the previous section, in 2013, a research groupinvestigated bone marrow and peripheral blood mono-nuclear cells in AML samples. The results were based on 2-DE followed by MALDI-TOF MS analysis of AML samples(before induction therapy who achieved complete remissionor were resistant) and they proposed four prognosis proteins(Table 3).[48]

Abnormal functioning of ubiquitin-proteasome system(UPS), one of the major protein degradation pathways ineukaryotic cells, has been observed in cancer. The 20S pro-teasomes are essential components of the UPS which presentwithin the cells and in the extracellular space. Therefore,their concentration in blood plasma can be used as prognos-tic significance in patients suffering from cancer. Althoughfunctions of extracellular proteasomes and mechanisms oftheir release by cells remain largely unknown, modulation ofproteasome composition and PTMs are considered as themain mechanism of proteasome activity regulation. In thisregard in 2020, Tsimokha et al, studied on PTMs of


Table 3. Proteins related to prognosis of leukemia based on proteomics approaches

M. Wt. (k Da)pI

Protein Name The. Obs. Theo Obs. Type of leukemiaExpressed inCell line Ref.

MIG2B – 60 – – CLL [32]

BCNP1 80 – 80 – – CLL [32]

Lipocortin 1 (annexin 1) 38.9 – 6.6 – AML [39]

Annexin I – – – – AML(in completeremission higherthan resistance)

[48]

Rho-GDI beta 23.0 – 5.1 – AML [39]

Dystrobrevin alpha 65.8 – 8.7 – AML [39]

Annexin A10 37.7 – 5.2 – AML [39]

Annexin A10 short isoform 23.1 – 5.8 – AML [39]

Zinc finger, MYND domain 42.3 – 5.8 – AML [39]

Zinc finger protein BTB 73.4 – 6.03 – AML [39]

Ribosomal protein S6 kinase 3 29.0 – 6.0 – AML [39]

Transforming protein RhoA 21.4 – 5.8 – AML [39]

Ras association domain family 4 45.8 – 6.7 – AML [39]

Disulfide isomerase ER-60 56.7 – 6.0 – AML [39]

T-cell receptor delta chain 12.2 – 5.04 – AML [39]

Enolase 1 47.5 – 7.0 – AML [39]

Apolipoprotein A-I 30.7 – 7.1 – AML [39]

Apolipoprotein A-IV precursor 34.4 – 5.7 – AML [39]

Triosephosphate isomerase 26.5 – 6.5 – AML [39]

Hemoglobin, chain D 15.9 – 7.9 – AML [39]

Hemoglobin beta (fragment) 4.5 – 9.4 – AML [39]

NIT1 protein 26.9 – 5.8 – AML [39]

Dihydrolipoamidedehydrogenase precursor

54.2 – 7.6 – AML [39]

Fumarase hydratase,cytoplasmic isoform

50.1 – 6.9 – AML [39]

Enoyl CoA hydratase 31.8 – 8.3 – AML [39]

Voltage-dependent anionchannel 2

30.8 – 8.0 – AML [39]

ATP synthase, H1 transporting 54.5 – 8.2 – AML [39]

Catalase 59.9 – 6.9 – AML [39]

Peroxiredoxin 2 21.9 – 5.6 – AML [39]

Tropomyosin 3 26.5 – 4.8 – AML [39]

ACTB protein 40.5 – 5.5 – AML [39]

Platelet factor-4 (PF4) – 7764.8� 10-3 a – – AML [122]

Non-metastatic cells 2 17.4 10.1 8.5 10.5 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

17.4 17.7 8.5 10.3 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]

Cofilin 1 (non-muscle) 18.7 11.6 8.2 10.5 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

Protein phosphatase 1, catalyticsubunit, beta

38.0 37.9 5.8 6.9 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

MHC B complex protein 12.3 35.5 33.0 7.6 9.1 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

Chain A, crystal structure ofhuman L-isoaspartylmethyltransferase


[44]

Neuropolypeptide h3 21.0 17.6 7.4 9.0 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

Similar to retinoblastoma-bindingprotein 4


[44]

Guaninemonophosphate synthetase


[44]

ERp28 29.0 28.2 6.8 7.5 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

Purine nucleoside phosphorylase 32.3 31.7 6.5 8.2 ALL [44]

(continued)


Table 3. Continued.

M. Wt. (k Da)pI


non-treated and PRED-treatedsamples of PRED-resistantREH cells

Enoyl-CoA hydratase 31.8 27.2 8.3 7.2 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

N-Acetylglucosaminekinase variant


[44]

Selenium donor protein 42.8 41.7 6.0 6.4 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

ECH1 protein 36.1 33.4 8.5 7.7 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

ACY1L2 protein 51.3 50.2 5.8 6.4 ALL non-treated and PRED-treatedsamples of PRED-resistantREH cells

[44]

Chaperonin containing TCP1,subunit 2 (beta)


[44]

Chaperonin containing TCP1,subunit 7 (eta) variant


[44]

PSMA4 protein 29.6 31.0 7.6 8.8 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]

Proteasome beta 9 subunitisoform2 proprotein


[44]

PA28b 27.5 28.3 5.4 5.5 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]


[44]

Chain P, Crystal Structure of theMammalian 20 s Proteasome At2.75 A Resolution


[44]

PCNA 29.0 30.5 4.6 4.0 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]


[44]

PREDICTED: similar toproteasome 26 S ATPasesubunit 2


[44]

Chain A, X-Ray Crystal StructureOf Human Galectin-1


[44]

HMGB2 protein 22.4 25.3 9.5 10.4 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]

Heat shock protein 27 22.4 26.0 7.8 7.0 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]

29.2 22.4 6.1 7.8 ALL non-treated and PRED-treatedsamples of PRED-sensitive cell

[44]

(continued)


extracellular proteasomes in comparison to cellular counter-parts of the human leukemia cell line K562. And by the aidof MALDI- FT-ICR MS analysis, they could identify 64 and55 PTM sites in extracellular and cellular proteasomes,respectively, including phosphorylation, ubiquitination,acetylation, and succinylation. These data may be useful infuture studies aiming at the dissection of molecular mecha-nisms of extracellular proteasome function and transportinto extracellular space.[124]

Effect of leukemia’s medications on proteins based onproteomics approaches

Proteomic analysis can be used to evaluate our understandingof the mechanism of action of anticancer drugs. In 2004, theaction mechanism of interferon-alpha (IFN-a), which wasused for treatment of CML, was investigated by using 2-DEfollowed by MALDI-TOF MS. The level of ubiquitin cross-reactive protein (UBCR) was increased in K562 cells due tointerferon-alpha treatment. The increasing level of UBCR canbe related to the IFN-a anti-proliferating activity, which ismediated by the specific degradation of intermediate filamentprotein compounds.[34] Cytokine-induced differentiation ofmyeloid leukemia cells is an important therapeutic implica-tion. To clarify its mechanism, the effect of interleukin-6 (IL-6) on myeloid leukemia cells proteome was evaluated using

2-DE followed by MALDI-TOF-MS and/or ESI-MS/MS.Fifteen proteins were identified to be differentially expressedin leukemia (Table 4), and based on their function, theybelong to eight classes, such as signal transduction, transcrip-tion, cell stress, and defense.[36] Azacitidine and decitabineare used for the treatment of AML. They are known as DNAmethyltransferase inhibitors. Thus, by comparing the proteo-mics analysis of leukemic cells of patients untreated andtreated with these two drugs, some important data for thedevelopment of target drugs can be retrieved. The effects ofthese medications on the proteins are summarized in Table4.[46] Based on 2-DE and MALDI-TOF MS experiments onnine children with ALL with side effects of chemotherapy,some proteins were identified, which can be useful for per-sonalized medicine studies. Although the type of treatmentwas not mentioned in this study, the identified proteins aresummarized in Table 4.[54] Chemotherapy and radiotherapycan cause gonadal toxicity in patients who suffer from cancerand can affect their ability to reproduce (temporary and/orpermanent). In 2019, a proteomics study was conducted toidentify the differentially expressed proteins in the seminalplasma samples of ALL survivors in comparison with healthysamples. The samples were collected at least two years afterthe successful treatment of patients with ALL who were18 years of age at the time of therapy. By using a two-dimen-sional difference gel electrophoresis (2D-DIGE) followed by

Table 3. Continued.

M. Wt. (k Da)pI


lines (697, Sup-B15 andRS4;11)


[44]

ER-60 protease 57.1 55.0 6.0 6.0 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]


[44]

VDAC1 30.7 32.8 8.6 10.3 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]


[44]

Calreticulin precursor variant 47.1 51.3 4.3 3.7 ALL non-treated and PRED-treatedsamples of PRED-sensitive celllines (697, Sup-B15 andRS4;11)

[44]

Glutathione transferaseomega

– – – – AML(complete remission)

[48]

Esterase D/formylglutathione hydrolase

– – – – AML(complete remission)

[48]

Gamma 1 actin – – – – AML(higher in resistance)

[48]

M.Wt., Molecular weight; pI, Isoelectric point; Theo., Theory; Obs., Observed.aIn the original paper, Da was reported as molecular weight unit.


Table 4. Proteins related to effect of medications based on proteomics approaches.

M. Wt. (Da) pI

Protein name Theo. Obs. Theo Obs.Medicationand effects Leukemia type Ref.

Tumor protein D52 20.05� 103a 28.35� 103a 4.87 4.93 interleukin 6: þ AML [36]

Cystatin B 11.04� 103a 11.05� 103a 6.82 7.24 interleukin 6: þ AML [36]

Peroxisomal membraneprotein 20

17.00� 103a 17.09� 103a 7.71 8.10 interleukin 6: þ AML [36]

Destrin 18.51� 103a 18.35� 103a 8.14 8.11 interleukin 6: þ AML [36]

Nucleolin 76.55� 103a 15.32� 103a 4.69 6.85 interleukin 6: - AML [36]

Aldolase A 39.33� 103a 39.12� 103a 8.30 8.00 interleukin 6: þ AML [36]

Stathmin 1 17.26� 103a 17.27� 103a 5.76 5.20 interleukin 6: - AML [36]

Putative 60.48� 103a 15.25� 103a 7.63 7.35 interleukin 6: þ AML [36]

Acidic nuclearphosphoprotein pp32(pp32)

28.52� 103a 16.16� 103a 3.99 5.35 interleukin 6: - AML [36]

Similar to arginine-rich,mutated in early stagetumors


Similar to translationalycontrolled tumor protein(TCTP)


Breast carcinoma amplifiedsequence 2


Peroxiredoxin 6 24.81� 103a 26.11� 103a 5.98 6.81 interleukin 6:biphasic proteinexpression pattern

AML [36]

Proliferating cell nuclearantigen (PCNA)

28.77� 103a 34.56� 103a 4.66 4.65 interleukin 6: - AML [36]

29,156 – 4.57 – Azacitidine: -Decitabine: -

AML [46]

Lectin NP 14.86� 103a 14.53� 103a 5.32 5.25 interleukin 6: þ AML [36]

Endoplasmin 92,637 – 4.73 – Azacitidine: - AML [46]

90,351 – 4.73 – Azacitidine: þ AML [46]

Endoplasmic reticulum protein ERp29 29,032 – 6.08 – Decitabine: - AML [46]

Heat shock protein 90 kDa alpha, class B member 1 83,638 – 4.94 – Azacitidine: -Decitabine: -

AML [46]


AML [46]

83,638 – 4.94 – Azacitidine: þDecitabine: þ

AML [46]

83,638 – 4.94 – Azacitidine:- AML [46]

Heat shock 70 kDa protein 8 71,138 – 5.37 – Azacitidine: -Decitabine: -

AML [46]


AML [46]

Heat shock protein 60 61,388 – 5.24 – Azacitidine: N/ADecitabine: -

AML [46]

Chaperonin containing TCP1, subunit 2 (beta) 57,878 – 6.02 – Azacitidine: -Decitabine: -

