Research ArticleEffect of BRCA1 on the Concurrent ChemoradiotherapyResistance of Cervical Squamous Cell Carcinoma Based onTranscriptome Sequencing Analysis

Xin Wen ,1 Shui Liu ,2 and Manhua Cui 1

1Department of Gynecology and Obstetrics, The Second Hospital of Jilin University, Changchun, Jilin, China2The Second Hospital of Jilin University, Changchun, Jilin, China

Correspondence should be addressed to Manhua Cui; cuimanhua@126.com

Received 22 December 2019; Revised 29 May 2020; Accepted 3 June 2020; Published 24 June 2020

Academic Editor: Nobuo Kanazawa

Copyright © 2020 XinWen et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. Cervical squamous cell carcinoma (CSCC) is the main pathological type of cervical cancer, accounting for 80%–85% ofcervical cancer. Owing to concurrent chemoradiotherapy (CCRT) resistance in a subset of CSCC patients, the treatment response isoften unsatisfactory. Identifying predictors and therapeutic targets related to cisplatin-based CCRT resistance in CSCC is critical.Methods. We reanalyzed GSE56363, an mRNA dataset from the GEO database with 21 patients with locally advanced CSCC, toidentify differentially expressed genes (DEGs) related to CCRT resistance. The hub genes were screened from the protein-protein interaction network of DEGs using cytoHubba plug-in of Cytoscape software. Transcriptome sequencing technologywas used to compare differential expression between SiHa cells overexpressing BRCA1 compared with control SiHa cells.Functional annotation for DEGs and gene set enrichment analysis (GSEA) was performed to identify DEG-enriched relativesignaling pathways to examine the molecular mechanisms of BRCA1 in CCRT resistance of CSCC. qPCR was used to verify theexpression of key genes in SiHa/DDP cells. Results. A total of 609 DEGs including 223 upregulated DEGs and 386downregulated DEGs were identified between the complete response to CCRT (CR) and noncomplete response to CCRT (NCR)CSCC patients based on the GSE56363 dataset. Ten hub genes with the highest degrees were identified via the plug-inCytoHubba in Cytoscape: BRCA1, CDCA8, ASPM, CDC45, RAD51, HMMR, CENPF, EXO1, DTL, and ZWINT genes, andBRCA1 ranked first. Through transcriptome sequencing analysis based on GSE141558, 1344 DEGs were identified in BRCA1-overexpressing SiHa cells, including 824 upregulated DEGs and 520 downregulated DEGs. GSEA results showed that CCRT-resistance related signaling pathways, such as the JAK/STAT signaling pathway and the WNT signaling pathway, weredifferentially enriched in BRCA1-expressing SiHa cells. STAT1, STAT2, and CCND1 were screened as the differentiallyexpressed target genes of BRCA1 and may correlate with resistance of CSCC. qPCR results showed that only STAT1 wassignificantly increased in SiHa cells with GV230-BRCA1 plasmid transfection. Conclusion. BRCA1 overexpression in SiHa cellsmay upregulate STAT1 to activate the JAK/STAT signaling pathway, suggesting a mechanism for enhanced CCRT resistance.

1. Introduction

Cervical cancer (CC) ranks fourth for both incidence andmortality among cancers in women worldwide, and therewere an estimated 570,000 new cases and 311,000 deathsrelated to CC in 2018 [1]. Cervical squamous cell carcinoma(CSCC) is the main pathological type of CC, accounting for80%–85% of all CC cases. Approximately two-thirds ofCSCC patients are diagnosed at medium or advanced stage,and the standard treatment is a concurrent chemoradiother-

apy (CCRT) regimen consisting of cisplatin-based chemo-therapy, external beam radiotherapy, and brachytherapy[2]. However, owing to CCRT resistance in a subset ofpatients, the treatment response is often unsatisfactory.Therefore, identifying efficient predictors and therapeutictargets related to CCRT is critical to improve the prognosisof CSCC patients.

With the rise of bioinformatics and high-throughputsequencing technologies, cancer development mechanisms,drug resistance, and prognosis can be better explained at

HindawiBioMed Research InternationalVolume 2020, Article ID 3598417, 16 pageshttps://doi.org/10.1155/2020/3598417

the genetic level [3, 4]. These findings will contribute tounderstanding individual differences and tumor heterogene-ity in cancer patients. These technologies have been appliedto investigate predictors and therapeutic targets of radiother-apy and chemotherapy resistance in many tumor types [5–7].However, relatively few studies have examined CCRT resis-tance in CSCC [8, 9].

The BRCA1 tumor suppressor responds to DNA damageby participating in DNA repair, mRNA transcription, cellcycle regulation, protein ubiquitination, and other cellularpathways [10, 11]. The DNA repair ability of damaged cellsimproves with increased intracellular BRCA1 expression,resulting in drug resistance. Multiple studies have shown thatthe expression level of BRCA1 gene in cancers is negativelycorrelated with the sensitivity to platinum drugs [12–14]and sensitivity to radiotherapy [15–17].

