Research ArticleSensitive Biomarker Analysis of Xue-Fu-Zhu-Yu Capsule forPatients with Qi Stagnation and Blood Stasis Pattern: A NestedCase-Control Study

Guang Chen ,1,2 Haoqiang He,1,2 Kun Hu ,1 Jialiang Gao ,1 Jun Li ,1 Mei Han ,3

and Jie Wang 1

1Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China2Graduate School, Beijing University of Chinese Medicine, Beijing 100029, China3Center for Evidence-based Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China

Correspondence should be addressed to Jie Wang; wangjie0103@126.com

Received 4 May 2019; Revised 21 September 2019; Accepted 26 September 2019; Published 18 November 2019

Academic Editor: Arthur De Sa Ferreira

Copyright © 2019 Guang Chen et al. .is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objectives. To identify the sensitive biomarker to predict the effectiveness of Xue-Fu-Zhu-Yu capsules (XFZYC). Methods. .isnested case-control study included 5 patients with response to XFZYC in the treatment group, 5 patients with no response toXFZYC also in the treatment group, and 5 patients in the control group treated with placebo who participated in the previousRCT. .e mRNAs, miRNAs, lncRNAs, and circRNAs were sequenced by next-generation sequencing and differential-expressed(DE) RNAs were identified if p value≤ 0.05 and fold change ≥2, bioinformatics analysis was conducted in terms of functionannotations and signaling pathways, and then sensitive biomarker was analyzed based on real-time PCR. Results..e distributionsof clinical characteristics between the selected participants from treatment group and placebo group were well balanced. A total of1156 DE RNAs, 388 miRNAs, 1954 lncRNAs, and 560 circRNAs were identified, which was associated the mechanism of XFZYCand composed the targeted potential biomarkers for further real-time PCR. .e DE RNAs were enriched in KEGG pathwayspertaining to pathogenesis of Qi Stagnation and Blood Stasis- (QS & BS-) & associated diseases such as coronary heart disease anddigestive diseases. .e expression level of FZD8 was significantly higher in response patients than that in nonresponse patients(p � 0.041) and circRNA_13799 significantly lower in response patients than that in nonresponse patients (p � 0.040) based onreal-time PCR. Patients with higher expression level of FZD8 with 75% stratification have significantly higher reduction in thequestionnaire score (p � 0.010), and the area under the curve (AUC) was 0.765 (95%CI = 0.593–0.936; p � 0.014). Conclusions.FZD8 might perform the sensitive biomarker for predicting the effectiveness of XFZYC. However, further prospective cohortstudy was warranted to confirm the exact specificity and sensitivity of this biomarker.

1. Introduction

Within the framework of Traditional Chinese Medicine(TCM) theory, TCM practitioners always prescribe theherbal formula or acupuncture therapy according to thedifferentiation of TCM Zheng [1]. Hence, the diagnosiscriteria of a sort of specific Zheng were often included in theinclusion criteria for a randomized clinical trial (RCT) interms of Research and Development (R&D) of TCM newdrug, in order to target the population with that kind ofZheng. However, in one of our previous clinical trials to

investigate the effects of Xue-Fu-Zhu-Yu capsules (XFZYC),a kind of herbal medicine approved by the China Food andDrug Administration (CFDA), for patients with Qi Stag-nation and Blood Stasis (QS&BS), a sort of typical Zheng,where QS&BS diagnosis criteria has been used for partici-pants inclusion, the data of primary outcome in the XFZYCgroup failed to be within normality and the scatter diagramdemonstrated that certain participants receiving XFZYCshowed response in effectiveness while some participantsindicated nonresponse. .is abnormal distribution directlyaffected the statistical inference of the data and the

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2019, Article ID 7182865, 12 pageshttps://doi.org/10.1155/2019/7182865

interpretation of the results of the clinical trial, which mightlead to the negative conclusion of the trial.