AML [46]

Heat shock 70 kDa protein 9 74,089 – 5.97 – Azacitidine: - AML [46]

Phosphoglycerate kinase 1 44,985 – 8.30 – Decitabine: - AML [46]

Cypa (Cyclophilin A) 18,154 – 7.82 – Azacitidine: þ AML [46]

18,154 – 7.82 – Azacitidine: - AML [46]

18,154 – 7.82 – Azacitidine: - AML [46]

18,229 – 7.82 – Decitabine: - AML [46]

18,229 – 7.82 – Decitabine: - AML [46]

18,229 – 7.82 – Decitabine: þ AML [46]

Cyclophilin B 22,785 – 9.25 – Decitabine: - AML [46]

Calreticulin 48,325 – 4.28 – Azacitidine: þDecitabine: -

AML [46]

48,325 – 4.28 – Azacitidine: - AML [46]

Protien-disulfide isomerase A3 56,761 – 5.61 – Azacitidine: -Decitabine: -

AML [46]

Alpha-Enolase 47,538 – 6.99 – Azacitidine: þDecitabine: -

AML [46]


AML [46]

Fumarate hydratase, mitochondrial 54,773 – 6.99 – Decitabine: þ AML [46]

Prolyl 4-hydroxylase beta-subunit 57,578 – 4.69 – Azacitidine: - AML [46]

Inosine monophosphate dehydrogenase 2 56,338 – 6.44 – Azacitidine: - AML [46]

Glyceraldehyde-3-phosphate dehydrogenase 36,244 – 8.58 – Azacitidine: - AML [46]

36.201� 103a – 8.58 – Decitabine: - AML [46]

Chain B, Triosephosphate Isomerase (Tim) 26,877 – 6.51 – Azacitidine: -Decitabine: N/A

AML [46]

(continued)


MALDI-TOF analysis, they identified eight differentiallyexpressed proteins, which are summarized in Table 4.[56] It isnoteworthy that the direct labeling of proteins with fluores-cent dyes prior to IEF in 2D-DIGE, the lack of reproducibil-ity, and the quantitation by using conventional 2-DE canimprove the results obtained.[125]

Drug resistance studies based onproteomics approaches

As mentioned before, drug resistance plays a crucial role intreatment failure. Therefore, understanding the related pro-teins can be helpful for investigating the treatment process.In this regard, the proteome of two AML cell lines, HL-60/

Table 4. Continued.

M. Wt. (Da) pI

Protein name Theo. Obs. Theo Obs.Medicationand effects Leukemia type Ref.

Cytocrome P450 27, mitochondrial 60,595 – 8.43 – Decitabine: þ AML [46]

Proteasome subunit, alpha type, 5 26,621 – 4.74 – Azacitidine: - AML [46]

Valosin-containing protein 71,660 – 5.14 – Azacitidine: - AML [46]

Non-metastatic cells 1, protein (NM23A) isoform a 19,925 – 5.84 – Azacitidine: - AML [46]

Catalase 59,947 – 6.95 – Azacitidine: -Decitabine: þ

AML [46]

Inorganic pyrophosphatase 33,095 – 5.54 – Decitabine: - AML [46]

Protein phosphatase 1 regulatory subunit 14 C 17,946 – 5.09 – Decitabine: þ AML [46]

Glutathione S-transferase P 23,651 – 5.44 – Azacitidine: -Decitabine: -

AML [46]

Prohibitin 29,843 – 5.57 – Decitabine: þ AML [46]

Folate receptor beta 30,286 – 7.47 – Decitabine: þ AML [46]

Alpha tubulin 50,972 – 4.94 – Azacitidine: - AML [46]


AML [46]

Beta tubulin 48,233 – 4.78 – Azacitidine: -Decitabine: -

AML [46]

ACTB protein(Beta- actin) 40,620 – 5.29 – Azacitidine: - AML [46]

40,620 – 5.29 – Azacitidine: þDecitabine: -

AML [46]

F-actin capping protein alpha 1 33,115 – 5.45 – Azacitidine: - AML [46]

Tropomysin, TPMsk3 28,934 – 4.72 – Azacitidine: N/A AML [46]

Retinoblastoma binding protein 4 variant 47,910 – 4.74 – Azacitidine: N/A AML [46]

Serine-threonine kinase receptor-associated protein 38,756 – 4.98 – Decitabine :- AML [46]

Nucleophosmin 32,653 – 4.64 – Azacitidine: -Decitabine: -

AML [46]

Heterogeneous nuclear ribonucleoproteins (hnRNP) A2/B1 37,464 – 8.97 – Azacitidine -Decitabine: þ

AML [46]

Heterogeneous nuclear ribonucleoprotein A1 38,936 – 9.17 – Decitabine: þ AML [46]

Guanine nucleotide-binding protein subunit alpha-11 42,382 – 5.51 – Decitabine: þ AML [46]

Rho GDP-dissociation inhibitor 1 23,250 – 5.03 – Decitabine: þ AML [46]

Calmodulin 17,152 – 4.09 – Azacitidine: þDecitabine: þ

AML [46]

Peroxiredoxin-1 22,324 – 8.27 – Decitabine: - AML [46]

Glutaredoxin-3 37,693 – 5.31 – Decitabine: - AML [46]

Eukaryotic translation initiation factor 3 subunit I 36,878 – 5.38 – Decitabine: þ AML [46]

ARL14_HUMAN 21,608 – 8,7 – ALL [54]

THIO_HUMAN 11,730 – 4.7 – ALL [54]

HV303_HUMAN 12,574 – 7.0 – ALL [54]

SOD2_HUMAN 24,707 – 8.3 – ALL [54]

PCLI1_HUMAN 28,253 – 5.8 – ALL [54]

PRDX2_HUMAN 21,878 – 5.7 – ALL [54]

N2DL3_HUMAN 27,931 – 8.0 – ALL [54]

KCY_HUMAN 22,208 – 7.9 – ALL [54]

THAP1_HUMAN 24,928 – 8.2 – ALL [54]

CWC15_HUMAN 26,608 – 5.5 – ALL [54]

PGAM4_HUMAN 28,759 – 6.5 – ALL [54]

PGAM1_HUMAN 28,786 – 7.0 – ALL [54]

CISH_HUMAN 28,645 – 6.2 – ALL [54]

PAB1L_HUMAN 29,857 – 8.6 – ALL [54]

Isocitrate Dehydrogenase 1 (IDH1) 46� 103a – – – þ 1.7 fold ALL [56]

Semenogelin 1 (SEMGI) 52� 103a – – – þ 2.4 fold ALL [56]

Lactoferrin 78� 103a þ 2.6 fold ALL [56]

Pepsinogen 42� 103a – – – � 19.2 fold ALL [56]

Prostate specific antigen precursor, partial 28� 103a – – – � 1.6 fold ALL [56]

Prolactin-inducible protein precursor 17� 103a – – – þ 5.4 fold ALL [56]

Acid phosphatase 44� 103a – – – � 8.7 fold ALL [56]

Serum albumin 69� 103a – – – þ 2.6 fold ALL [56]

M.Wt., Molecular weight; pI, Isoelectric point; Theo., Theory; Obs., Observed; þ, Increased/ Induced or overexpressed/ Upregulated; -, Reduced/ Inhibited orreduced/ downregulated.

aIn the original paper, k Da was reported as molecular weight unit.


DOX (doxorubicin resistant AML cell line) and HL-60(drug-sensitive cell line), were investigated by using 2-DEfollowed by MALDI-TOF. Sixteen differentially expressedproteins between the two drug-resistant and drug-sensitivecell lines were identified, which can help us with the investi-gation of the drug resistance mechanism (Table 5).However, only 13 proteins were identified in the Swissprotdatabase.[37] The same trend was observed in 2011 for theidentification of protein expression changes between cell lineHL-60 and adriamycin-resistant HL-60 cell line (HL-60/ADR), based on 2D-DIGE followed by MALDI-TOF. Theresults indicated that 16 proteins were differentiallyexpressed, as summarized in Table 5.[43] Imatinib is knownas an effective therapy for chronic phase CML BCRABL-positive patients. However, imatinib resistance in CMLpatients is one of the reasons for treatment failure. The useof MALDI-TOF MS in proteomics analysis of imatinibresistant cells revealed that increasing the acetylation ofHSP90, hnRNP L, and decreasing phosphorylation of HSPs,and hnRNPs as PTMs in these cells could be associated withthe imatinib resistance in CML patients.[42] Similarly, in2015, a proteomics study was completed to identify the pro-teins related to doxorubicin resistance in CML patients inK562/A02 cell lines compared to the K562 cell line. After 2-DE, 44 proteins were identified by using MALDI-TOF MSand/or LC-MS/MS, as summarized in Table 5.[52]

Protein glycosylation is considered a type of PTM in avariety of biochemical and cellular processes, which mayhave a key function in cancer cell growth metastasis. In thisregard, in 2019 the N-glycan profiles of membrane proteinsof AML patients were analyzed from adriamycin (ADR)-resistant U937/ADR cells and sensitive line U937 cells. Inthis study, by using the Ficoll density gradient separationmethod, the peripheral blood mononuclear cells were puri-fied. Some treatments, as well as the digestion of lyophilizedcell membrane proteins were used for Peptide-N-Glycosidase F (PNGase F) treatment, N-glycans releasedfrom U937/ADR and U937 cells. Using MALDI-TOF MS,the 23N-linked glycans were analyzed in the m/z range of1000–4000. Based on the findings of the high mannose N-glycans and mannosyltransferase ALG3, they affected drugresistance in AML cells. As they have provided only thename of N-glycans (not the name of glycol proteins), we donot summarize these N-glycans in Table 5. For furtherinformation about these N-glycans, please see therelated article.[55]

Leukemia relapse studies by the aid of MALDI-TOF MS

Since relapse has emerged as a challenge in leukemia treat-ment success, detection of minimal residual disease (MRD),which is defined as the presence of remaining leukemic cells,is necessary. In this regard, in 2010, Liang et al. claimed thatin serum samples of acute leukemia patients, the relativeintensities of two peptides decreased, with a m/z ratio of1778 and 1865, which were identified as fragments of com-plement 3 (C3f), and would be related to an increasingchance of remission and could be used as an MRD marker.

They extracted the peptides based on copper-chelated mag-netic beads and analyzed them using MALDI-TOF massspectrometry. They have indicated that C3f-desArg could bea good candidate for the discrimination between ALL andAML before effective treatment.[40]

By proteomics (peptidomics) analysis of the blood serumof relapsed AML patients and by comparing them with nor-mal blood serum samples, the differential expression of 47peptides (in range of 700–10,000Da) was detected, includingubiquitin-like modifier activating enzyme 1(UBA1), isoform1 of fibrinogen alpha chain precursor, and platelet factor 4.They claimed that in newly diagnosed AML and relapsedAML patients, these peptides were upregulated when com-pared to the levels in healthy samples.[49]

Results

In this review article, we have tried to gather informationon proteomics studies related to leukemia with the aid ofMALDI-TOF MS. As can be seen here, there are more than400 of reported proteins involved in leukemia, which aresummarized in Tables 2–5. By classifying the informationfrom these tables, we have found 42 proteins that appearedin more than one table which means they are involved intwo or three different stages (classification and/or prognosisand/or effect of leukemia’s medications and/or drug resist-ance, and/or relapse) of different type of leukemia (ALL,AML, CLL, or CML). These proteins are summarized inTable 6.