In the current study, we screened the differentiallyexpressed genes (DEGs) related to CCRT resistance of CSCCand found that BRCA1 overexpression enhanced CCRTresistance in CSCC. Subsequently, we performed tran-scriptome sequencing to explore the potential mechanismsby which BRCA1 may enhance resistance in CSCC. Ourfindings may provide theoretical support and new strategiesfor the diagnosis and treatment of CSCC patients withCCRT resistance.

2. Materials and Methods

2.1. Identification of DEGs Related to CCRT Resistance inCSCC Based on the GEO Dataset. The GSE56363 databaseof CSCC patients was downloaded from the NCBI-GEOdatabase [18]. The CSCC patients were treated by CCRT,and cisplatin-based treatments were used as chemotherapeu-tics. Patients were divided into two groups: completeresponse to CCRT (CR) and noncomplete response to CCRT(NCR). The DEGs between NCR and CR patients were rea-nalyzed using limma package in R [19]. False discovery rateðFDRÞ < 0:05 and ∣LogFoldChangeðFCÞ ∣ >1were consideredas the cut-off criterion.

2.2. Gene Ontology (GO) Enrichment Analysis andConstruction of the Protein-Protein Interaction Network.We performed GO enrichment for the DEGs related toCCRT resistance in CSCC by clusterProfiler package [20].The STRING database (https://string-db.org/) is an onlineresource that provides known direct and indirect protein-protein interaction (PPI). We mapped the DEGs to STRING[21] to reveal the PPI information and visualized the PPI net-work using Cytoscape 3.7.2 [22]. The combined scores ðinteraction scoresÞ > 0:4 were considered as significant. Wethen analyzed the PPI network to screen the hub genes usingthe cytoHubba plug-in of Cytoscape software [23].

We selected the top 10 DEGs with the highest degrees forfurther analysis. The receiver operating characteristic (ROC)curves of the hub genes were generated by the pROC packagein R [24]. The violin plots of the hub gene expression weredeveloped by the ggplot2 package in R [25]. Heat maps ofthe hub gene expression were developed by the pheatmappackage in R [26].

2.3. Cells, Cell Culture, and Plasmid Transfection. The SiHahuman CSCC cell line was purchased from Cell Bank of theChinese Academy of Sciences, and SiHa/DDP cells were pur-chased from Fenghui Bio-Technology Limited. The cellswere cultured in DMEM medium (Gibco, USA) supple-mented with 10% fetal bovine serum (Sangon, China) and1% antibiotic-antimitotic solution (Gibco). The GV230-BRCA1 and GV230 plasmids were purchased from Gene-Chem (Shanghai, China). SiHa cells were transfected usingLipofectamine 3000 reagent (Invitrogen, USA) followingthe manufacturer’s protocol. Transfected cells were incu-bated for 48 h before further analysis.

2.4. Reverse Transcription and Quantitative Real-Time PCR.Total RNA was extracted using TRIzol (Sangon, Shanghai,China) according to the manufacturer’s protocols. The quan-tity and quality of RNA were measured using the NanoDrop2000 spectrophotometer (Thermo Scientific, USA). qRT-PCR was performed using EasyScript® First-Strand cDNASynthesis SuperMix (TransGen Biotech, Beijing, China) and2x SG Fast qPCR Master Mix (Sangon Biotech, China) withthe Roche LightCycler480 PCR system. The 2-ΔΔCT methodwas used to calculate relative expression. The primersequences are listed in Table 1. Experiments were performedin triplicate.

2.5. Transcriptome Sequencing of mRNAs and DifferentialExpression Analysis. Sequencing libraries were producedusing the Hieff NGS™ MaxUp Dual-mode mRNA LibraryPrep Kit for Illumina (YEASEN, Shanghai, China) based onthe instructions from the manufacturer. First, total RNAwas quantified using the Qubit 2.0 RNA Assay Kit (Life,USA) to determine the amount of total RNA added to thelibrary construction. Purification and fragmentation ofmRNA were performed using the mRNA Capture Beadsand Frag/Prime buffer. Synthesis and purification ofdouble-stranded DNA were conducted using 1st Strand Syn-thesis Reaction Buffer, 2nd Strand Synthesis Buffer, and HieffNGS™ DNA Selection Beads before PCR. Amplification and

Table 1: Primer sequences used for qRT-PCR amplification.

Primer Sequences

BRCA15′-GACAGTCAGCCGCATCTTCT-3′5′-ACCAAATCCGTTGACTCCGA-3′

STAT15′-ATCAGGCTCAGTCGGGGAATA-3′5′-TGGTCTCGTGTTCTCTGTTCT-3′

STAT25′-CCAGCTTTACTCGCACAGC-3′

5′-AGCCTTGGAATCATCACTCCC-3′

CCND15′-GCTGCGAAGTGGAAACCATC-3′5′-CCTCCTTCTGCACACATTTGAA-3′

GAPDH5′-GACAGTCAGCCGCATCTTCT-3′5′-ACCAAATCCGTTGACTCCGA-3′

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GSM

1359

938

GSM

1359

939

GSM

1359

940

GSM

1359

941

GSM

1359

942

GSM

1359

943

GSM

1359

944

GSM

1359

945

GSM

1359

946

GSM

1359

926

GSM

1359

927

GSM

1359

928

GSM

1359

929

GSM

1359

930

GSM

1359

931

GSM

1359

932

GSM

1359

933

GSM

1359

934

GSM

1359

935

GSM

1359

936

GSM

1359

937

6

8

10

12

14

GSE56363/GPL4133 selected samples

NCRCR

(a)