One of the reasons why there existed both response andnonresponse participants in the treatment group was thatthe QS&BS diagnosis criteria currently used in the trial wasnot adequate enough to target the exact population of onlyresponse patients. It has been reported that herbal medicinefeatured multicomponents and corresponding multitargetsto perform the healing function [2], and a network phar-macology study of XFZYC for QS&BS predicted that therewere 6 targets mostly possible for the merged network andtarget fishing [3]. Hence, we hypothesize that some patientswith QS&BS are sensitive to the targets of XFZYC whileothers even with the same Zheng diagnosed by QS&BScriteria are insensitive to those targets, which might be theprofound mechanism of the phenomenon of both responseand nonresponse. Sensitive biomarker is just a sort ofphysiological substance in human body that when present inabnormal amounts in the serum may indicate the presenceof disease or predict to what extent patients could respond tothe therapy [4]. .erefore, to find the sensitive biomarkerhaving the capacity to predict the effectiveness of XFZYC iscrucial to narrow the scope of targeted population to theexact response population in the clinical trial.

.e genetic profile of an individual provides informationon what could theoretically happen in the body, whileprotein expression profiles are representative of the pro-cesses that are active in the body at the time-point of de-termination [5]. Of note, ribonucleic acid (RNA) builds theconnection between deoxyribonucleic acid (DNA) andproteins, which features the interaction of genotype andenvironment. Among the different types of RNAs, mes-senger RNAs (mRNAs) are transcripts of DNA and theinformation they carry is transferred by their translation intoproteins [6]. Long noncoding RNAs (lncRNAs) are a class ofnon-protein-coding RNAs of >200 nucleotides in length,and they are known to have roles in controlling chromatinstructure, transcriptional regulation, and posttranscriptionalprocessing [7, 8]. Furthermore, circular RNAs (circRNAs),which have drawn an increasing amount of attention re-cently, are a type of noncanonical form of alternative splicingand are more stable than linear RNAs [9, 10]. .erefore, theobjective of this study was to identify the sensitive biomarkerto predict the effectiveness of XFZYC by nested case-controldesign through transcriptome next-generation sequencingand real-time PCR in terms of RNAs.

2. Materials and Methods

2.1. Patients and Samples for RNA Sequencing. .e protocolof the present study was approved by the Ethics Committeeof Guang’anmen Hospital (Beijing, China). In our previousRCT, eligible participants fulfilled the diagnostic criteria forQS&BS pattern whose specificity and sensitivity of diagnosisare 81.91% and 80.35%, respectively [11]; the interventiongroup was treated with XFZYC, and the control group wasplacebo with 6 capsules of XFZYC or placebo 2 times dailyand the duration was 7 weeks; the primary outcome was thechange in the patient-reported QS&BS pattern questionnaire

score from baseline to the end of the 7-week intervention..is questionnaire is a well-validated, multidimensionalmeasurement of overall severity of QS&BS pattern [12]. .eprotocol of this previous trial was registered at the Clin-icalTrials.gov (ID: NCT03091634).

.is nested case-control study was designed and con-ducted based on the previous RCT. .is study included 5patients with response to XFZYC in the treatment group(response group), 5 patients with no response to XFZYC alsoin the treatment group (nonresponse group), and 5 patientsin the control group treated with placebo (placebo group)who presented at Guang’anmen Hospital affiliated to theChina Academy of Chinese Medical Sciences (Beijing,China) between June 2017 to August 2018 and participatedin our previous RCT. Response was identified with reductionin this questionnaire score of >30%, while nonresponse with<30%. Blood sample was collected in 5 participants withresponse to XFZYC before the treatment (group Case1_-before) and after the treatment (group Case1_after), 5participants with no response to XFZYC before the treat-ment (group Case2_before), and 5 participants treated withplacebo before the treatment (group Control_before) andafter the treatment (group Control_after). From each of thepatients, blood samples (3-4ml) were collected in EDTA-containing tubes. Informed consent was provided by all ofthe participants prior to the study.

2.2. RNA Isolation and Library Preparation. RNA degra-dation and contamination were monitored on 1% agarosegels. RNA purity was confirmed using the AgencourtAMPure XP (cat. no. A63881; Beckman Coulter, Brea, CA,USA). RNA integrity was evaluated using the Agilent 2100bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA,USA). .e sample with RNA integrity number ≥7 wassubjected to the subsequent analysis. .e library was con-structed using TruSeq-stranded total RNA with Ribo-ZeroGold (cat. no. 15021048; Illumina, Inc., San Diego, CA, USA)according to the manufacturer’s instructions.