As mentioned in Table 6, seven proteins are involved intwo or three different stages of one type of leukemia.Among them, six proteins (actin gamma, annexin 1, apoli-poprotein A-1, apolipoprotein A-IV, catalase, and cyclophi-lin B) were observed only in AML. These six proteins havethe potential to be further studied as AML markers.However, catalase was the only one observed in the classifi-cation, prognosis, and effect of medication on protein stud-ies. This protein can be considered a good AML marker forfurther studies. Catalase is a crucial enzyme that protectscells against the toxic effects of hydrogen peroxide by scav-enging and decomposing it into water and molecular oxy-gen.[126] Also, as shown in Table 6, cofilin 1 was onlypresented in the classification and prognosis of ALL. It hasbeen reported that an overexpression of this protein is asso-ciated with certain types of malignancies.[127] Therefore, itcan be a great candidate for further study of ALL bio-markers as well.

On the other hand, from the rest of the proteins, valuableinformation has been obtained. Gluthatione S-transferese,proliferating cell nuclear antigen, and alpha-enolase wereobserved only in the classification of AML, ALL, and CML.As mentioned before, GST enzymes protect cells from carci-nogens and other toxicants through thioether linkages. Thepresence of GST in many forms of cancer, makes it a goodcandidate for use as a leukemia biomarker.[128] AlthoughPCNA was originally considered as a DNA sliding clamp forreplicative DNA polymerases, it can interact with multiplepartners involved in DNA repair, translesion DNA synthesis,


Table 5. Proteins related to drug resistance studies based on proteomics approaches.

M. Wt. (k Da) pI

Protein name Theo. Obs. Theo. Obs. Cell lines Protein alteration Leukemia type Ref.

Protein disulfide isomerase precursor – – – – HL-60/DOX vs. HL-60 –14.80 AML [37]

Proteasome a1 – – – – HL-60/DOX vs. HL-60 –6.92 AML [37]

Proteasome subunit beta type-4 29.20 – 5.70 – K562/A02 vs.K562

– CML [52]

Purine nucleoside phosphorylase – – – – HL-60/DOX vs. HL-60 þ16.70 AML [37]

– – – – HL-60/DOX vs. HL-60 only in HL-60/DOX AML [37]

Superoxide dismutase (Cu-Zn) – – – – HL-60/DOX vs. HL-60 –9.34 AML [37]

Metallothionein – – – – HL-60/DOX vs. HL-60 only in HL-60 AML [37]

N-chimaerin – – – – HL-60/DOX vs. HL-60 –2.96 AML [37]

WNT5B-precusor – – – – HL-60/DOX vs. HL-60 –10.41 AML [37]

Non-neuronal cytoplasmic intermediatefilament protein isoform

– – – – HL-60/DOX vs. HL-60 –19.64 AML [37]

Pre-mRNA splicing factor – – – – HL-60/DOX vs. HL-60 þ11.77 AML [37]

Bifunctional purine biosynthesis protein – – – – HL-60/DOX vs. HL-60 þ6.41 AML [37]

Inorganic pyrophosphatase – – – – HL-60/DOX vs. HL-60 –13.53 AML [37]

Inorganic pyrophosphatase 32.66 5.54 K562/A02 vs. K562 – CML [52]

SH3 and multiple ankyrin repeat domainsproteins 2 isoform

– – – – HL-60/DOX vs. HL-60 –14.29 AML [37]

Serum albumin (precursor) 69,293� 10-3a – 5.60 – HL-60/ADR vs. HL-60 cell þ1.21 acuteleukemia

[43]

Plastin-2 70,289� 10-3a – 5.20 – HL-60/ADR vs. HL-60 cell �1.22 acuteleukemia

[43]

Myc far upstream element-binding protein 67,560� 10-3a – 7.18 – HL-60/ADR vs. HL-60 �1.60 acuteleukemia

[43]

Nucleolin C23 76,614� 10-3a – 4.60 – HL-60/ADR vs. HL-60 cell þ2.42 acuteleukemia

[43]

CGI-46 51,157� 10-3a – 5.49 – HL-60/ADR vs. HL-60 cell �1.49 acuteleukemia

[43]

Calreticulin (precursor) 48,114� 10-3a – 4.29 – HL-60/ADR vs. HL-60 cell þ1.91 acuteleukemia

[43]

48.14 – 4.29 – K562/A02 vs.K562

þ CML [52]

Mutant beta-actin (b’-actin) 41,737� 10-3a – 5.29 – HL-60/ADR vs. HL-60 þ1.35 acuteleukemia

[43]

41,737� 10-3a – 5.29 – HL-60/ADR vs. HL-60 þ1.35 acuteleukemia

[43]

Elongation factor 43,547� 10-3a – 6.30 – HL-60/ADR vs. HL-60 þ1.31 acuteleukemia

[43]

2-Phosphopyruvate-hydratase alpha-enolase 47,169� 10-3a – 6.99 – HL-60/ADR vs. HL-60 þ1.42 acuteleukemia

[43]

Serine protease Htra2, mitochondrial 48,841� 10-3a – 6.13 – HL-60/ADR vs. HL-60 þ1.27 acuteleukemia

[43]

Porin 38,093� 10-3a – 7.50 – HL-60/ADR vs. HL-60 þ2.05 acuteleukemia

[43]

Ubiquitin carboxyl-terminal esterase L3 26,183� 10-3a – 4.84 – HL-60/ADR vs. HL-60 þ1.27 acuteleukemia

[43]

Endoplasmic reticulum protein 29 isoform 1precursor


[43]

ATP synthase 18,491� 10-3a – 5.22 – HL-60/ADR vs. HL-60 þ1.98 acuteleukemia

[43]

ATP synthase subunitbeta, mitochondrial

56.56 – 5.26 – K562/A02 vs.K562

– CML [52]

Nucleolar phosphoprotein B23 32,575� 10-3a – 4.64 – HL-60/ADR vs. HL-60 þ2.81 acuteleukemia

[43]


[43]

Nm23 protein 17,149� 10-3a – 5.83 – HL-60/ADR vs. HL-60 þ1.48 acuteleukemia

[43]

Heat shock 70 kDaprotein 1A/1B

70.05 – 5.47 – K562/A02vs. K562

þ CML [52]

T-complex protein 1subunit alpha

60.34 – 5.80 – K562/A02vs.K562

þ CML [52]

Serpin B9 42.40 – 5.61 – K562/A02vs. K562

þ CML [52]

Vimentin 53.65 – 5.05 – K562/A02vs. K562

þ CML [52]

Annexin 38.71 – 5.85 – K562/A02vs. K562

þ CML [52]

Inosine-50-monophosphatedehydrogenase 1

55.41 – 6.43 – K562/A02vs. K562

þ CML [52]

Creatine kinase U-type 43.08 – 7.31 – þ CML [52]

(continued)


Table 5. Continued.

M. Wt. (k Da) pI

Protein name Theo. Obs. Theo. Obs. Cell lines Protein alteration Leukemia type Ref.

K562/A02vs. K562

Fructose-bisphosphatealdolase A

39.42 – 8.30 – K562/A02vs. K562

þ CML [52]

Fructose-bisphosphatealdolase C

39.46 – 6.41 – K562/A02vs. K562

þ CML [52]

Rho GDP-dissociationinhibitor 1

23.21 – 5.01 – K562/A02vs. K562

þ CML [52]

L-lactate dehydrogenaseB chain

36.64 – 5.71 – K562/A02vs. K562

þ CML [52]

Chaperonin containingTCP1, subunit 2 (Beta),isoform CRA_b

57.49 – 6.01 – K562/A02vs. K562

þ CML [52]

Peroxiredoxin 6 25.03 – 6.00 – K562/A02vs. K562

þ CML [52]

Chloride intracellularchannel protein 1

26.92 – 5.09 – K562/A02vs. K562

þ CML [52]

Sorcin 21.68 – 5.32 – K562/A02vs. K562

þ CML [52]

Protein S100-A11 11.74 – 6.65 – K562/A02vs. K562

þ CML [52]

Phosphoglycerate kinase 1 44.61 – 8.30 – K562/A02vs. K562

þ CML [52]

Alpha-enolase 47.17 – 7.01 – K562/A02vs. K562

þ CML [52]

Sialic acid synthase 40.31 – 6.29 – K562/A02vs. K562

þ CML [52]

Ubiquitin-conjugatingenzyme E2 D2

16.74 – 7.69 – K562/A02vs. K562

þ CML [52]

Keratin, type IIcytoskeletal 8

53.71 – 5.52 – K562/A02vs. K562

þ CML [52]

Protein FAM50A 40.24 – 6.39 – K562/A02vs. K562

þ CML [52]

Peptidyl-prolyl cis-trans isomerase D 40.76 – 6.77 – K562/A02vs. K562

þ CML [52]

Epithelial cell adhesionmolecule

34.93 – 7.42 – K562/A02vs. K562

þ CML [52]

Transaldolase 37.54 – 6.36 – K562/A02vs. K562

þ CML [52]

Ornithine decarboxylase 51.15 – 5.10 – K562/A02vs. K562

þ CML [52]

Annexin A4 35.88 – 5.83 – K562/A02vs. K562

þ CML [52]

Glucose-6-phosphateisomerase

63.15 – 8.42 – K562/A02vs. K562

þ CML [52]

Keratin, type Icytoskeletal 18

48.06 – 5.34 – K562/A02vs. K562

þ CML [52]

Protein disulfideisomeraseA3

56.78 – 5.98 – K562/A02vs. K562

– CML [52]

Capping protein (Actinfilament), gelsolin-like

38.77 – 6.47 – K562/A02vs. K562

– CML [52]

Thioredoxin reductase 2, mitochondrial 56.51 – 7.24 – K562/A02vs. K562

– CML [52]

AP-2 complex subunitalpha-1

107.5 – 6.63 – K562/A02vs. K562

– CML [52]

Heat shock protein beta-2 20.23 – 5.07 – K562/A02vs. K562

– CML [52]

Aldo-keto reductasefamily 1 member C3

36.85 – 8.06 – K562/A02vs. K562

– CML [52]

Lymphocyte antigen 6E 13.51 – 8.06 – K562/A02vs. K562

– CML [52]

Prohibitin 29.80 – 5.57 – K562/A02vs. K562

– CML [52]

Receptor-type tyrosineproteinphosphatase U

16.24 – 6.46 – K562/A02vs. K562

– CML [52]

apolipoprotein E 36.15 – 5.65 – K562/A02vs. K562

– CML [52]

Heterogeneous nuclearribonucleoprotein F

45.67 – 5.37 – K562/A02vs. K562

– CML [52]

M.Wt., Molecular weight; pI, Isoelectric point; Theo., Theory; Obs., Observed; þ, increasing spot intensity; -, decreasing spot intensity.aIn the original paper, Da was reported as molecular weight unit.


DNA methylation, among others.[129] Therefore, it is notsurprising that PCNA appeared in leukemia identificationand/or classification proteomics studies. Since cancer cellsconsume more glucose than normal cells, several glucosetransporters and glycolytic enzymes in tumor cells causeupregulation, activation of alpha-enolase, and the presenceof alpha-enolase in tumor cells was confirmed.[130] Thesethree proteins may have a role in the etiology of leukemia,and further studies are warranted.

Although aldolase A, f-actin capping protein alpha 1,heat shock protein 60, protein disulfide isomerase, peroxire-doxin 2, peroxiredoxin 6, purine nucleoside phosphorylase,Rho GDP-dissociation inhibitor 1, stathmin, triosephosphateisomerase, and tubulin beta are not the specific identifica-tion biomarkers of AML (because these proteins wereobserved in the identification of ALL and/or CML), theseproteins have been observed specifically in AML prognosisor by the effect of AML medication or drug resistance inAML. Therefore, they may be important for further AMLstudies. Lactoferrin and serum albumin were observed in the

identification of ALL and AML. However, they were onlyobserved as an effect of medication on the ALL proteins.These two proteins are also good candidates for furtherALL studies.

Conclusion

Recent advancements in proteomics and MS tools haveenabled scientists to identify biomarkers of leukemia andstudy its molecular complexity, which leads to a decrease inthe mortality rate due to leukemia. As mentioned before,MALDI-TOF MS is one of the best candidates for clinicalproteomics studies. It can detect large biomolecules (in lessthan a second) in trace amounts and it is a quite simple andeconomical high-throughput method. In this review article,for the first time, the information on proteomics studiesrelated to leukemia with the aid of MALDI-TOF MS hasbeen gathered. Among hundreds of reported proteinsinvolved in leukemia, 42 proteins were involved in two or

Table 6. Proteins that have appeared in two or more stage of studies.