0

2

4

6

8

−4 −2 −1 0 1 2 4Log2 (fold change)

−Log

10 P

val

ue

(b)

Figure 1: Processing of microarray data and identification of DEGs. (a) Boxplot of normalized expression values for GSE56363. The dottedline in the middle of each box represents the median of each sample, and its distribution among samples indicates the level of normalization ofthe data, with a straight line revealing a fair normalization level. (b) Volcano plot of DEGs based on GSE56363. The upregulated DEGs inNCR CSCC patients were signed in red, and downregulated DEGs were signed in green.

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purification of the library were performed by PCR methodwith the 2x Super Canace™ High-Fidelity Mix, primer mix,and Hieff NGS™ DNA Selection Beads. PCR products werecollected and assessed using the Qubit2.0 DNA Test Kit(Life). Transcriptome sequencing was performed by SangonBiotech Co. Ltd. (Shanghai, China). DEGs were detected bythe negative binomial distribution test using the DESeq Rpackage [27]. FDR < 0:05 and ∣LogFC ∣ >1 were the cut-offcriterion. Each group sample for transcriptome sequencingcontained three biological replicates.

2.6. euKaryotic Ortholog Groups (KOG) FunctionalAnnotation and Gene Set Enrichment Analysis (GSEA).KOG is the NCBI annotation system based on the directhomologous relationship of genes, in which Clusters ofOrthologous Groups of proteins (COG) are targeted at pro-

karyotes for eukaryotes. KOG currently has 4852 classifica-tions and can be combined with evolutionary relationshipsto classify homologous genes from different species into dif-ferent ortholog clusters.

To explore potential mechanisms underlying the effect ofBRCA1 in CCRT resistance in CSCC, GSEA was performedto detect whether a priori defined set of genes showed statis-tically significant differential expression between the BRCA1overexpression group and negative control group usingBroad Institute software [28]. Gene sets with a nominalP value < 0.05 and FDR < 0:25were considered significantlyenriched.

Transcriptional Regulatory Relationships Unraveled bySentenced-based Textmining Version 2.0 database (TRRUST)(https://www.grnpedia.org/trrust/) is a database based on theliterature to reflect the relationship between transcriptional

Table 2: The significantly enriched analysis of DEGs in NCR CSCC patients.

Category Term Description Adjusted P value Count

BP GO:0009615 Response to virus 2:19E − 17 67

BP GO:0060337 Type I interferon signaling pathway 2:19E − 17 35

BP GO:0071357 Cellular response to type I interferon 2:19E − 17 35

BP GO:0034340 Response to type I interferon 1:36E − 16 35

BP GO:0034341 Response to interferon-gamma 4:28E − 16 50

BP GO:0051607 Defense response to virus 1:64E − 15 53

BP GO:1903900 Regulation of viral life cycle 1:02E − 14 40

BP GO:0071346 Cellular response to interferon-gamma 8:74E − 14 44

BP GO:0060333 Interferon-gamma-mediated signaling pathway 1:01E − 13 31

BP GO:0050792 Regulation of viral process 1:64E − 12 42

CC GO:0071556 Integral component of lumenal side of endoplasmic reticulum membrane 7:23E − 06 12

CC GO:0098553 Lumenal side of endoplasmic reticulum membrane 7:23E − 06 12

CC GO:0005925 Focal adhesion 6:34E − 05 49

CC GO:0005924 Cell-substrate adherens junction 6:34E − 05 49

CC GO:0005788 Endoplasmic reticulum lumen 6:34E − 05 40

CC GO:0030055 Cell-substrate junction 6:90E − 05 49

CC GO:0012507 ER to Golgi transport vesicle membrane 1:29E − 04 15

CC GO:0030176 Integral component of endoplasmic reticulum membrane 2:83E − 04 24

CC GO:0042611 MHC protein complex 3:75E − 04 9

CC GO:0030662 Coated vesicle membrane 3:75E − 04 26

MF GO:0005201 Extracellular matrix structural constituent 8:84E − 08 22

MF GO:0019838 Growth factor binding 9:67E − 07 19

MF GO:0005539 Glycosaminoglycan binding 9:67E − 07 24

MF GO:0005178 Integrin binding 9:67E − 07 18

MF GO:0008201 Heparin binding 4:38E − 05 18

MF GO:0004714 Transmembrane receptor protein tyrosine kinase activity 6:88E − 04 10

MF GO:1901681 Sulfur compound binding 6:88E − 04 20

MF GO:0050839 Cell adhesion molecule binding 1:36E − 03 30

MF GO:0030020 Extracellular matrix structural constituent conferring tensile strength 3:32E − 03 7

MF GO:0019199 Transmembrane receptor protein kinase activity 3:55E − 03 10

4 BioMed Research International

regulations [29]. To screen the mechanisms of BRCA1 inresistance in CSCC, we searched the targets of BRCA1 usingthe TRRUST Version 2 database.