2.3. Sequencing of RNAs and DE RNA Analysis. .e librarywas sequenced on the Illumina sequencing platformHiSeqTM 2500, and 150 bp/125 bp paired-end reads weregenerated. Trimmomatic software (version 0.36) [13] wasused to further filter the quality of the raw data generatedfrom the sequencing to get the high-quality clean reads, thenHierarchical Indexing for Spliced Alignment of Transcripts(version 0.1.6-beta) [14] was used to align clean reads to thereference of human beings, Stringtie software (version 1.3.5)[15] was used to assemble the reads so that the transcript wasspliced. .e spliced transcription products of each samplewere combined and screened as lncRNAs with Cuffmergeprogram in the Cufflinks software package (version 2.2.1).All transcripts that overlapped with known mRNAs, othernoncoding RNA, and non-lncRNA were discarded. Next,the transcripts longer than 200 bp and >2 exons were ob-tained, and CPC (version 0.9-r2) [16], PLEK (version 1.2)[17], CNCI (version 1.0) [18], and Pfam (version 30) [19]software packages were used to predict transcripts with

2 Evidence-Based Complementary and Alternative Medicine

coding potential. .e characteristics (including length, type,and number of exons) of lncRNA were analyzed afterscreening by the Cuffcompare program [20] in the Cufflinks2.2.1 software package. CircRNAs were identified using CIRI[21] and compared with the circBase [22] and with thedatabase of CircAtlas 2.0 with species-focused integratedresource of Homo sapiens [23]. .e estimate size factorsfunction in R package DESeq [24] was used to normalize thecounts, and nbinom test in the package was used to calculatethe p value as well as fold change for the difference com-parison. .e difference comparison was conducted betweengroup Case1_before and Case1_after, between Con-trol_before and Control_after, and between Case1_beforeand Case2_before, to get 3 sets of differential-expressed (DE)RNAs for further analysis of sensitive biomarker. DE RNAswere identified if p value≤ 0.05 and fold change ≥2.

2.4. GO Annotations and KEGG Pathway Analysis. Geneontology (GO) annotations and Kyoto Encyclopedia ofGenes and Genomes (KEGG) pathway analysis were per-formed to investigate the possible roles of the DE RNAs. GOannotations were performed to identify regulatory networksof DE genes (http://geneontology.org). KEGG analysis wasalso performed to explore the enriched pathways of the DERNAs based on the KEGG database (http://www.genome.jp/kegg/). To reveal the role and interactions among the DERNAs, an RNA regulatory network was constructed usingCytoscape software version 3.2.1 (http://www.cytoscape.org/)based on the prediction and interaction analysis by miRWalk2.0 (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/) and starBase 3.0 (http://starbase.sysu.edu.cn/).

2.5. Real-Time PCR and Sensitivity Analysis. A series ofclinical data and blood sample were collected from 30participants with both response and nonresponse to XFZYCin treatment group before intervention. .e expressionlevels of the core RNAs in the regulatory network wereconfirmed by quantitative real-time PCR. Sensitive bio-marker analysis was conducted to identify the RNA thatcould predict the effectiveness of XFZYC among patientsdiagnosed with QS&BS and to further validate the associ-ation between DE RNAs and response degree to XFZYC.

2.6. Statistical Analysis. .e data regarding the clinicalcharacteristics of the participants are expressed as themean± standard error of the mean, and the correspondingresults were statistically analyzed using one-way analysis ofvariance. .e results of DE RNAs in the comparison ofCase1_before vs Case1_after, Control_before vs Control_-after, and Case1_before vs Case2_before were analyzed bythe algorithm of DESeq2 in R software [24], and overlap DERNAs between these three comparisons were identified byVenn’s diagram [25]. 2− ΔCt data generated from real-timePCR were used to compare the confirmed DE RNAs [26]..e multiple regression analysis was used to determine theindependent predictive factors for response of XFZYC.Receiver operating characteristics (ROC) curve was used to

analyze the predictive value of the biomarker. All statisticalanalyses were performed using R software, and p< 0.05 wasconsidered to indicate a statistically significant difference.

3. Results

3.1. Clinical Characteristics of Participants. .e distributionsof clinical characteristics between the selected participantsfrom treatment group and placebo group are listed in Ta-ble 1. .e 2 groups were matched in terms of sex, age, race,Body-mass index, and severity of the pattern, and there wereno significant differences in terms of self-reported coexistingillness and routine medication between the groups(p> 0.05).