Type of leukemia in:

Protein name Classification Prognosis Medications effect Drug resistance

Actin beta (ACTB protein) ALL AML AMLActin gamma AML AMLAldolase A AML, CML AMLAlpha-enolase ALL, AML, CML AML CMLAnnexin 1 AML AMLApolipoprotein E ALL, AML CMLApolipoprotein A-1 AML AMLApolipoprotein A-IV AML AMLATP synthase subunit

beta, mitochondrialALL CML

Calreticulin ALL AML CMLCatalase AML AML AMLChaperonin containing TCP1, subunit 2 (beta) ALL AML CMLCofilin 1 (non-muscle) ALL ALLCyclophilin B AML AMLEndoplasmic reticulum protein 29 ALL AML Acute leukemiaF-actin capping protein alpha 1 ALL, AML AMLGlutathione S-transferase omega ALL, AML AMLGlutathione S-transferase P (GST-P) ALL, CML AMLHeat shock protein 60 kDa protein ALL, AML AMLHeat shock 70 kDa protein 1 AML CMLHeat shock 70 kDa protein 8 ALL AMLHeterogeneous nuclear ribonucleoprotein F ALL CMLInorganic pyrophosphatase ALL AML AML, CMLLactoferrin ALL, AML ALLNucleolin AML Acute leukemiaNucleophosmin ALL AMLProhibitin ALL AML CMLProliferating cell nuclear antigen (PCNA) ALL, AML, CML ALL AMLProtein disulfide isomerase ALL, AML AMLPeroxiredoxin 2 ALL, AML AMLperoxiredoxin 6 ALL, AML AML CMLPhosphoglycerate kinase 1 AML AML CMLPurine nucleoside

PhosphorylaseAML ALL AML

Rho GDP-dissociationinhibitor 1

ALL, AML AML CML

serum albumin ALL, AML ALL Acute leukemiaStathmin ALL, AML AMLSuperoxide Dismutase ALL AMLTriosephosphate isomerase ALL, AML AML AMLTropomyosin 3 ALL AMLTubulin beta ALL, AML AMLVimentin ALL CMLVoltage-dependent anion channel 2 ALL, AML AML


three different stages (classification and/or prognosis and/oreffect of leukemia’s medications on proteins and/or proteinsrelated to drug resistance, and/or relapse) of leukemia. Dueto presence of seven proteins (six for AML and one forALL) in two or three different stages of one type of leuke-mia, they have the potential to be further studied as bio-markers. Also, three proteins have been selected that mayhave a role in the etiology of leukemia, and further studiesare warranted. Based on the information provided in Table6, more proteins (eleven for AML, and two for ALL) havebeen observed that may be important for further studies.

Finally, we hope that this review article can help scientistsand researchers who are interested in proteomics studies ofleukemia find the missing part of the leukemia treat-ing puzzle.

Acknowledgment

The authors are gratefully acknowledged from financial support by theIran National Science Foundation, INSF (95007250).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Competing interest

The authors declare that they have no competing interests.

Abbreviations

ALL acute lymphoblastic leukemiaAML acute myeloid leukemiaCLL chronic lymphoblastic leukemiaCML chronic myeloid leukemiaMLL mixed lineage leukemiaPTM post-translational modificationsMS mass spectrometryESI electrospray ionizationQqQ triple quadrupolesQ-TOF quadrupole-time-of-flightTOF-TOF time-of-flight/time of flightMALDI-TOF MS matrix-assisted laser desorption/ionization time-

of-flight mass spectrometerTOF time of flightFAB French-American-British2-DE two-dimensional gel electrophoresisLAAs leukemia-associated antigensIgGs immunoglobinsBM bone marrowPB peripheral bloodGST-P glutathione S-transferase PGSH glutathioneBMC bone marrow mononuclear cell

PBMC peripheral blood mononuclear cellMARS multiple affinity removal system2D-DIGE two- dimensional differential in gel

electrophoresisMRD minimal residual disease.

Funding

This work was done based on postdoctoral research project of Dr.Negin Fasih Ramandi and supported by Iran National ScienceFoundation, INSF (95007250).

ORCID

Hassan Y. Aboul-Enein http://orcid.org/0000-0003-0249-7009

References

[1] Bain, B. J. Leukaemia Diagnosis: Fourth Edition; Wiley-Blackwell: New Jersey, 2010.

[2] Vora, A. Childhood Leukaemia: An Update. Paediatr. ChildHealth. 2016, 26, 51–56. DOI: 10.1016/j.paed.2015.10.007.

[3] Seth, R.; Singh, A. Leukemias in Children. Indian J. Pediatr.2015, 82, 817–824. DOI: 10.1007/s12098-015-1695-5.

[4] Andreeff, M. Targeted Therapy of Acute Myeloid Leukemia;Springer: New York, 2015. DOI: 10.1007/978-1-4939-1393-0.

[5] Ali, I.; Rahis-Ud-Din, Saleem, K.; Aboul-Enein, H. Y.; Rather,A. Social Aspect of Cancer Genesis. Cancer Ther. 2011, 8,6–14.

[6] Ali, I.; Saleem, K.; Wesselinova, D.; Haque, A. Synthesis, DNABinding, Hemolytic, and Anti-Cancer Assays of Curcumin I-Based Ligands and Their Ruthenium(III) Complexes. Med.Chem. Res. 2013, 22, 1386–1398. DOI: 10.1007/s00044-012-0133-8.

[7] Ali, I.; Wani, W. A.; Haque, A.; Saleem, K. Glutamic Acid andIts Derivatives: Candidates for Rational Design of AnticancerDrugs. Future Med. Chem. 2013, 5, 961–978. DOI: 10.4155/fmc.13.62.

[8] Ali, I.; Wani, W. A.; Saleem, K.; Hsieh, M.-F. AnticancerMetallodrugs of Glutamic Acid Sulphonamides: In Silico,DNA Binding, Hemolysis and Anticancer Studies. RSC Adv.2014, 4, 29629–29641. DOI: 10.1039/C4RA02570A.

[9] Ali, I.; Wani, W. A.; Saleem, K.; Haque, A. Thalidomide: ABanned Drug Resurged into Future Anticancer Drug. Curr.Cancer Drug Targets 2012, 7, 13–23. DOI: 10.2174/157488512800389164.

[10] Ali, I.; Wani, W. A.; Saleem, K.; Hseih, M.-F. Design andSynthesis of Thalidomide Based Dithiocarbamate Cu(II),Ni(II) and Ru(III) Complexes as Anticancer Agents.Polyhedron 2013, 56, 134–143. DOI: 10.1016/j.poly.2013.03.056.

[11] Saleem, K.; Wani, W. A.; Haque, A.; Lone, M. N.; Hsieh, M.-F.; Jairajpuri, M. A.; Ali, I. Synthesis, DNA Binding,Hemolysis Assays and Anticancer Studies of Copper(II),Nickel(II) and Iron(III) Complexes of a Pyrazoline-BasedLigand. Future Med. Chem. 2013, 5, 135–146. DOI: 10.4155/fmc.12.201.

[12] Ali, I.; Lone, M. N.; Alothman, Z. A.; Badjah, A. Y.; Alanazi,A. G. Spectroscopic and in Silico DNA Binding Studies on theInteraction of Some New N-Substituted Rhodanines with Calf-Thymus DNA: In Vitro Anticancer Activities. AnticancerAgents Med. Chem. 2019, 19, 425–433. DOI: 10.2174/1871520618666181002131125.

[13] Ali, I.; Lone, M. N.; Al-Othman, Z. A.; Al-Warthan, A.;Sanagi, M. M. Heterocyclic Scaffolds: Centrality in Anticancer


https://doi.org/10.1016/j.paed.2015.10.007

https://doi.org/10.1007/s12098-015-1695-5

https://doi.org/10.1007/978-1-4939-1393-0

https://doi.org/10.1007/s00044-012-0133-8

https://doi.org/10.1007/s00044-012-0133-8

https://doi.org/10.4155/fmc.13.62


https://doi.org/10.1039/C4RA02570A

https://doi.org/10.2174/157488512800389164

https://doi.org/10.2174/157488512800389164

https://doi.org/10.1016/j.poly.2013.03.056

https://doi.org/10.1016/j.poly.2013.03.056



https://doi.org/10.2174/1871520618666181002131125

https://doi.org/10.2174/1871520618666181002131125

Drug Development. Curr. Drug Targets 2015, 16, 711–734.DOI: 10.2174/1389450116666150309115922.

[14] Ali, I.; Lone, M. N.; Aboul-Enein, H. Y. Imidazoles asPotential Anticancer Agents. Med. Chem. Comm. 2017, 8,1742–1773. DOI: 10.1039/C7MD00067G.

[15] Ali, I.; Wani, W. A.; Khan, A.; Haque, A.; Ahmad, A.; Saleem,K.; Manzoor, N. Synthesis and Synergistic AntifungalActivities of a Pyrazoline Based Ligand and Its Copper(II) andNickel(II) Complexes with Conventional Antifungals. Microb.Pathog. 2012, 53, 66–73. DOI: 10.1016/j.micpath.2012.04.005.

[16] Ali, I.; Aboul-Enein, H. Y.; Ghanem, A. EnantioselectiveToxicity and Carcinogenesis. CPA 2005, 1, 109–125. DOI: 10.2174/1573412052953328.

[17] Ali, I. Nano Anti-Cancer Drugs: Pros and Cons and FuturePerspectives. Curr. Cancer Drug Targets 2011, 11, 131–134.DOI: 10.2174/156800911794328457.

[18] Ali, I. Nano Drugs: Novel Agents for Cancer Chemo-Therapy.Curr. Cancer Drug Targets 2011, 11, 130. DOI: 10.2174/156800911794328466.

[19] Ali, I.; Nadeem Lone, M.; Suhail, M.; Danish Mukhtar, S.;Asnin, L. Advances in Nanocarriers for Anticancer DrugsDelivery. Curr. Med. Chem. 2016, 23, 2159–2187. DOI: 10.2174/0929867323666160405111152.

[20] Ali, I.; Mukhtar, S. D.; Hsieh, M. F.; Alothman, Z. A.;Alwarthan, A. Facile Synthesis of Indole HeterocyclicCompounds Based Micellar Nano Anti-Cancer Drugs. RSCAdv. 2018, 8, 37905–37914. DOI: 10.1039/C8RA07060A.

[21] Ali, I.; Haque, A.; Wani, W. A.; Saleem, K.; Al Za’abi, M.Analyses of Anticancer Drugs by Capillary Electrophoresis: AReview. Biomed. Chromatogr. 2013, 27, 1296–1311. DOI: 10.1002/bmc.2953.

[22] Cooper, J.; Maupin, K.; Merrill, N. Origins of CancerSymposium 2015: Posttranslational Modifications and Cancer.Genes Cancer 2015, 6, 303–306. DOI: 10.18632/genesand-cancer.75.

[23] Mathot, P.; Grandin, M.; Devailly, G.; Souaze, F.; Cahais, V.;Moran, S.; Campone, M.; Herceg, Z.; Esteller, M.; Juin, P.;et al. DNA Methylation Signal Has a Major Role in theResponse of Human Breast Cancer Cells to theMicroenvironment. Oncogenesis 2017, 6, e390. DOI: 10.1038/oncsis.2017.88.

[24] Massimo, L.; Corrias, M. V.; Tonini, G. P. Why Do CancerOmics Attract Clinicians so Much? OMICS 2011, 15, 123–124.DOI: 10.1089/omi.2010.0081.