2.7. Statistical Analysis. Statistical analysis was performedusing GraphPad Prism 7.0 (GraphPad Software Inc., USA).FDR controlling was used to correct P values with the Benja-mini Hochberg algorithm implemented in R 3.5.1 suite(Lucent Technologies). FDR < 0:05 and P value < 0.05 wereconsidered statistically significant.

3. Results

3.1. Processing of Microarray Data and Identification of DEGsfrom the GEO Dataset. The GSE56363 dataset was normal-ized and the results are shown in Figure 1(a). A total of 21CSCC patients at advanced stage including 9 NCR and 12CR patients were analyzed. We obtained 609 DEGs including223 upregulated DEGs and 386 downregulated DEGs inNCR patients compared with CR patients (Figure 1(b)).

3.2. GO Enrichment Analysis for DEGs. To obtain the func-tion of the DEGs related to CCRT in CSCC, enrichment anal-ysis of DEGs was performed using clusterProfiler in R. Asshown in Table 2 and Figure 2, in the biological processgroup, the DEGs were most enriched in Response to virus.In the cellular component group, the DEGs were most

enriched in the Integral component of lumenal side of endo-plasmic reticulum membrane. In the molecular functiongroup, the DEGs were most enriched in extracellular matrixstructural constituent.

3.3. Construction of the PPI Network and Screening andAnalysis of Hub Genes. The PPI of the DEGs was collectedby STRING and visualized in Cytoscape software. We evalu-ated the degree and betweenness centrality in the PPI net-work through the CytoHubba plug-in and identified the top10 hub genes with the highest degrees: BRCA1, CDCA8,ASPM, CDC45, RAD51, HMMR, CENPF, EXO1, DTL, andZWINT (Figure 3). Among them, BRCA1 has the highestdegrees. The ROC curves of the hub genes all indicated favor-able diagnostic value in CSCC patients with CCRT resistance(Figure 4).

3.4. Validation of BRCA1 Expression. The qPCR resultsshowed that the expression level of BRCA1 mRNA was sig-nificantly upregulated in SiHa/DDP cells compared withSiHa cells (Figure 5(a)). Furthermore, the expression levelof BRCA1 was significantly increased in SiHa cell lines trans-fected with the GV230-BRCA1 plasmid (OV-BRCA1 group)compared with cells transfected with empty vector (NCgroup) (Figure 5(b)).

BP CC MF

GO

:005

1302

GO

:004

3552

GO

:009

0218

GO

:003

0199

GO

:005

1897

GO

:004

3491

GO

:004

5785

GO

:005

0900

GO

:003

0198

GO

:004

3062

GO

:000

0793

GO

:003

0496

GO

:004

4815

GO

:000

0775

GO

:004

3202

GO

:004

4420

GO

:000

9897

GO

:000

5788

GO

:006

2023

GO

:003

1012

GO

:005

0839

GO

:000

4714

GO

:001

9199

GO

:003

0020

GO

:000

5178

GO

:000

5539

GO

:000

8201

GO

:190

1681

GO

:001

9838

GO

:000

5201

0

3

6

9

–Log

(adj

P v

alue

)

Decreasing Increasing

z-score

The top ten GO terms in each group

Figure 2: GO enrichment analysis of DEGs. The top 10 GO terms in each group were ranked by adjusted P value. The gradual colorrepresents the z-score.

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TIMELESS

TGFB1

UBE2T

TICRR

TK1

SGOL1

SMC4 TEK

SMC2

PECAM1

STIL

SELP

RAD18

RPA3

PRC1

RACGAP1

RAD51

RAD54L

POLQ

OIP5

PDGFRB

NUP107

NRP1

PIK3R3

NUSAP1

PCOLCE

MMP2

PDGFRA

KDR

NEK2

MIS18BP1NCAPG2

LYVE1

NDC1NCAPH

IRF4

ERBB2

EXO1FBN1

ECT2

EME1ENG

EFEMP2

KIAA0101INCENP

HMMRKIF23

HELLS

KIF14KNSTRN

CIT

DTL

CKS1B

DEPDC1

CLSPN

E2F8

E2F7

DIAPH3

CENPF

COL6A3

COL6A2

CENPO

COL5A2

CEP152

CXCL12

DLG4

CD79BCDCA8

CDC45

COL3A1

CBX5CASC5

C3

CD34

BLM

CCL19

BRCA1BRIP1

ATAD5

FANCD2FANCI

FGF7

FGF2FAM64A

H2AFV

TGFB3

WDHD1

ZAP70

ZWILCHZWINTASPM

VWF

UHRF1

IGFBP3IGF2 FMOD

GNG7 FYN

CLDN5

ADAMTS2

(a)

Figure 3: Continued.