3.2. DE RNAs and 6eir Overlap among Groups. .e resultsof the DE RNAs in the comparisons were as follows: a total of58 DE mRNAs (46 upregulated and 12 downregulated), 17DE miRNAs (8 upregulated and 9 downregulated), 691 DElncRNAs (317 upregulated and 374 downregulated), and 161DE circRNAs (95 upregulated and 66 downregulated) wereidentified with fold change >2 and p value <0.05 in com-parison between Case1_before and Case1_after, respectively.In comparison between Control_before and Control_after, atotal of 71 DE mRNAs (37 upregulated and 34 down-regulated), 16 DE miRNAs (9 upregulated and 7 down-regulated), 616 DE lncRNAs (259 upregulated and 357downregulated), and 34 DE circRNAs (14 upregulated and20 downregulated) were identified with fold change >2 and p

value <0.05, respectively. After removing the overlapped DERNA between Case1_before vs Case1_after and Con-trol_before vs Control_after, the unique DE RNA inCase1_before vs Case1_after included 1156 DE RNAs, 388miRNAs, 1954 lncRNAs, and 560 circRNAs, which wereassociated the mechanism of XFZYC and composed thetargeted potential biomarkers for further real-time PCR. Incomparison between Case1_before and Case2_before, a totalof 107 DE mRNAs (39 upregulated and 68 downregulated),45 DE miRNAs (12 upregulated and 33 downregulated), 721DE lncRNAs (383 upregulated and 338 downregulated), and242 DE circRNAs (148 upregulated and 94 downregulated)were identified with fold change >2 and p value< 0.05,which also formed the potential biomarkers for further real-time PCR..e top 10 upregulated and top 10 downregulatedDE mRNAs, miRNAs, lncRNAs, and circRNAs are listed inSupplementary Tables 1, 2, 3, and 4, respectively, and theclustering maps as well as volcano plot was displayed inFigures 1–3 in terms of Case1_before vs Case1_after,Control_before vs Control_after, and Case1_before vsCase2_before, respectively.

3.3. Functional Prediction and Merged Interactions of RNAs.To further determine the possible functions of the DE RNAsand to investigate the association between these functionsand underlying mechanisms in the biological networks,significant GO terms in the categories of biological process,cellular component, and molecular function are shownFigures 4–7 in terms of mRNA, miRNA, lncRNA, and

Evidence-Based Complementary and Alternative Medicine 3

Table 1: Baseline characteristics of the study participants∗.

Variable Response group (n� 5) Nonresponse group (n� 5) Placebo group (n� 5)Sex, nMale 3 4 3

Age, yrMean age (SD) 49.80± 6.68 43.80± 18.32 42.60± 13.22

Race, nHan 5 5 5

Body-mass index‡ 21.39± 3.79 22.90± 0.56 24.34± 4.10Severity of QS&BS pattern§ 34.40± 10.11 31.60± 6.03 34.00± 5.34Self-reported coexisting illness, n (%)Atherosclerosis 1 0 2Digestive system disease 3 1 0Reproductive system disease 3 3 1Osteoarthritis 1 2 1Hypertension 0 1 1Diabetes 0 0 0

Routine medications, n (%)Antiplatelet drugs 0 0 1Antihypertensive drug 0 1 1Statins 0 0 0Oral hypoglycemic drugs 0 0 0

XFZYC�Xue-Fu-Zhu-Yu Capsules; QS&BS�Qi Stagnation and Blood Stasis. ∗Plus-minus values are mean± SD unless otherwise noted. ‡Bodymass index isthe weight in kilograms divided by the square of the height inmeters. §Severity of QS&BS pattern is assessed by the scores of the items in diagnostic criteria forQS&BS pattern. Scores range from 0 to 51, with higher scores indicating more severe pattern of diagnosis.

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Figure 1: Clustering map and volcano plot of DE RNAs for comparison of Case1_before and Case1_after. (a, c, e, g) Heat maps present thehierarchical clustering of DE RNAs with upregulated RNAsmarked in red and downregulated ones marked in blue. (b, d, f, h) Volcano plotsindicate up- and downregulated RNAs with |log2FC|> 1 and p< 0.05, where upregulated RNA was marked in red and downregulated RNAwas marked in green. From left to right, mRNA, miRNA, lncRNA, and circRNA are shown in sequence.