[25] Garay, J. P.; Gray, J. W. Omics and Therapy—A Basis forPrecision medicine. Mol. Oncol. 2012, 6, 128–139. DOI: 10.1016/j.molonc.2012.02.009.

[26] Cho, W. C. Mass Spectrometry-Based Proteomics in CancerResearch. Expert Rev. Proteomics 2017, 14, 725–727. DOI: 10.1080/14789450.2017.1365604.

[27] Liu, X.; Zheng, W.; Wang, W.; Shen, H.; Liu, L.; Lou, W.;Wang, X.; Yang, P. A New Panel of Pancreatic CancerBiomarkers Discovered Using a Mass Spectrometry-BasedPipeline. Br. J. Cancer. 2017, 117, 1846–1854. DOI: 10.1038/bjc.2017.365.

[28] Swami, M. Proteomics: A Discovery Strategy for Novel CancerBiomarkers. Nat Rev Cancer. 2010, 10, 597. DOI: 10.1038/nrc2922.

[29] Zhou, Y.; Xu, X.; Tian, Z.; Wei, H. "Multi-Omics" Analyses ofthe Development and Function of Natural Killer Cells. Front.Immunol. 2017, 8, 1095. DOI: 10.3389/fimmu.2017.01095.

[30] Manikandan, M.; Deenadayalan, A.; Vimala, A.; Gopal, J.;Chun, S. Clinical MALDI Mass Spectrometry for TuberculosisDiagnostics: Speculating the Methodological Blueprint andContemplating the Obligation to Improvise. TrAC TrendsAnal. Chem. 2017, 94, 190–199. DOI: 10.1016/j.trac.2017.06.014.

[31] Hosseini, S.; Martinez-Chapa, S. O. Principles and Mechanismof MALDI-ToF-MS Analysis. In Fundamentals of MALDI-ToF-MS Analysis; Amit Kumar and Allam Appa Rao, Eds.;

Springer: Singapore, 2017, pp 1–19. DOI: 10.1007/978-981-10-2356-9_1.

[32] Boyd, R. S.; Adam, P. J.; Patel, S.; Loader, J. A.; Berry, J.;Redpath, N. T.; Poyser, H. R.; Fletcher, G. C.; Burgess, N. A.;Stamps, A. C.; et al. Proteomic Analysis of the Cell-SurfaceMembrane in Chronic Lymphocytic Leukemia: Identificationof Two Novel Proteins, BCNP1 and MIG2B. Leukemia 2003,17, 1605–1612. DOI: 10.1038/sj.leu.2402993.

[33] Cui, J. W.; Wang, J.; He, K.; Jin, B. F.; Wang, H. X.; Li, W.;Kang, L. H.; Hu, M. R.; Li, H. Y.; Yu, M.; et al. ProteomicAnalysis of Human Acute Leukemia Cells: Insight into TheirClassification. Clin. Cancer Res. 2004, 10, 6887–6896. DOI: 10.1158/1078-0432.CCR-04-0307.

[34] Grebenov�a, D.; Kuzelov�a, K.; Fuchs, O.; Halada, P.; Havl�ıcek,V.; Marinov, I.; Hrkal, Z. Interferon-Alpha SuppressesProliferation of Chronic Myelogenous Leukemia Cells K562 byExtending Cell Cycle S-Phase without Inducing Apoptosis.Blood Cells Mol. Dis. 2004, 32, 262–269. DOI: 10.1016/j.bcmd.2003.10.008.

[35] Zou, L.; Wu, Y.; Pei, L.; Zhong, D.; Gen, M.; Zhao, T.; Wu, J.;Ni, B.; Mou, Z.; Han, J.; et al. Identification of Leukemia-Associated Antigens in Chronic Myeloid Leukemia byProteomic Analysis. Leuk. Res. 2005, 29, 1387–1391. DOI: 10.1016/j.leukres.2005.04.021.

[36] Xia, Q.; Wang, H. X.; Wang, J.; Zhang, J. Y.; Liu, B. Y.; Li,A. L.; Lv, M.; Hu, M. R.; Yu, M.; Feng, J. N.; et al. ProteomicAnalysis of Interleukin 6-Induced Differentiation in MouseMyeloid Leukemia Cells. Int. J. Biochem. Cell Biol. 2005, 37,1197–1207. DOI: 10.1016/j.biocel.2004.11.015.

[37] Chen, C. Y.; Jia, J. H.; Zhang, M. X.; Meng, Y. S.; Kong, D. X.;Pan, X. L.; Yu, X. P. Proteomic Analysis on Multi-DrugResistant Cells HL-60/DOX of Acute Myeloblastic Leukemia.Chin. J. Physiol. 2005, 48, 115–120.

[38] Pizzatti, L.; S�a, L. A.; de Souza, J. M.; Bisch, P. M.; Abdelhay,E. Altered Protein Profile in Chronic Myeloid LeukemiaChronic Phase Identified by a Comparative Proteomic Study.Biochim. Biophys. Acta. 2006, 1764, 929–942. DOI: 10.1016/j.bbapap.2006.02.004.

[39] L�opez-Pedrera, C.; Villalba, J. M.; Siendones, E.; Barbarroja,N.; G�omez-D�ıaz, C.; Rodr�ıguez-Ariza, A.; Buend�ıa, P.; Torres,A.; Velasco, F. Proteomic Analysis of Acute MyeloidLeukemia: Identification of Potential Early Biomarkers andTherapeutic Targets. Proteomics 2006, 6, S293–S299. DOI: 10.1002/pmic.200500384.

[40] Liang, T.; Wang, N.; Li, W.; Li, A.; Wang, J.; Cui, J.; Liu, N.;Li, Y.; Li, N.; Yang, G.; et al. Identification of ComplementC3f-desArg and Its Derivative for Acute Leukemia Diagnosisand Minimal Residual Disease Assessment. Proteomics 2010,10, 90–98. DOI: 10.1002/pmic.200900513.

[41] Braoudak, I. M.; Tzortzatou-Stathopoulou, F.;Anagnostopoulos, A. K.; Papathanassiou, C.; Vougas, K.;Karamolegou, K.; Tsangaris, G. T. Proteomic Analysis ofChildhood de Novo Acute Myeloid Leukemia andMyelodysplastic Syndrome/AML: Correlation to Molecular andCytogenetic Analyses. Amino Acids 2011, 40, 943–951. DOI:10.1007/s00726-010-0718-9.

[42] Buchi, F.; Pastorelli, R.; Ferrari, G.; Spinelli, E.; Gozzini, A.;Sassolini, F.; Bosi, A.; Tombaccini, D.; Santini, V. Acetylomeand Phosphoproteome Modifications in Imatinib ResistantChronic Myeloid Leukaemia Cells Treated with Valproic Acid.Leuk. Res. 2011, 35, 921–931. DOI: 10.1016/j.leukres.2011.01.033.

[43] Hu, J.; Lin, M.; Liu, T.; Li, J.; Chen, B.; Chen, Y. DIGE-BasedProteomic Analysis Identifies Nucleophosmin/B23 andNucleolin C23 as over-Expressed Proteins in Relapsed/Refractory Acute Leukemia. Leuk. Res. 2011, 35, 1087–1092.DOI: 10.1016/j.leukres.2011.01.010.

[44] Jiang, N.; Kham, S. K. Y.; Koh, G. S.; Suang Lim, J. Y.; Ariffin,H.; Chew, F. T.; Yeoh, A. E. J. Identification of PrognosticProtein Biomarkers in Childhood Acute Lymphoblastic


https://doi.org/10.2174/1389450116666150309115922

https://doi.org/10.1039/C7MD00067G

https://doi.org/10.1016/j.micpath.2012.04.005

https://doi.org/10.2174/1573412052953328

https://doi.org/10.2174/1573412052953328

https://doi.org/10.2174/156800911794328457

https://doi.org/10.2174/156800911794328466

https://doi.org/10.2174/156800911794328466

https://doi.org/10.2174/0929867323666160405111152

https://doi.org/10.2174/0929867323666160405111152

https://doi.org/10.1039/C8RA07060A

https://doi.org/10.1002/bmc.2953


https://doi.org/10.18632/genesandcancer.75

https://doi.org/10.18632/genesandcancer.75

https://doi.org/10.1038/oncsis.2017.88

https://doi.org/10.1038/oncsis.2017.88

https://doi.org/10.1089/omi.2010.0081

https://doi.org/10.1016/j.molonc.2012.02.009

https://doi.org/10.1016/j.molonc.2012.02.009

https://doi.org/10.1080/14789450.2017.1365604

https://doi.org/10.1080/14789450.2017.1365604

https://doi.org/10.1038/bjc.2017.365

https://doi.org/10.1038/bjc.2017.365

https://doi.org/10.1038/nrc2922

https://doi.org/10.1038/nrc2922

https://doi.org/10.3389/fimmu.2017.01095

https://doi.org/10.1016/j.trac.2017.06.014

https://doi.org/10.1016/j.trac.2017.06.014

https://doi.org/10.1007/978-981-10-2356-9_1

https://doi.org/10.1007/978-981-10-2356-9_1

https://doi.org/10.1038/sj.leu.2402993

https://doi.org/10.1158/1078-0432.CCR-04-0307

https://doi.org/10.1158/1078-0432.CCR-04-0307

https://doi.org/10.1016/j.bcmd.2003.10.008

https://doi.org/10.1016/j.bcmd.2003.10.008

https://doi.org/10.1016/j.leukres.2005.04.021


https://doi.org/10.1016/j.biocel.2004.11.015

https://doi.org/10.1016/j.bbapap.2006.02.004

https://doi.org/10.1016/j.bbapap.2006.02.004

https://doi.org/10.1002/pmic.200500384



https://doi.org/10.1007/s00726-010-0718-9




Leukemia (ALL). J. Proteomics 2011, 74, 843–857. DOI: 10.1016/j.jprot.2011.02.034.

[45] Saha, S.; Halder, S.; Bhattacharya, D.; Banerjee, D.;Chakrabarti, A. Fractional Precipitation of Plasma Proteomeby Ammonium Sulphate: Case Studies in Leukemia andThalassemia. J. Proteomics Bioinform. 2012, 5, 163–171. DOI:10.4172/jpb.1000230.

[46] Buchi, F.; Spinelli, E.; Masala, E.; Gozzini, A.; Sanna, A.; Bosi,A.; Ferrari, G.; Santini, V. Proteomic Analysis IdentifiesDifferentially Expressed Proteins in AML1/ETO AcuteMyeloid Leukemia Cells Treated with DNMT InhibitorsAzacitidine and Decitabine. Leuk. Res. 2012, 36, 607–618.DOI: 10.1016/j.leukres.2011.11.024.

[47] Wang, D.; Lv, Y-q.; Liu, Y-f.; Du, X-j.; Li, B. DifferentialProtein Analysis of Lymphocytes between Children with AcuteLymphoblastic Leukemia and Healthy Children. Leuk.Lymphoma 2013, 54, 381–386. DOI: 10.3109/10428194.2012.713104.

[48] Ka�zmierczak, M.; Luczak, M.; Lewandowski, K.; Handschuh,L.; Czy_z, A.; Jarmu_z, M.; Gniot, M.; Michalak, M.; Figlerowicz,M.; Komarnicki, M. Esterase D and Gamma 1 Actin LevelMight Predict Results of Induction Therapy in Patients withAcute Myeloid Leukemia without and with Maturation. Med.Oncol. 2013, 30, 725. DOI: 10.1007/s12032-013-0725-2.

[49] Bai, J.; He, A.; Zhang, W.; Huang, C.; Yang, J.; Yang, Y.;Wang, J.; Zhang, Y. Potential Biomarkers for Adult AcuteMyeloid Leukemia Minimal Residual Disease AssessmentSearched by Serum Peptidome profiling. Proteome Sci. 2013,11, 39. DOI: 10.1186/1477-5956-11-39.