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3.5. Identification and GO and KOG Enrichment of DEGs inSiHa Cells with BRCA1 Overexpression Plasmid Transfection.After removing reads with adapters, unknown nucleotides,and low-quality reads of sequencing, we acquired a total of15.79Gb clean data. The Q30 percentage of all samples wasmore than 90% (Table S1). The transcriptome sequencingdata have been uploaded to the GEO database, and theaccession number is GSE141558. We normalized the readcount data using the FPKM method. Using FDR < 0:05 and∣LogFC ∣ >1 as the screening criterion in the process of geneidentification, the transcriptome sequencing analysis iden-tified 1344 DEGs between the OV-BRCA1 and NC groups,including 824 upregulated DEGs and 520 downregulatedDEGs (Table S2).

The enrichment analysis results showed that in the bio-logical process group, the DEGs were most enriched inresponse to virus; in the cellular component group, the DEGswere most enriched in the nucleosome; and in the molecularfunction group, the DEGs were most enriched in transferaseactivity transferring pentosyl groups Table 3 and Figure 6).

We also performed KOG annotation to reveal the func-tion distribution of the DEGs to clarify the embodiment ofsample differences in gene function. The results of the DEGsclassified on the KOG system are shown in Figure 7.

3.6. GSEA Identifies a Cisplatin Resistance-Related SignalingPathway. GSEA results revealed a positive correlation of theDEGs in the OV-BRCA1 group with CCRT resistance-related signaling pathways, such as the WNT signaling path-way and the JAK/STAT signaling pathway (Figures 8(a),8(b), 8(d), and 8(e)). We intersected the key genes in theWNT and JAK/STAT signaling pathway based on the GSEAresults and the DEGs from the BRCA1 overexpression groupand obtained 5 key DEGs in theWNT signaling pathway and

14 key DEGs in the JAK/STAT signaling pathway. The corre-lation analysis indicated a strong positive correlationbetween BRCA1 and the key DEGs in the WNT and JAK/-STAT signaling pathways (Figures 8(c) and 8(f)).

3.7. BRCA1 May Affect CCRT Resistance via the JAK/STATSignaling Pathway. Tomore closely examine the mechanismsof BRCA1 in CCRT resistance in CSCC, we intersected thekey genes in the WNT and JAK/STAT signaling pathwayand target genes of BRCA1 based on the TRRUST database(Figure 9(a)). We found that BRCA1 may upregulate theexpressions of STAT1, STAT2, and CCND1, three key genesin the JAK/STAT and WNT signaling pathway (Figure 9(b)).We performed qPCR verification, and the results showedthat only STAT1 was significantly increased in SiHa cellswith GV230-BRCA1 plasmid transfection (Figure 9(c)).These findings showed that overexpression of BRCA1 inCSCC cells may enhance resistance to CCRT through activat-ing the JAK/STAT signaling pathway via upregulating STAT1.

4. Discussion

CCRT is the standard therapy for locally advanced CSCC;however, the development of intrinsic or acquired CCRTresistance dramatically decreases its clinical effectiveness.To investigate efficient biomarkers that could predict theresponse to CCRT and serve as potential therapeutic targetsfor CSCC patients, we performed comprehensive analysis ofresistance in CSCC based on a GEO dataset and tran-scriptome sequencing. First, we reanalyzed the GSE56363dataset and identified 609 DEGs including 223 upregulatedDEGs and 386 downregulated DEGs between NCR and CRpatients [18]. We performed GO enrichment for DEGsrelated to resistance in CSCC and identified 10 hub genes

CDC45

RAD51

ASPM

CDCA8

ZWINT

CENPF

BRCA1

EXO1

DTL

HMMR

(b)

Figure 3: PPI networks. (a) PPI network of top 100 DEGs with the highest degrees. Red nodes denote upregulated genes, while green nodesdenote downregulated genes. (b) PPI network of the hub genes. The gradual color represents the degree value.

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0

5

10

15

ASP

M

BRCA

1

CDC4

5L

CDCA

8

CEN

PF

DTL

EXO

1

HM

MR

RAD

51

ZWIN

T

Expr

essio

n of

hub

gen

es

NCRCR

(a)

Heatmap of top ten hub genes from GSE56363

−2.04−2.52−2.26−2.39−2.38−2.35−2.31−2.48−2.41−2.23

−2.15−2.46−2.40−2.64−2.53−2.60−2.64−2.59−2.47−2.40

−0.640.36

−0.67−0.58−0.65−0.59−0.44−0.28−0.94−0.26

−0.18−0.15−0.68−0.21−0.30−0.18−0.37−0.11−0.91−0.86

−0.11−0.57−0.13−0.67−0.43−0.59−0.71−0.66−0.17−0.37

−0.48−0.82−0.15−0.21−0.42−0.64−0.02−0.020.02

−0.74

−1.18−0.51−0.04−0.05−0.25−0.52−0.36−0.46−0.50−0.74

−0.52−0.13−0.660.050.040.11

−0.66−0.200.190.47

−0.150.52

−0.430.24

−0.060.03

−0.01−0.19−0.040.21

−0.100.19

−0.430.390.060.400.15

−0.060.300.49

0.120.040.43

−0.17−0.010.390.880.790.040.03

0.150.090.12

−0.240.52

−0.050.500.360.59

−0.12

0.180.110.240.24

−0.260.610.290.030.300.12

0.570.210.490.420.460.390.580.200.160.77

2.111.101.360.801.540.761.581.541.491.87

0.230.371.211.581.130.820.750.510.580.42

1.100.590.521.070.811.080.450.340.770.75

1.050.421.120.960.851.260.511.080.790.72

0.690.641.100.510.881.140.360.680.530.40

0.380.840.730.270.210.140.530.890.810.46

0.971.680.540.630.780.400.960.600.891.01

BRCA1CDCA8ASPMCDC45LRAD51HMMRCENPFEXO1DTLZWINT

Type

−2

−1

0

1

2

CR

NCR

(b)