4 Evidence-Based Complementary and Alternative Medicine

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Figure 2: Clustering map and volcano plot of DE RNAs for comparison of Control_before and Control_after. (a, c, e, g) Heat maps presentthe hierarchical clustering of DE RNAs with upregulated RNA marked in red and downregulated RNA marked in blue. (b, d, f, h) volcanoplots indicate up- and downregulated RNAs with |log2FC|> 1 and p value< 0.05, where upregulated RNA was marked in red anddownregulated was marked in green. From left to right, mRNA, miRNA, lncRNA, and circRNA are shown in sequence.

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Figure 3: Clusteringmap and volcano plot of DE RNAs for comparison of Case1_before and Case2_before. (a, c, e, g) Heat maps present thehierarchical clustering of DE RNAs with upregulated RNAmarked in red and downregulated RNAmarked in blue. (b, d, f, h) Volcano plotsindicate up- and downregulated RNAs with |log2FC|> 1 and p< 0.05, where upregulated RNA was marked in red and downregulated RNAwas marked in green. From left to right, mRNA, miRNA, lncRNA, and circRNA are shown in sequence.

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Figure 4: GO analysis of DE mRNAs. ¡e GO enrichment analysis of DE RNAs is represented by directed acyclic graph in (a), (b),and (c), and the GO terms and number are shown by histogram in (d).

6 Evidence-Based Complementary and Alternative Medicine

circRNA, respectively, and enrichment analysis based onKEGG database is shown in Figure 8(a) in terms of mRNA,miRNA, lncRNA, and circRNA, respectively. .e DE RNAswere enriched in KEGG pathways such as glyoxylate anddicarboxylate metabolism, glycine, serine and threoninemetabolism, neomycin, kanamycin and gentamicin bio-synthesis, lipoic acid metabolism, steroid biosynthesis, andone carbon pool by folate. .e interaction analysis of DERNAs is shown in Figure 8(b) in terms of the mergednetwork of lncRNA-miRNA-mRNA and in Figure 8(c) interms of the network of circRNA-miRNA-mRNA.

3.4. Real-Time PCR and Sensitive Biomarker Prediction.To validate the association between DE RNAs and responseto XFZYC, a set of 30 patients with both response andnonresponse was further analyzed by using quantitative real-time PCR. Consistently, we found the expression level of

FZD8 was significantly higher in response patients than thatin nonresponse patients (p � 0.041) and circRNA_13799significantly lower in response patients than that in non-response patients (p � 0.040) as is shown in Table 2, con-firming that the higher expression of FZD8 and lowerexpression of circRNA_13799 are associated with a betterresponse to XFZYC. Furthermore, analyzing the reductionin questionnaire score of these 30 patients after 50%, 75%,and 90% stratification by the level of FZD8 andcircRNA_13799 revealed that only patients with higherexpression level of FZD8 with 75% stratification have sig-nificantly higher reduction in questionnaire score (p �

0.010). Nevertheless, multiple regression analysis showedthat only the level of FZD8 could be an independent pre-dictive factor for response to XFZYC (p � 0.0003). Toevaluate this predictive value, the ROC curve was used toanalyze the sensitivity and specificity. .e area under thecurve (AUC) was 0.765 (95%CI� 0.593–0.936; p � 0.014).

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Figure 5: GO analysis of DE miRNAs. .e GO enrichment analysis of DE RNAs is represented by directed acyclic graph in (a), (b),and (c), and the GO terms and number are shown by histogram in (d).

Evidence-Based Complementary and Alternative Medicine 7

.ese findings suggested that increased expression of FZD8may be responsible for high sensitivity to XFZYC andpredicting the response in QS&BS patients.

4. Discussion

In the present study, expression profiles of mRNA, miRNA,lncRNA, and circRNA were obtained by next-generationsequencing and compared in before-treatment and after-treatment of participants selected in the randomized clinicaltrials and also compared in response and nonresponseparticipants within the nested case-control design, and thensensitive biomarker for predicting the effectiveness ofXFZYC was analyzed based on the functional prediction andmerged interactions of the identified DE RNAs. Patientswith higher expression level of FZD8 with 75% stratificationhave significantly higher reduction in questionnaire score,indicating a better response.