[50] Saha, S.; Banerjee, S.; Banerjee, D.; Chandra, S.; Chakrabarti,A. 2DGE and DIGE Based Proteomic Study of Malignant B-Cells in B-Cell Acute Lymphoblastic Leukemia. EuPA OpenProteom. 2014, 3, 13–26. DOI: 10.1016/j.euprot.2014.01.002.

[51] Alsagaby, S. A.; Khanna, S.; Hart, K. W.; Pratt, G.; Fegan, C.;Pepper, C.; Brewis, I. A.; Brennan, P. Proteomics-BasedStrategies to Identify Proteins Relevant to ChronicLymphocytic Leukemia. J. Proteome Res. 2014, 13, 5051–5062.DOI: 10.1021/pr5002803.

[52] Qinghong, S.; Shen, G.; Lina, S.; Yueming, Z.; Xiaoou, L.;Jianlin, W.; Chengyan, H.; Hongjun, L.; Haifeng, Z.Comparative Proteomics Analysis of Differential Proteins inRespond to Doxorubicin Resistance in Myelogenous LeukemiaCell Lines. Proteome Sci. 2015, 13, 1. DOI: 10.1186/s12953-014-0057-y.

[53] Raza, S. K.; Saleem, M.; Shamsi, T.; Choudhary, M. I.; AttaUr, R.; Musharraf, S. G. 5D Proteomic Approach for theBiomarker Search in Plasma: Acute Myeloid Leukaemia as aCase Study. Sci. Rep. 2017, 7, 16440. DOI: 10.1038/s41598-017-16699-2.

[54] Zyubin, A.; Lavrova, A.; Demin, M.; Pankina, A.; Babak, S.The Data Obtained during the Analysis of Clinical BloodSamples for Children Acute Lymphoblastic Leukemia Patientswith Severe Side-Effects. Data Brief 2017, 11, 522–526. DOI:10.1016/j.dib.2017.03.016.

[55] Liu, B.; Ma, X.; Liu, Q.; Xiao, Y.; Pan, S.; Jia, L. AberrantMannosylation Profile and FTX/miR-342/ALG3-AxisContribute to Development of Drug Resistance in AcuteMyeloid Leukemia. Cell Death Dis. 2018, 9, 688. DOI: 10.1038/s41419-020-2320-8.

[56] Jain, P.; Ojha, S. K.; Kumar, V.; Bakhshi, S.; Singh, S.; Yadav,S. Differential Seminal Plasma Proteome Signatures of AcuteLymphoblastic Leukemia survivors. Reprod. Biol. 2019, 19,322–328. DOI: 10.1016/j.repbio.2019.11.002.

[57] Nijenhuis, C. M.; Lucas, L.; Rosing, H.; Jamieson, G.; Fox,J. A.; Schellens, J. H. M.; Beijnen, J. H. Quantification ofVosaroxin and Its Metabolites N-Desmethylvosaroxin and O-Desmethylvosaroxin in Human Plasma and Urine Using High-Performance Liquid Chromatography-Tandem MassSpectrometry. J. Chromatogr. B Analyt Technol. Biomed. LifeSci. 2016, 1027, 1–10. DOI: 10.1016/j.jchromb.2016.05.016.

[58] Gavasso, S.; Gullaksen, S.-E.; Skavland, J.; Gjertsen, B. T.Single-Cell Proteomics: Potential Implications for CancerDiagnostics. Expert Rev. Mol. Diagn. 2016, 16, 579–589. DOI:10.1586/14737159.2016.1156531.

[59] Hernandez-Valladares, M.; Aasebø, E.; Selheim, F.; Berven,F. S. Bruserud, Ø. Selecting Sample Preparation Workflows forMass Spectrometry-Based Proteomic and PhosphoproteomicAnalysis of Patient Samples with Acute Myeloid Leukemia.Proteomes 2016, 4, 24. DOI: 10.3390/proteomes4030024.

[60] Elise, A.; Rakel, B. F.; Frode, B.; Frode, S.; Maria, H.-V. GlobalCell Proteome Profiling, Phospho-Signaling and QuantitativeProteomics for Identification of New Biomarkers in AcuteMyeloid Leukemia Patients. Curr. Pharm. Biotechnol. 2016, 17,52–70. DOI: 10.2174/1389201016666150826115626.

[61] Glazyrin, Y. E.; Komarova, M. A.; Bakhtina, V. I.; Silacheva,M. V.; Demko, I. V.; Zamay, A. S.; Zamay, T. N. AComparative Protein Profiling of Lymphocytes from Blood ofPatients with Chronic Lymphoid Leukemia by High-Resolution Mass-Spectrometry in Search of New Markers forHeterogeneity and Disease Prognosis. J. Anal. Chem. 2016, 71,1242–1250. DOI: 10.1134/S1061934816130062.

[62] D�ıez, P.; Lorenzo, S.; D�egano, R. M.; Ibarrola, N.; Gonz�alez-Gonz�alez, M.; Nieto, W.; Almeida, J.; Gonz�alez, M.; Orfao, A.;Fuentes, M. Multipronged Functional Proteomics Approachesfor Global Identification of Altered Cell Signalling Pathways inB-Cell Chronic Lymphocytic Leukaemia. Proteomics 2016, 16,1193–1203. DOI: 10.1002/pmic.201500372.

[63] Nakahara, R.; Satho, Y.; Itoh, H. High-Performance LiquidChromatographic Ultraviolet Detection of Nilotinib in HumanPlasma from Patients with Chronic Myelogenous Leukemia,and Comparison with Liquid Chromatography–Tandem MassSpectrometry. J. Clin. Lab. Anal. 2016, 30, 1028–1030. DOI:10.1002/jcla.21975.

[64] Chen, D.; Fan, F.; Zhao, X.; Xu, F.; Chen, P.; Wang, J.; Ban,L.; Liu, Z.; Feng, X.; Zhang, Y.; Liu, B.-F. Single Cell ChemicalProteomics with Membrane-Permeable Activity-Based Probefor Identification of Functional Proteins in Lysosome ofTumors. Anal. Chem. 2016, 88, 2466–2471. DOI: 10.1021/acs.analchem.5b04645.

[65] Unnikrishnan, A.; Guan, Y. F.; Huang, Y.; Beck, D.; Thoms,J. A. I.; Peirs, S.; Knezevic, K.; Ma, S.; de Walle, I. V.; de Jong,I.; et al. A Quantitative Proteomics Approach Identifies ETV6and IKZF1 as New Regulators of an ERG-DrivenTranscriptional Network. Nucleic Acids Res. 2016, 44,10644–10661. DOI: 10.1093/nar/gkw804.

[66] Roboz, G. J.; Roboz, J. The Application of Mass Spectrometryto Leukemia Drug Discovery. Expert Opin. Drug Discov. 2016,11, 1029–1032. DOI: 10.1080/17460441.2016.1233175.

[67] Novikova, S. E.; Zgoda, V. G. [Transcriptomics andProteomics in Studies of Induced Differentiation of LeukemiaCells]. Biomed. Khim. 2015, 61, 529–544. DOI: 10.18097/PBMC20156105529.

[68] Halbach, S.; Dengjel, J.; Brummer, T. Quantitative ProteomicsAnalysis of Leukemia Cells. In: Chronic Myeloid Leukemia:Methods and Protocols; Li, S., Zhang, H., Eds.; Springer: NewYork. 2016, pp 139–148. DOI: 10.1007/978-1-4939-4011-0.

[69] Xu, G.; Li, Z.; Wang, L.; Chen, F.; Chi, Z.; Gu, M.; Li, S.; Wu,D.; Miao, J.; Zhang, Y.; et al. Label-Free QuantitativeProteomics Reveals Differentially Expressed Proteins in HighRisk Childhood Acute Lymphoblastic Leukemia. J. Proteomics.2017, 150, 1–8. DOI: 10.1016/j.jprot.2016.08.014.

[70] Perna, F.; Berman, S. H.; Soni, R. K.; Mansilla-Soto, J.;Eyquem, J.; Hamieh, M.; Hendrickson, R. C.; Brennan, C. W.;Sadelain, M. Integrating Proteomics and Transcriptomics forSystematic Combinatorial Chimeric Antigen Receptor Therapyof AML. Cancer Cell 2017, 32, 506–519.e5. DOI: 10.1016/j.ccell.2017.09.004.

[71] Johnston, H. E.; Carter, M. J.; Cox, K. L.; Dunscombe, M.;Manousopoulou, A.; Townsend, P. A.; Garbis, S. D.; Cragg,M. S. Integrated Cellular and Plasma Proteomics of


https://doi.org/10.1016/j.jprot.2011.02.034


https://doi.org/10.4172/jpb.1000230


https://doi.org/10.3109/10428194.2012.713104

https://doi.org/10.3109/10428194.2012.713104

https://doi.org/10.1007/s12032-013-0725-2

https://doi.org/10.1186/1477-5956-11-39

https://doi.org/10.1016/j.euprot.2014.01.002

https://doi.org/10.1021/pr5002803

https://doi.org/10.1186/s12953-014-0057-y

https://doi.org/10.1186/s12953-014-0057-y

https://doi.org/10.1038/s41598-017-16699-2

https://doi.org/10.1038/s41598-017-16699-2

https://doi.org/10.1016/j.dib.2017.03.016

https://doi.org/10.1038/s41419-020-2320-8

https://doi.org/10.1038/s41419-020-2320-8

https://doi.org/10.1016/j.repbio.2019.11.002

https://doi.org/10.1016/j.jchromb.2016.05.016

https://doi.org/10.1586/14737159.2016.1156531

https://doi.org/10.3390/proteomes4030024

https://doi.org/10.2174/1389201016666150826115626

https://doi.org/10.1134/S1061934816130062


https://doi.org/10.1002/jcla.21975

https://doi.org/10.1021/acs.analchem.5b04645


https://doi.org/10.1093/nar/gkw804

https://doi.org/10.1080/17460441.2016.1233175

https://doi.org/10.18097/PBMC20156105529

https://doi.org/10.18097/PBMC20156105529

https://doi.org/10.1007/978-1-4939-4011-0


https://doi.org/10.1016/j.ccell.2017.09.004

https://doi.org/10.1016/j.ccell.2017.09.004

Contrasting B-Cell Cancers Reveals Common, Unique andSystemic Signatures. Mol. Cell. Proteomics 2017, 16, 386–406.DOI: 10.1074/mcp.M116.063511.

[72] Supandi, S.; Harahap, Y.; Harmita, H.; Andalusia, R.; Ika, M.Analysis of 6-Mercaptopurine and 6-Methylmercaptopurine inDried Blood Spots Using Liquid Chromatography-TandemMass Spectrometry and Its Application in Childhood AcuteLymphoblastic Leukemia Patients. Asian J. Pharm. Clin. Res.2017, 10, 120–125. DOI: 10.22159/ajpcr.2017.v10i9.19790.

[73] Ali, S. A.; Kaur, G.; Kaushik, J. K.; Malakar, D.; Mohanty,A. K.; Kumar, S. Examination of Pathways Involved inLeukemia Inhibitory Factor (LIF)-Induced Cell Growth ArrestUsing Label-Free Proteomics Approach. J. Proteomics 2017,168, 37–52. DOI: 10.1016/j.jprot.2017.07.014.

[74] Zeng, Y.; Deng, F.-Y.; Zhu, W.; Zhang, L.; He, H.; Xu, C.;Tian, Q.; Zhang, J.-G.; Zhang, L.-S.; Hu, H.-G.; Deng, H.-W.Mass Spectrometry Based Proteomics Profiling of HumanMonocytes. Protein Cell 2017, 8, 123–133. DOI: 10.1007/s13238-016-0342-x.

[75] Reckel, S.; Hamelin, R.; Georgeon, S.; Armand, F.; Jolliet, Q.;Chiappe, D.; Moniatte, M.; Hantschel, O. Differential SignalingNetworks of Bcr-Abl p210 and p190 Kinases in LeukemiaCells Defined by Functional Proteomics. Leukemia 2017, 31,1502–1512. DOI: 10.1038/leu.2017.36.