ROC of BRCA1

Specificity

Sens

itivi

ty

0.0

0.4

0.8

Sens

itivi

ty

0.0

0.4

0.8

Sens

itivi

ty

0.0

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Sens

itivi

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Sens

itivi

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itivi

ty

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Sens

itivi

ty

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Sens

itivi

ty

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0.8

Sens

itivi

ty

0.0

0.4

0.8

Sens

itivi

ty

0.0

0.4

0.8

ROC of CDCA8

0.624 (0.889, 0.833)

AUC: 0.926

ROC of ASPM

0.624 (0.889, 0.833)

AUC: 0.926

ROC of CDC45L

0.521 (0.889, 0.917)

AUC: 0.917

ROC of RAD510.500 (1.000, 1.000)

AUC: 1.000

ROC of HMMR

0.661 (0.889, 0.750)

AUC: 0.889

ROC of CENPF

0.517 (1.000, 0.917)

AUC: 0.935

ROC of EXO1

0.684 (1.000, 0.917)

AUC: 0.944

ROC of DTL0.345 (0.889, 1.000)

AUC: 0.972

ROC of ZWINT

1.2 0.8 0.4 0.0Specificity

1.2 0.8 0.4 0.0Specificity

1.2 0.8 0.4 0.0Specificity

1.2 0.8 0.4 0.0Specificity

1.2 0.8 0.4 0.0

Specificity1.2 0.8 0.4 0.0

Specificity1.2 0.8 0.4 0.0

Specificity1.2 0.8 0.4 0.0

Specificity1.2 0.8 0.4 0.0

Specificity1.2 0.8 0.4 0.0

0.506 (0.889, 0.833)

AUC: 0.898

0.707 (1.000, 0.917)

AUC: 0.991

(c)

Figure 4: Comprehensive analysis of the hub genes. (a) Violin plots for different expressions of the hub genes between NCR and CR patients.(b) Heat maps of the hub genes in CSCC. (c) ROC curves of the hub genes sorted by AUC. NCR: noncomplete response; CR: completeresponse; ROC: receiver operating characteristic; AUC: area under the curve.

8 BioMed Research International

with the highest degrees from the PPI network, includingBRCA1, CDCA8, ASPM, CDC45, RAD51, HMMR, CENPF,EXO1, DTL, and ZWINT genes. We next performed tran-scriptome sequencing of SiHa cells transfected with GV230-BRCA1 or GV230 plasmid and performed functional anno-tation for DEGs based on GO and KOG database. Finally,

we performed GSEA to identify CCRT resistance related sig-naling pathways to investigate potential molecular mecha-nisms of resistance in CSCC. STAT1, STAT2, and CCND1were selected as the differentially expressed target genes ofBRCA1 in the WNT and JAK/STAT signaling pathway andmay correlate with the CCRT resistance of CSCC.

SiHa SiHa/DDP0

1

2

3

Rela

tive e

xpre

ssio

n of

BRC

A1

⁎

(a)

GV230 GV230-BRCA10.0

0.5

1.0400

500

600

700

800

Relat

ive e

xpre

ssio

n of

BRC

A1

SiHa⁎⁎

(b)

Figure 5: qRT-PCR. (a) The expression of BRCA1 by qRT-PCR between the SiHa/DDP group and the SiHa group. (b) The expression ofBRCA1 in the SiHa cell with GV230-BRCA1 or GV230 transfection. ∗P < 0:05, ∗∗P < 0:01.

Table 3: The significantly enriched analysis of DEGs between OV-BRCA1 and NC SiHa cells.

Category Term Description Adjusted P value Count

BP GO:0051607 Defense response to virus 4:34E − 25 50

BP GO:0009615 Response to virus 1:13E − 24 57

BP GO:0060337 Type I interferon signaling pathway 2:42E − 19 29

BP GO:0071357 Cellular response to type I interferon 2:42E − 19 29

BP GO:0034341 Response to interferon-gamma 7:82E − 19 40

BP GO:0071346 Cellular response to interferon-gamma 7:98E − 19 38

BP GO:0034340 Response to type I interferon 7:98E − 19 29

BP GO:0060333 Interferon-gamma-mediated signaling pathway 4:05E − 17 27

BP GO:1903900 Regulation of viral life cycle 1:13E − 15 31

BP GO:0048525 Negative regulation of viral process 8:16E − 15 25

CC GO:0000786 Nucleosome 2:38E − 11 31

CC GO:0044815 DNA packaging complex 1:05E − 10 31

CC GO:0032993 Protein-DNA complex 3:98E − 07 36

CC GO:0071556 Integral component of lumenal side of endoplasmic reticulum membrane 4:23E − 06 12