XFZYC is a common Chinese patent medicine approvedby CFDA for treating QS &BS pattern. However, there wereboth response and nonresponse participants in the treat-ment group in our previous randomized clinical trial, whichindicated that the diagnosis criteria for QS&BS pattern in theclinical trial should be further narrowed, that is, the in-dications of the population for XFZYC should be clarifiedand specified so that the population with response could betargeted in the clinical trial and in the clinical practice.Adding the objective sensitive biomarker of FZD8 with 75%stratification to the diagnosis criteria composed of onlysymptoms and signs we currently used might specificallynarrow the scope of targeted population and then improvethe effectiveness of XFZYC.

.e function annotation of the DE RNAs indicated thatthe sensitive biomarker FZD8 is a member of the frizzledgene family and a sort of receptor for Wnt proteins, and thecomponent of the Wnt-Fzd-LRP5-LRP6 complex could

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8 Evidence-Based Complementary and Alternative Medicine

trigger beta-catenin signaling through inducing aggregationof receptor-ligand complexes into ribosome-sized signal-osomes [27]. .is signaling pathway could lead to the ac-tivation of disheveled proteins, inhibition of GSK-3 kinase,nuclear accumulation of beta-catenin, and activation of Wnttarget genes. Moreover, FZD8 also gets involved in PKC andcalcium fluxes pathway. Both these two pathways seem toinvolve interactions with G-proteins and to participate in theprocess of transduction and intercellular transmissionduring tissue morphogenesis and pathology of inflammation[28]. Bioinformatics analysis of the RNA sequencing resultsof the present study indicated that DE RNAs were mostsignificantly enriched in pathways including Wnt signalingpathway, glyoxylate and dicarboxylate metabolism, glycine,serine and threonine metabolism, and homologous re-combination. .ese results imply that the pharmacologicalfunctions of XFZYC is associated with both pathogenesis ofQS&BS associated diseases such as coronary heart disease,

digestive diseases, osteoporosis, and type 2 diabetes [29] andvital metabolism process [30]. In the lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA networks, the coreregulator hsa-miR-137 is closely associated with the nervesystem [31], and hsa-miR-451a performs the function in theprocess of myocardial lesions [32].

To the best of our knowledge, the present study was thefirst to explore the sensitive biomarker for effectiveness ofXFZYC and it was attempted to interpret the phenomenonof existing of both response and nonresponse patients, fromthe perspective of RNAs, which was determined by next-generation sequencing and real-time PCR within the designof nested case control. It is worth mentioning that thissensitive biomarker could be used in future clinical trial interms of inclusion criteria and that the design of nested case-control in this study might be implemented to discoverbiomarker of a sort of Chinese herbal formulae in furtherstudies. However, one of the limitations of the present study

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Evidence-Based Complementary and Alternative Medicine 9

Organismal systems−−agingOrganismal systems−−circulatory system

Organismal systems−−developmentOrganismal systems−−digestive system

Organismal systems−−endocrine systemOrganismal systems−−environmental adaptation

Organismal systems−−excretory systemOrganismal systems−−immune systemOrganismal systems−−nervous systemOrganismal systems−−sensory systemMetabolism−−amino acid metabolism

Metabolism−−biosynthesis of other secondary metabolitesMetabolism−−carbohydrate metabolism

Metabolism−−energy metabolismMetabolism−−glycan biosynthesis and metabolism

Metabolism−−lipid metabolismMetabolism−−metabolism of cofactors and vitamins

Metabolism−−metabolism of other amino acidsMetabolism−−metabolism of terpenoids and polyketides

Metabolism−−nucleotide metabolismMetabolism−−xenobiotics biodegradation and metabolism

Human diseases−−cancersHuman diseases−−cardiovascular diseases

Human diseases−−drug resistanceHuman diseases−−endocrine and metabolic diseases

Human diseases−−immune diseasesHuman diseases−−infectious diseases

Human diseases−−neurodegenerative diseasesHuman diseases−−substance dependence

Genetic information processing−−folding, sorting and degradationGenetic information processing−−replication and repair

Genetic information processing−−transcriptionGenetic information processing−−translation

Environmental information processing−−membrane transportEnvironmental information processing−−signal transduction

Environmental information processing−−signaling molecules and interactionCellular processes−−cell growth and death

Cellular processes−−cell motilityCellular processes−−cellular community − eukaryotes

Cellular processes−−transport and catabolism

172537

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Organismal systems−−agingOrganismal systems−−circulatory system