[76] Matondo, M.; Marcellin, M.; Chaoui, K.; Bousquet-Dubouch,M.-P.; Gonzalez-de-Peredo, A.; Monsarrat, B.; Burlet-Schiltz,O. Determination of Differentially Regulated Proteins uponProteasome Inhibition in AML Cell Lines by the Combinationof Large-Scale and Targeted Quantitative Proteomics.Proteomics 2017, 17, 1600089. DOI: 10.1002/pmic.201600089.

[77] Chebouba, L.; Miannay, B.; Boughaci, D.; Guziolowski, C.Discriminate the Response of Acute Myeloid LeukemiaPatients to Treatment by Using Proteomics Data and AnswerSet Programming. BMC Bioinformatics. 2018, 19, 59. DOI: 10.1186/s12859-018-2034-4.

[78] Johnston, H. E.; Carter, M. J.; Larrayoz, M.; Clarke, J.; Garbis,S. D.; Oscier, D.; Strefford, J. C.; Steele, A. J.; Walewska, R.;Cragg, M. S. Proteomics Profiling of CLL versus Healthy B-Cells Identifies Putative Therapeutic Targets and a Subtype-Independent Signature of Spliceosome Dysregulation. Mol.Cell. Proteomics 2018, 17, 776–791. DOI: 10.1074/mcp.RA117.000539.

[79] Monteleone, F.; Taverna, S.; Alessandro, R.; Fontana, S.SWATH-MS Based Quantitative Proteomics Analysis RevealsThat Curcumin Alters the Metabolic Enzyme Profile of CMLCells by Affecting the Activity of miR-22/IPO7/HIF-1a Axis. J.Exp. Clin. Cancer Res. 2018, 37, 170. DOI: 10.1186/s13046-018-0843-y.

[80] Cifani, P.; Dhabaria, A.; Chen, Z.; Yoshimi, A.; Kawaler, E.;Abdel-Wahab, O.; Poirier, J. T.; Kentsis, A.ProteomeGenerator: A Framework for ComprehensiveProteomics Based on de Novo Transcriptome Assembly andHigh-Accuracy Peptide Mass Spectral Matching. J. ProteomeRes. 2018, 17, 3681–3692. DOI: 10.1021/acs.jproteome.8b00295.

[81] Ramadan, A. M.; Daguindau, E.; Rech, J. C.; Chinnaswamy,K.; Zhang, J.; Hura, G. L.; Griesenauer, B.; Bolten, Z.; Robida,A.; Larsen, M.; et al. From Proteomics to Discovery of First-in-Class ST2 Inhibitors Active in Vivo. JCI Insight 2018, 3,e99208. DOI: 10.1172/jci.insight.99208.

[82] Schmidt, J. R.; R€ucker-Braun, E.; Heidrich, K.; Bonin, M.;St€olzel, F.; Thiede, C.; Middeke, J. M.; Ehninger, G.;Bornh€auser, M.; Schetelig, J.; et al. Pilot Study on MassSpectrometry–Based Analysis of the Proteome ofCD34þCD123þ Progenitor Cells for the Identification ofPotential Targets for Immunotherapy in Acute MyeloidLeukemia. Proteomes 2018, 6, 11–23. DOI: 10.3390/proteomes6010011.

[83] Mayer, R. L.; Schwarzmeier, J. D.; Gerner, M. C.; Bileck, A.;Mader, J. C.; Meier-Menches, S. M.; Gerner, S. M.;

Schmetterer, K. G.; Pukrop, T.; Reichle, A.; et al. Proteomicsand Metabolomics Identify Molecular Mechanisms of AgingPotentially Predisposing for Chronic Lymphocytic Leukemia.Mol. Cell. Proteomics 2018, 17, 290–303. DOI: 10.1074/mcp.RA117.000425.

[84] McManus, F. P.; Bourdeau, V.; Acevedo, M.; Lopes-Paciencia,S.; Mignacca, L.; Lamoliatte, F.; Pino, J. W. R.; Ferbeyre, G.;Thibault, P. Quantitative SUMO Proteomics Reveals theModulation of Several PML Nuclear Body Associated Proteinsand an Anti-Senescence Function of UBC9. Sci. Rep. 2018, 8,7754. DOI: 10.1038/s41598-018-25150-z.

[85] Leng, P.; Yang, Z.; Ma, B.; Li, J.; Sun, J.; Xie, Y.; Sun, Y.Simultaneous Determination of AM80 (Tamibarotene) andWJD-A-1 in Rat Plasma by Ultra High-Performance LiquidChromatography–Tandem Mass Spectrometry and ItsApplication to a Pharmacokinetic Study. Artif. Cells. Nanomed.Biotechnol. 2018, 46, 1131–1137. DOI: 10.1080/21691401.2018.1446443.

[86] Wang, D.; Tan, G.; Wang, H.; Chen, P.; Hao, J.; Wang, Y.Identification of Novel Serum Biomarker for the Detection ofAcute Myeloid Leukemia Based on Liquid Chromatography-Mass Spectrometry. J. Pharm. Biomed. Anal. 2019, 166,357–363. DOI: 10.1016/j.jpba.2019.01.022.

[87] Paczesny, S.; Metzger, J. Clinical Proteomics for Post-Hematopoeitic Stem Cell Transplantation Outcomes. Prot.Clin. Appl. 2019, 13, 1800145. DOI: 10.1002/prca.201800145.

[88] Tsai, C.-F.; Zhao, R.; Williams, S. M.; Moore, R. J.; Schultz, K.;Chrisler, W.; Pasa-Tolic, L.; Rodland, K.; Smith, R. D.; Shi, T.;et al. An Improved Boosting to Amplify Signal with IsobaricLabeling (iBASIL) Strategy for Precise Quantitative Single-CellProteomics. Mol. Cell. Proteomics 2020, 19, 828–838. DOI: 10.1074/mcp.RA119.001857.

[89] Ghosh, M.; Gauger, M.; Marcu, A.; Nelde, A.; Denk, M.;Schuster, H.; Rammensee, H.-G.; Stevanovi�c, S. GuidanceDocument: Validation of a High-Performance LiquidChromatography-Tandem Mass SpectrometryImmunopeptidomics Assay for the Identification of HLA ClassI Ligands Suitable for Pharmaceutical Therapies. Mol. CellProteomics 2020, 19, 432–443. DOI: 10.1074/mcp.C119.001652.

[90] Gao, L.; Xu, Q.; Liu, Q.; Lang, X. In Vitro MetabolitesCharacterization of Ponatinib in Human Liver MicrosomesUsing Ultra-High Performance Liquid ChromatographyCombined with Q-Exactive-Orbitrap Tandem MassSpectrometry. Biomed. Chromatogr. 2020, 34, e4819. DOI: 10.1002/bmc.4819.

[91] Li, C.; McManus, F. P.; Plutoni, C.; Pascariu, C. M.; Nelson,T.; Alberici Delsin, L. E.; Emery, G.; Thibault, P. QuantitativeSUMO Proteomics Identifies PIAS1 Substrates Involved inCell Migration and motility. Nat. Commun. 2020, 11, 834.DOI: 10.1038/s41467-020-14581-w.

[92] Alanazi, B.; Munje, C. R.; Rastogi, N.; Williamson, A. J. K.;Taylor, S.; Hole, P. S.; Hodges, M.; Doyle, M.; Baker, S.;Gilkes, A. F.; et al. Integrated Nuclear Proteomics andTranscriptomics Identifies S100A4 as a Therapeutic Target inAcute Myeloid Leukemia. Leukemia 2020, 34, 427–440. DOI:10.1038/s41375-019-0596-4.

[93] Dwivedi, P.; Chutipongtanate, S.; Muench, D. E.; Azam, M.;Grimes, H. L.; Greis, K. D. SWATH-Proteomics of Ibrutinib’sAction in Myeloid Leukemia Initiating Mutated G-CSFRSignaling. Prot. Clin. Appl. 2020, 14, 1900144. DOI: 10.1002/prca.201900144.

[94] Shah, H. N.; Ghariba, S. E. MALDI-TOF and Tandem MS forClinical Microbiology. John Wiley & Sons Ltd: Chichester,2017. DOI: 10.1002/9781118960226.

[95] Cho, Y.-T.; Su, H.; Wu, W.-J.; Wu, D.-C.; Hou, M.-F.; Kuo,C.-H.; Shiea, J. Chapter Six—Biomarker Characterization byMALDI–TOF/MS. In: Advances in Clinical Chemistry,Makowski, G. S., Ed., Elsevier: Netherlands, 2015, pp 209–254.DOI: 10.1016/bs.acc.2015.01.001.


https://doi.org/10.1074/mcp.M116.063511

https://doi.org/10.22159/ajpcr.2017.v10i9.19790


https://doi.org/10.1007/s13238-016-0342-x

https://doi.org/10.1007/s13238-016-0342-x

https://doi.org/10.1038/leu.2017.36


https://doi.org/10.1186/s12859-018-2034-4

https://doi.org/10.1186/s12859-018-2034-4

https://doi.org/10.1074/mcp.RA117.000539


https://doi.org/10.1186/s13046-018-0843-y

https://doi.org/10.1186/s13046-018-0843-y

https://doi.org/10.1021/acs.jproteome.8b00295

https://doi.org/10.1021/acs.jproteome.8b00295

https://doi.org/10.1172/jci.insight.99208





https://doi.org/10.1038/s41598-018-25150-z

https://doi.org/10.1080/21691401.2018.1446443

https://doi.org/10.1080/21691401.2018.1446443

https://doi.org/10.1016/j.jpba.2019.01.022

https://doi.org/10.1002/prca.201800145



https://doi.org/10.1074/mcp.C119.001652



https://doi.org/10.1038/s41467-020-14581-w

https://doi.org/10.1038/s41375-019-0596-4



https://doi.org/10.1002/9781118960226

https://doi.org/10.1016/bs.acc.2015.01.001

[96] Pusch, W.; Kostrzewa, M. Application of MALDI-TOF MassSpectrometry in Screening and Diagnostic Research. Curr.Pharm. Des. 2005, 11, 2577–2591. DOI: 10.2174/1381612054546932.

[97] Karpova, M. A.; Moshkovskii, S. A.; Toropygin, I. Y.;Archakov, A. I. Cancer-Specific MALDI-TOF Profiles of BloodSerum and Plasma: Biological Meaning and Perspectives. J.Proteomics 2010, 73, 537–551. DOI: 10.1016/j.jprot.2009.09.011.

[98] Armengaud, J. Defining Diagnostic Biomarkers Using ShotgunProteomics and MALDI-TOF Mass Spectrometry. In:Diagnostic Bacteriology: Methods and Protocols, Bishop-Lilly,K. A., Ed., Springer: New York, 2017, pp 107–120. DOI: 10.1007/978-1-4939-7037-7.

[99] Knochenmuss, R. MALDI Ionization Mechanisms: AnOverview. In: Electrospray and MALDI Mass SpectrometryFundamentals, Instrumentation, Practicalities, and BiologicalApplications, Cole, R. B., Ed., John Wiley & Sons, Inc:Hoboken, NJ, 2010, pp 147–183.

[100] Erra-Balsells, R. MALDI: A Very Useful UV Light-InducedProcess…That Still Remains Quite Obscure. In: Fundamentalsof Mass Spectrometry, Hiraoka, K., Ed., Springer: New York,2013, pp 173–197. DOI: 10.1007/978-1-4614-7233-9.

[101] Aebersold, R.; Mann, M. Mass Spectrometry-BasedProteomics. Nature 2003, 422, 198–207. DOI: 10.1038/nature01511.

[102] McAvey, K. M.; Guan, B.; Fortier, C. A.; Tarr, M. A.; Cole,R. B. Laser-Induced Oxidation of Cholesterol Observed duringMALDI-TOF Mass Spectrometry. J. Am. Soc. Mass Spectrom.2011, 22, 659–669. DOI: 10.1007/s13361-011-0074-3.