CC GO:0098553 Lumenal side of endoplasmic reticulum membrane 4:23E − 06 12

CC GO:0005788 Endoplasmic reticulum lumen 4:81E − 05 41

CC GO:0005925 Focal adhesion 8:43E − 05 49

CC GO:0000788 Nuclear nucleosome 8:43E − 05 12

CC GO:0005924 Cell-substrate adherens junction 9:24E − 05 49

CC GO:0030055 Cell-substrate junction 1:10E − 04 49

MF GO:0016763 Transferase activity transferring pentosyl groups 1:26E − 03 13

MF GO:0003950 NAD+ ADP-ribosyltransferase activity 1:26E − 03 9

MF GO:0002020 Protease binding 1:54E − 02 20

MF GO:0005125 Cytokine activity 1:54E − 02 29

9BioMed Research International

Interferon-gamma-mediated signalingpathway

Response to type I interferonCellular response to type I interferonType I interferon signaling pathway

Regulation of viral life cycleRegulation of viral process

Cellular response to interferon-gammaResponse to interferon-gamma

Defense response to virusResponse to virus

0.03 0.04 0.05 0.06GeneRatio

1.6e−12

1.2e−12

8.0e−13

4.0e−13

P.adjust

405060

Biological process

(a)

MHC protein complexLumenal side of endoplasmic reticulum membrane

Integral component of lumenal side of endoplasmicreticulum membrane

ER to Golgi transport vesicle membrane

Integral component of endoplasmic reticulummembrane

Coated vesicle membraneEndoplasmic reticulum lumen

Cell-substrate junctionCell-substrate adherens junction

Focal adhesion

0.01 0.02 0.03 0.04GeneRatio

1020

3040

3e−04

2e−04

1e−04

P.adjustCell component

(b)

NAD+ ADP-ribosyltransferase activity

Transferase activity, transferring pentosyl groups

Protease binding

Cytokine activity

0.008 0.012 0.016 0.020 0.024GeneRatio

1015

2025

0.015

0.010

0.005

P.adjustMolecular function

(c)

Figure 6: Dot plots for GO enrichment analysis of DEGs between the OV-BRCA1 and NC groups. (a) Dot plot of the top 10 GO terms in thebiological process group. (b) Dot plot of the top 10 GO terms in the cell component group. (c) Dot plot of the top 4 GO terms in the molecularfunction group. The gradual color of the dots indicates the adjusted P value, and the size of the dots indicates the number of genes inthe dot plots.

10 BioMed Research International

BRCA1 (BRCA1 DNA repair associated) is a tumorsuppressor gene and related to hereditary breast and ovar-ian cancer. BRCA1 plays a pivotal role in homologousrecombination-based DNA repair and functions in both cellcycle checkpoint control and maintenance of genomic stabil-ity [30]. Genomic instability caused by DNA repair defectafter BRCA1 deletion may lead to tumorigenesis [31, 32];however, it was also because of DNA repair defect inBRCA1-deficient cells, DNA damage caused by platinum-based chemotherapy or radiation may be substantiallyenhanced [33–35]. Gao et al. found that advanced or meta-static esophageal squamous cell carcinoma patients withlow BRCA1 mRNA expression had increased response rateto cisplatin-based chemotherapy or chemoradiotherapy[36]. For breast cancer and ovarian cancer patients, BRCA1

mutations also increased chemosensitivity and/or radiosensi-tivity [37, 38]. Our study showed that the expression level ofBRCA1 was higher in CSCC resistant to CCRT than that inCSCC sensitive to CCRT by bioinformatics analysis andqPCR verification. ROC curves showed that BRCA1 showeda high diagnostic value in CSCC patients with CR to CCRT,which demonstrated that BRCA1 might be correlated withthe sensitivity of CC cells to CCRT.

Through the analysis of our sequencing results, we foundthat STAT1, STAT2, and CCND1 were upregulated in SiHacells with BRCA1 overexpression, and these findings mightreveal part of the mechanisms by which BRCA1 affects sensi-tivity to chemoradiotherapy in CSCC. Previous studies foundthat BRCA1 is required for the upregulation of STAT1 andSTAT2, and they play a synergistic role in the differential

KOG function classificationPe

rcen

t of g

enes

0.01

0.1

1

10

100

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130

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Num

ber o

f gen

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skel

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)

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eral

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ly(R

)

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etab

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)

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odifi

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)

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tion(

K)

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gene

sis(J

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l div

ision

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)

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sion(

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Chro

mat

in st

ruct

ure a

nd d

ynam

ics(

B)

RNA

pro

cess

ing

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mod

ifica

tion(

A)

Figure 7: Functional classification of euKaryotic Ortholog Groups (KOG) database on DEGs. The x-axis shows the contents of KOG, the lefty-axis shows the percent of genes, and the right y-axis shows the number of genes in the classification.