Organismal systems−−developmentOrganismal systems−−digestive system

Organismal systems−−endocrine systemOrganismal systems−−environmental adaptation

Organismal systems−−excretory systemOrganismal systems−−immune systemOrganismal systems−−nervous systemOrganismal systems−−sensory systemMetabolism−−amino acid metabolism

Metabolism−−biosynthesis of other secondary metabolitesMetabolism−−carbohydrate metabolism

Metabolism−−energy metabolismMetabolism−−glycan biosynthesis and metabolism

Metabolism−−lipid metabolismMetabolism−−metabolism of cofactors and vitamins

Metabolism−−metabolism of other amino acidsMetabolism−−metabolism of terpenoids and polyketides

Metabolism−−nucleotide metabolismMetabolism−−xenobiotics biodegradation and metabolism

Human diseases−−cancersHuman diseases−−cardiovascular diseases

Human diseases−−drug resistanceHuman diseases−−endocrine and metabolic diseases

Human diseases−−immune diseasesHuman diseases−−infectious diseases

Human diseases−−neurodegenerative diseasesHuman diseases−−substance dependence

Genetic information processing−−folding, sorting and degradationGenetic information processing−−replication and repair

Genetic information processing−−transcriptionGenetic information processing−−translation

Environmental information processing−−membrane transportEnvironmental information processing−−signal transduction

Environmental information processing−−signaling molecules and interactionCellular processes−−cell growth and death

Cellular processes−−cell motilityCellular processes−−cellular community − eukaryotes

Cellular processes−−transport and catabolism

20100Percent of genes (%)

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TypeAllDeg

TypeAllDeg

TypeAllDeg

TypeAllDeg

Organismal systems−−agingOrganismal systems−−circulatory system

Organismal systems−−developmentOrganismal systems−−digestive system

Organismal systems−−endocrine systemOrganismal systems−−environmental adaptation

Organismal systems−−excretory systemOrganismal systems−−immune systemOrganismal systems−−nervous systemOrganismal systems−−sensory systemMetabolism−−amino acid metabolism

Metabolism−−biosynthesis of other secondary metabolitesMetabolism−−carbohydrate metabolism

Metabolism−−energy metabolismMetabolism−−glycan biosynthesis and metabolism

Metabolism−−lipid metabolismMetabolism−−metabolism of cofactors and vitamins

Metabolism−−metabolism of other amino acidsMetabolism−−metabolism of terpenoids and polyketides

Metabolism−−nucleotide metabolismMetabolism−−xenobiotics biodegradation and metabolism

Human diseases−−cancersHuman diseases−−cardiovascular diseases

Human diseases−−drug resistanceHuman diseases−−endocrine and metabolic diseases

Human diseases−−immune diseasesHuman diseases−−infectious diseases

Human diseases−−neurodegenerative diseasesHuman diseases−−substance dependence

Genetic information processing−−folding, sorting and degradationGenetic information processing−−replication and repair

Genetic information processing−−transcriptionGenetic information processing−−translation

Environmental information processing−−membrane transportEnvironmental information processing−−signal transduction

Environmental information processing−−signaling molecules and interactionCellular processes−−cell growth and death

Cellular processes−−cell motilityCellular processes−−cellular community − eukaryotes

Cellular processes−−transport and catabolism

250546

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Organismal systems−−agingOrganismal systems−−circulatory system

Organismal systems−−developmentOrganismal systems−−digestive system

Organismal systems−−endocrine systemOrganismal systems−−environmental adaptation

Organismal systems−−excretory systemOrganismal systems−−immune systemOrganismal systems−−nervous systemOrganismal systems−−sensory systemMetabolism−−amino acid metabolism

Metabolism−−biosynthesis of other secondary metabolitesMetabolism−−carbohydrate metabolism

Metabolism−−energy metabolismMetabolism−−glycan biosynthesis and metabolism

Metabolism−−lipid metabolismMetabolism−−metabolism of cofactors and vitamins

Metabolism−−metabolism of other amino acidsMetabolism−−metabolism of terpenoids and polyketides

Metabolism−−nucleotide metabolismMetabolism−−xenobiotics biodegradation and metabolism