[103] Zhang, Y.; Fonslow, B. R.; Shan, B.; Baek, M.-C.; Yates, J. R.Protein Analysis by Shotgun/Bottom-up Proteomics. Chem.Rev. 2013, 113, 2343–2394. DOI: 10.1021/cr3003533.

[104] Thiede, B.; H€ohenwarter, W.; Krah, A.; Mattow, J.; Schmid,M.; Schmidt, F.; Jungblut, P. R. Peptide Mass Fingerprinting.Methods 2005, 35, 237–247. DOI: 10.1016/j.ymeth.2004.08.015.

[105] Toby, T. K.; Fornelli, L.; Kelleher, N. L. Progress in Top-Down Proteomics and the Analysis of Proteoforms. Annu.Rev. Anal. Chem. (Palo Alto Calif.) 2016, 9, 499–519. DOI: 10.1146/annurev-anchem-071015-041550.

[106] Chen, B.; Brown, K. A.; Lin, Z.; Ge, Y. Top-Down Proteomics:Ready for Prime Time? Anal. Chem. 2018, 90, 110–127. DOI:10.1021/acs.analchem.7b04747.

[107] Bain, B. J.; Estcourt, L. FAB Classification of Leukemia,Brenner’s Encyclopedia of Genetics, 2nd ed., Elsevier Inc.: SanDiego, CA, 2013, pp 5–7.

[108] Canaani, J.; Beohou, E.; Labopin, M.; Soci�e, G.; Huynh, A.;Volin, L.; Cornelissen, J.; Milpied, N.; Gedde-Dahl, T.;Deconinck, E.; et al. Impact of FAB Classification onPredicting Outcome in Acute Myeloid Leukemia, NotOtherwise Specified, Patients Undergoing Allogeneic Stem CellTransplantation in CR1: An Analysis of 1690 Patients fromthe Acute Leukemia Working Party of EBMT. Am. J. Hematol.2017, 92, 344–350. DOI: 10.1002/ajh.24640.

[109] Angelescu, S.; Berbec, N. M.; Colita, A.; Barbu, D.; Lupu, A. R.Value of Multifaced Approach Diagnosis and Classification ofAcute Leukemias. Maedica (Bucur). 2012, 7, 254–260.

[110] Aslam, B.; Basit, M.; Nisar, M. A.; Khurshid, M.; Rasool,M. H. Proteomics: Technologies and Their Applications. J.Chromatogr. Sci. 2017, 55, 182–196. DOI: 10.1093/chromsci/bmw167.

[111] Tu, C.; Rudnick, P. A.; Martinez, M. Y.; Cheek, K. L.; Stein,S. E.; Slebos, R. J. C.; Liebler, D. C. Depletion of AbundantPlasma Proteins and Limitations of Plasma Proteomics. J.Proteome Res. 2010, 9, 4982–4991. DOI: 10.1021/pr100646w.

[112] Polaskova, V.; Kapur, A.; Khan, A.; Molloy, M. P.; Baker,M. S. High-Abundance Protein Depletion: Comparison ofMethods for Human Plasma Biomarker Discovery.Electrophoresis 2010, 31, 471–482. DOI: 10.1002/elps.200900286.

[113] Liu, Z.; Fan, S.; Liu, H.; Yu, J.; Qiao, R.; Zhou, M.; Yang, Y.;Zhou, J.; Xie, P. Enhanced Detection of Low-Abundance Human Plasma Proteins by IntegratingPolyethylene Glycol Fractionation and ImmunoaffinityDepletion. PLoS One 2016, 11, e0166306. DOI: 10.1371/jour-nal.pone.0166306.

[114] Millioni, R.; Tolin, S.; Puricelli, L.; Sbrignadello, S.; Fadini,G. P.; Tessari, P.; Arrigoni, G. High Abundance ProteinsDepletion vs Low Abundance Proteins Enrichment:Comparison of Methods to Reduce the Plasma ProteomeComplexity. PLoS One 2011, 6, e19603. DOI: 10.1371/journal.pone.0019603.

[115] Aliya, S.; Reddanna, P.; Thyagaraju, K. Does Glutathione S-Transferase Pi (GST-Pi) a Marker Protein for Cancer? Mol.Cell. Biochem. 2003, 253, 319–327. DOI: 10.1023/a:1026036521852.

[116] Ricci, G.; Caccuri, A. M.; Lo Bello, M.; Parker, M. W.;Nuccetelli, M.; Turella, P.; Stella, L.; Di Iorio, E. E.; Federici,G. Glutathione Transferase P1-1: Self-Preservation of an Anti-Cancer Enzyme. Biochem. J. 2003, 376, 71–76. DOI: 10.1042/BJ20030860.

[117] Ranganathan, P. N.; Whalen, R.; Boyer, T. D. Characterizationof the Molecular Forms of Glutathione S-Transferase P1 inHuman Gastric Cancer Cells (Kato III) and in Normal HumanErythrocytes. Biochem. J. 2005, 386, 525–533. DOI: 10.1042/BJ20041419.

[118] Townsend, D. M.; Manevich, Y.; He, L.; Hutchens, S.; Pazoles,C. J.; Tew, K. D. Novel Role for Glutathione S-Transferase p:Regulator of Protein s-Glutathionylation Following Oxidativeand Nitrosative Stress. J. Biol. Chem. 2009, 284, 436–445. DOI:10.1074/jbc.M805586200.

[119] Zhang, X.; Li, T.; Zhang, J.; Li, D.; Guo, Y.; Qin, G.; Zhu,K. Y.; Ma, E.; Zhang, J. Structural and Catalytic Role of TwoConserved Tyrosines in Delta-Class Glutathione S-Transferasefrom Locusta Migratoria. Arch. Insect. Biochem. Physiol. 2012,80, 77–91. DOI: 10.1002/arch.21025.

[120] Axarli, I.; Muleta, A. W.; Chronopoulou, E. G.; Papageorgiou,A. C.; Labrou, N. E. Directed Evolution of GlutathioneTransferases towards a Selective Glutathione-Binding Site andImproved Oxidative Stability. Biochim. Biophys. Acta. Gen.Subj. 2017, 1861, 3416–3428. DOI: 10.1016/j.bbagen.2016.09.004.

[121] Wang, T.; Arifoglu, P.; Ronai, Z.; Tew, K. D. Glutathione S-Transferase P1-1 (GSTP1-1) Inhibits c-Jun N-Terminal Kinase(JNK1) Signaling through Interaction with the C Terminus. J.Biol. Chem. 2001, 276, 20999–21003. DOI: 10.1074/jbc.M101355200.

[122] Kim, J. Y.; Song, H. J.; Lim, H. J.; Shin, M. G.; Kim, J. S.;Kim, H. J.; Kim, B. Y.; Lee, S. W. Platelet Factor-4 is anIndicator of Blood Count Recovery in Acute MyeloidLeukemia Patients in Complete Remission. Mol. CellProteomics. 2008, 7, 431–441. DOI: 10.1074/mcp.M700194-MCP200.

[123] Nguyen, H.-Q.; Lee, D.; Kim, Y.; Paek, M.; Kim, M.; Jang, K.-S.; Oh, J.; Lee, Y.-S.; Yeon, J. E.; Lubman, D. M.; Kim, J.Platelet Factor 4 as a Novel Exosome Marker in MALDI-MS Analysis of Exosomes from Human Serum. Anal.Chem. 2019, 91, 13297–13305. DOI: 10.1021/acs.analchem.9b04198.

[124] Tsimokha, A. S.; Artamonova, T. O.; Diakonov, E. E.;Khodorkovskii, M. A.; Tomilin, A. N. Post-TranslationalModifications of Extracellular Proteasome. Molecules 2020, 25,3504. DOI: 10.3390/molecules25153504.

[125] Kirana, C.; Peng, L.; Miller, R.; Keating, J. P.; Glenn, C.; Shi,H.; Jordan, T. W.; Maddern, G. J.; Stubbs, R. S. Combinationof Laser Microdissection, 2D-DIGE and MALDI-TOF MS toIdentify Protein Biomarkers to Predict Colorectal CancerSpread. Clin. Proteomics 2019, 16, 3. DOI: 10.1186/s12014-019-9223-7.


https://doi.org/10.2174/1381612054546932

https://doi.org/10.2174/1381612054546932



https://doi.org/10.1007/978-1-4939-7037-7

https://doi.org/10.1007/978-1-4939-7037-7

https://doi.org/10.1007/978-1-4614-7233-9

https://doi.org/10.1038/nature01511

https://doi.org/10.1038/nature01511

https://doi.org/10.1007/s13361-011-0074-3

https://doi.org/10.1021/cr3003533

https://doi.org/10.1016/j.ymeth.2004.08.015

https://doi.org/10.1146/annurev-anchem-071015-041550

https://doi.org/10.1146/annurev-anchem-071015-041550


https://doi.org/10.1002/ajh.24640

https://doi.org/10.1093/chromsci/bmw167

https://doi.org/10.1093/chromsci/bmw167

https://doi.org/10.1021/pr100646w

https://doi.org/10.1002/elps.200900286

https://doi.org/10.1002/elps.200900286

https://doi.org/10.1371/journal.pone.0166306




https://doi.org/10.1023/a:1026036521852

https://doi.org/10.1023/a:1026036521852

https://doi.org/10.1042/BJ20030860

https://doi.org/10.1042/BJ20030860

https://doi.org/10.1042/BJ20041419

https://doi.org/10.1042/BJ20041419

https://doi.org/10.1074/jbc.M805586200

https://doi.org/10.1002/arch.21025

https://doi.org/10.1016/j.bbagen.2016.09.004

https://doi.org/10.1016/j.bbagen.2016.09.004



https://doi.org/10.1074/mcp.M700194-MCP200

https://doi.org/10.1074/mcp.M700194-MCP200



https://doi.org/10.3390/molecules25153504

https://doi.org/10.1186/s12014-019-9223-7

https://doi.org/10.1186/s12014-019-9223-7

[126] Goyal, M. M.; Basak, A. Human Catalase: Looking forComplete Identity. Protein Cell 2010, 1, 888–897. DOI: 10.1007/s13238-010-0113-z.

[127] Lu, L.; Fu, N.; Luo, X.; Li, X.; Li, X.-P. Overexpression of Cofilin1 in Prostate Cancer and the Corresponding Clinical Implications.Oncol. Lett. 2015, 9, 2757–2761. DOI: 10.3892/ol.2015.3133.

[128] Morse, M. A. The Role of Glutathione S-Transferase P1-1 inColorectal Cancer: Friend or Foe? Gastroenterology 2001, 121,1010–1013. DOI: 10.1053/gast.2001.28571.

[129] Maga, G.; H€ubscher, U. Proliferating Cell Nuclear Antigen(PCNA): A Dancer with Many Partners. J. Cell Sci. 2003, 116,3051–3060. DOI: 10.1242/jcs.00653.

[130] Song, Y.; Luo, Q.; Long, H.; Hu, Z.; Que, T.;Zhang, X. A.; Li, Z.; Wang, G.; Yi, L.; Liu, Z.; et al.Alpha-Enolase as a Potential Cancer Prognostic MarkerPromotes Cell Growth, Migration, and Invasion inGlioma. Mol. Cancer. 2014, 13, 65. DOI: 10.1186/1476-4598-13-65.


https://doi.org/10.1007/s13238-010-0113-z

https://doi.org/10.1007/s13238-010-0113-z

https://doi.org/10.3892/ol.2015.3133

https://doi.org/10.1053/gast.2001.28571

https://doi.org/10.1242/jcs.00653

https://doi.org/10.1186/1476-4598-13-65

https://doi.org/10.1186/1476-4598-13-65

Review MALDI-TOF in Leukemia’s Proteomics Studies

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