11BioMed Research International

0.4

WNT signaling pathway

0.2

Enric

hmen

t sco

re

0.0

Treatment<−−−−−−−−−−−−−−Control

(a)

WNT signaling pathway

C1 C2 C3 A1

A2

A3

Type.1

NCOV.BRCA1

–2

–1

0

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–1 1

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WNT6

WNT11

0.96

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WNT signaling pathway

–0.82 –0.64 –0.45 –0.27 –0.09 0.09 0.27 0.45 0.64 0.82

(c)

Figure 8: Continued.

12 BioMed Research International

Treatment<−−−−−−−−−−−−−−Control

JAK/STAT signaling pathway

0.0

0.2

0.4

0.6

Enric

hmen

t sco

re

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JAK-STAT signaling pathway

C1 C2 C3 A1

A2

A3

Type.1

–2

–1

0

1

2

NCOV.BRCA1

(e)

–1 1

IL20

STAT1

IL13RA2

IL11

LIF

IL7R

IL6

STAT2

IL3RA

CSF3

CCND1

IL24

BRCA1

IL15RA

SOCS1

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0.98

JAK-STAT signaling pathway

–0.82 –0.64 –0.45 –0.27 –0.09 0.09 0.27 0.45 0.64 0.82

0.96

(f)

Figure 8: Comprehensive analysis of the WNT and JAK/STAT signaling pathway. (a, d) Enrichment plot of the WNT and JAK/STATsignaling pathway from GSEA. (b, e) The heat map of genes in the WNT and JAK/STAT signaling pathway based on high-throughputsequencing data. (c, f) The relationship between BRCA1 and the key DEGs in the WNT and JAK/STAT signaling pathway.

13BioMed Research International

regulation of IFN-gamma target genes [39, 40]. In breast can-cer patients who are BRCA1 mutation carriers, CCND1expression was significantly reduced [41]. These findings areconsistent with our results, demonstrating that BRCA1expression is positively correlated with STAT1, STAT2, andCCND1. STAT1 and STAT2 are important components ofthe JAK/STAT pathway, and continued activation of theJAK/STAT signal is associated with tumor progression [42,43]. CCND1 is often overexpressed in tumors, and its expres-sion is positively correlated with activation of the JAK/STATpathway [44]. In addition, studies showed the JAK/STATpathway played a crucial role in cisplatin resistance of CC[45, 46]. Furthermore, our qPCR validation revealed thatSTAT1 was upregulated in SiHa cells overexpressing BRCA1in our study.We speculated that BRCA1may increase the sen-

sitivity of CSCC patients to cisplatin-based CCRT by upregu-lating STAT1 to activate the JAK/STAT pathway.

5. Conclusion

Our work analyzed transcriptome-related changes in CCRT-resistant CSCC patients and SiHa cells based on GEO datasetanalysis and transcriptome sequencing. We found thatBRCA1 may enhance the sensitivity of CSCC patients tocisplatin-based CCRT by upregulating STAT1 to activatethe JAK/STAT pathway. These findings may provide newtherapeutic strategies to overcome intrinsic or acquiredCCRT resistance in CSCC. Additional studies are needed tofurther elucidate the specific mechanism of BRCA1 in CCRTresistance of CSCC.

BRCA1 targets

WNT pathway

JAK/STAT pathway

112

1

00

4

54

(a)

WNT pathway

JAK/STATpathway

BRCA1

STAT1

Activat

ion

Activation

CCND1

STAT2

Unknown

(b)

GV230 GV230-BRCA1

3

2

1

0

4

Rela

tive e

xpre

ssio

n of

STA

T1Re

lativ

e exp

ress

ion

of S

TAT2

GV230 GV230-BRCA1

GV230 GV230-BRCA1

0.0

0.5

1.0

1.5

0.0

0.5

1.0

1.5Re

lativ

e exp

ress

ion

of C

CND

1

⁎

#

#

(c)

Figure 9: Screening of BRCA1-related CCRT resistance mechanisms in CSCC. (a) The Venn analysis of differentially expressed target genesof BRCA1. (b) The potential regulatory network of BRCA1 in the CCRT resistance of CSCC. (c) The expression of STAT1, STAT2, andCCND1 by qRT-PCR in the SiHa cell with GV230-BRCA1 or GV230 transfection.

14 BioMed Research International

Data Availability

The following information was supplied regarding data avail-ability. The transcriptome sequencing data has beenuploaded to the GEO database, and the accession number isGSE141558. Expression profiles of GSE56363 in the manu-script were downloaded from NCBI-GEO (https://www.ncbi.nlm.nih.gov/gds).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

All authors were involved in the study and approved the finalversion of the manuscript.

Acknowledgments

This study was supported by grants from the National Natu-ral Science Foundation of China (No. 81772772) and theMedical and Health Talents Project of Jilin Province(2019SCZT003).

Supplementary Materials

Table S1: differentially expressed genes in the BRCA1 overex-pression group compared with control group. Table S2: sum-mary of sequencing data. (Supplementary Materials)

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