Human diseases−−cancersHuman diseases−−cardiovascular diseases

Human diseases−−drug resistanceHuman diseases−−endocrine and metabolic diseases

Human diseases−−immune diseasesHuman diseases−−infectious diseases

Human diseases−−neurodegenerative diseasesHuman diseases−−substance dependence

Genetic information processing−−folding, sorting and degradationGenetic information processing−−replication and repair

Genetic information processing−−transcriptionGenetic information processing−−translation

Environmental information processing−−membrane transportEnvironmental information processing−−signal transduction

Environmental information processing−−signaling molecules and interactionCellular processes−−cell growth and death

Cellular processes−−cell motilityCellular processes−−cellular community − eukaryotes

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3020100Percent of genes (%)

20 30100Percent of genes (%)

3020100Percent of genes (%)

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(a)

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Figure 8: Continued.

10 Evidence-Based Complementary and Alternative Medicine

is the lack of validation of the sensitive biomarker in pro-spective cohort study to confirm the specificity and sensitivityof this biomarker. Furthermore, the sensitive biomarker as-sociated with the pharmacological mechanism of XFZYC hasnot been tested in animal or cell study because of lack ofanimal or cell model of QS&BS pattern.

5. Conclusion

FZD8 may act as sensitive biomarker for predicting theeffectiveness of XFZYC. However, further prospective co-hort study was warranted to confirm the exact specificity andsensitivity of this biomarker.

Data Availability

.e data used to support the findings of this study areavailable from the corresponding author upon request.

Ethical Approval

.is nested case-control study was designed and conductedin accordance with the principle of Declaration of Helsinkiwith written informed consent from each participant. .eprotocol was reviewed and approved by the Ethics Com-mittee of Guang’anmen Hospital of China Academy ofChinese Medical Sciences (No. 2017-043-ky-01).

Conflicts of Interest

.e authors declare that there are no conflicts of interestregarding the publication of this article.

Authors’ Contributions

Guang Chen, Haoqiang He, Kun Hu, and Jialiang Gaocontributed equally to this work. Jie Wang was the principal

(c)

Figure 8: (a) Enrichment analysis in KEGG pathways of DE mRNAs, miRNAs, lncRNAs, and circRNAs, respectively. (b) .e lncRNA-miRNA-mRNA interaction network of DE RNAs. (c) .e circRNA-miRNA-mRNA interaction network of DE RNAs. .e enrichedpathways and related gene number are shown in the histogram. .e miRNAs are marked in red, lncRNAs are marked in yellow, circRNAsare marked in green, and mRNAs are marked in blue.

Table 2: Real-time PCR results.

RNA name2− ΔCt (MD± SD)

P valueResponse Nonresponse

OR2L5 0.000217± 0.000279 0.000117± 0.000051 0.163CALN1 0.000110± 0.000153 0.000075± 0.000062 0.445IL3RA 0.000621± 0.000195 0.000678± 0.000204 0.448GALNT13 0.000138± 0.000196 0.000051± 0.000027 0.124FZD8 0.000191± 0.000147 0.000109± 0.000040 0.041SLC25A6 0.001081± 0.000770 0.000845± 0.000337 0.312PRR25 0.000054± 0.000042 0.000039± 0.000025 0.242circRNA_13799 0.000073± 0.000075 0.000031± 0.000020 0.040hsa-miR-4664-3p 0.000444± 0.000203 0.000499± 0.000168 0.438hsa-miR-190a-5p 0.000003± 0.000001 0.000003± 0.000001 0.979hsa-miR-3667-3p 0.000048± 0.000032 0.000049± 0.000018 0.945hsa-miR-1268b 0.007495± 0.002283 0.006612± 0.001168 0.214MD, mean difference; SD, standard deviation.

Evidence-Based Complementary and Alternative Medicine 11

investigator of this project and was responsible for theproject; Guang Chen contributed to the design of the studyand wrote the manuscript; Guang Chen, Haoqiang He, KunHu, and Jialiang Gao performed and conducted the study;Mei Han conducted the statistical design and analysis of thedata. All authors read and approved the final manuscript.

Acknowledgments

.is study was supported by the Program of China Food andDrug Administration (Project no. 201207009).

Supplementary Materials

.e top 10 upregulated and top 10 downregulated DEmRNAs,miRNAs, lncRNAs, and circRNAs with the comparison ofCase1_before vs Case1_after, Control_before vs Control_after,and Case1_before vs Case2_before. (Supplementary Materials)

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