# Identification of the Key Genes and Pathways in Esophageal Carcinoma

Authors: Peng Su; Shiwang Wen; Yuefeng Zhang; Yong Li; Yanzhao Xu; Yonggang Zhu; Huilai Lv; Fan Zhang; Mingbo Wang; Ziqiang Tian
Journal: Gastroenterology Research and Practice
 (2016)

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## Abstract

Objective. Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it.Methods. 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC.Results. A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis.Conclusion. The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.


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## Body

## 1. Introduction

Esophageal carcinoma (EC) is the sixth leading cause of cancer mortality in males and the ninth leading cause of cancer mortality in females in 2012 worldwide [1]. The highest incident rates of EC are found in Eastern Asia, Southern Africa, and Eastern Africa and the lowest incidence rate of EC is found in Western Africa [1]. Esophageal carcinoma is usually 3 to 4 times more common among men than women. The 5-year overall survival ranges from 15% to 25% [2]. In China, it is predicted that EC is the fourth leading cause of cancer deaths in males and females after lung and bronchus, stomach, and liver in 2015 [3].EC is classified as esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) according to histological type and ESCC is the predominant histological type of EC in the world [2]. It is reported that tobacco consumption, alcohol consumption, and low intake of fruits and vegetables are major risk factors for ESCC [4]. Overweight, obesity, gastroesophagus reflux disease (GERD), and Barrett’s esophagus increase incidence risk of EAC [1, 5].In addition to the above-mentioned environmental factors, abnormal expression of miRNA and genes and methylation of genes and SNPs are associated with EC tumorigenesis and development. miR-219-1 rs107822G > A polymorphism might significantly decrease ESCC risk through changing individual susceptibility to Chinese Kazakhs [5]. The cases carrying the GG variant homozygote have a significant 2.81-fold increased risk of EC [6]. miR-330-3p promotes cell growth, cell migration, and invasion and inhibits cisplatin-induced apoptosis in ESCC cells via suppression of PDCD4 expression [7]. miR-199a-5p downregulation contributes to enhancing EC cell proliferation through upregulation of mitogen-activated protein kinase kinase kinase-11 [8]. DACT2 is frequently methylated in human esophageal cancer; methylated DATC2 accelerates esophageal cancer development by activating Wnt signaling [9]. RUNX3 methylation is associated with an increased risk, progression, and poor survival in EC [10].Currently, the molecular mechanism of EC was unclear. In this study, we used bioinformatics methods to analyze the mRNA expression data of EC, which were available on the GEO database, to identify key genes and pathways in EC, aiming to provide valuable information for further pathogenesis mechanism elucidation and provide ground work for therapeutic targets identification for EC.


## 2. Materials and Methods

### 2.1. Expression Profile Microarray

Gene expression profiles data were downloaded from the Gene Expression Omnibus (GEO) data repository (http://www.ncbi.nlm.nih.gov/geo/). The datasets of patients receiving preoperative treatment before oesophagectomy and cell lines receiving drug stimulus were excluded. Total of 5 mRNA expression datasets of EC tissues/cell lines comprising GSE53625, GSE33810, GSE17351, GSE9982, and GSE12737 were included in our study.


### 2.2. Identification of DEGs

The raw data of the mRNA expression profiles were downloaded and analyzed by R language software [11]. Background correction, quartile data normalization, and probe summarization were applied for the original data. The limma [12] method in Bioconductor (http://www.bioconductor.org/) was used to identify genes which were differentially expressed between EC and normal controls; the significance of DEGs was calculated byt-test and was represented by p value. To reduce the risk of false positives, p values were adjusted for multiple testing using the Benjamini-Hochberg False Discovery Rate (FDR) method. The corrected p value was represented by FDR [13]. FDR < 0.05 were considered as the cutoff values for DEG screening.


### 2.3. Gene Ontology Analysis

GO is a useful tool for collecting a large number of gene annotation terms [14]. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) [15], is bioinformatics resources consisting of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological functional annotation from large gene/protein lists, such as being derived from high-throughput genomic experiments. To gain the in-depth understanding of the biological functions of DEGs, DAVID tool was used to obtain the enriched GO terms of DEGs based on the hypergeometric distribution to compute p values, which were corrected by the Benjamini and Hochberg FDR method for multiple hypothesis testing. FDR < 0.05 was set as the threshold value.


### 2.4. KEGG Enrichment Pathways

KEGG is a database resource for understanding functions of genes list from molecular level [16]. GeneCoDis3 is a valuable tool to functionally interpret results from experimental techniques in genomics [17]. This web-based application integrates different sources of information for finding groups of genes with similar biological meaning. The enrichment analysis of GeneCoDis3 is essential in the interpretation of high-throughput experiments. In the study, GeneCoDis3 software was used to test the statistical enrichment of DEGs in KEGG pathways. p<0.05 was set as the threshold value.


### 2.5. PPI Interaction Network

The Biological General Repository for Interaction Datasets (BioGRID:http://thebiogrid.org/) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species including budding yeastSaccharomyces cerevisiae, the fission yeastSchizosaccharomyces pombe, and the model plantArabidopsis thaliana. In a word, BioGRID is a depository for genetic and protein interactions based on experimental verification [18]. The top 10 upregulated genes and top 10 downregulated genes between EC and normal controls were subjected to BioGRID database to get the predicted PPIs of these DEGs. The PPIs were visualized in Cytoscape [17].


### 2.6. qRT-PCR Validation

Total RNA of fresh paired EC tumor and adjacent nontumor specimens were extracted using TRIzol reagent (Invitrogen, CA, USA). The SuperScript III Reverse Transcription Kit (Invitrogen, CA, USA) was used to synthesize the cDNA. qRT-PCR reactions were performed using Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) on the Applied Biosystems 7500 (Foster City, CA, USA).β-actin was used as internal control for mRNA detected. The relative expression of genes was calculated using the comparative Ct methods [19]. The PCR primers were used as shown in supplementary Table S3 in Supplementary Material available online at http://dx.doi.org/10.1155/2016/2968106.


## 2.1. Expression Profile Microarray

Gene expression profiles data were downloaded from the Gene Expression Omnibus (GEO) data repository (http://www.ncbi.nlm.nih.gov/geo/). The datasets of patients receiving preoperative treatment before oesophagectomy and cell lines receiving drug stimulus were excluded. Total of 5 mRNA expression datasets of EC tissues/cell lines comprising GSE53625, GSE33810, GSE17351, GSE9982, and GSE12737 were included in our study.


## 2.2. Identification of DEGs

The raw data of the mRNA expression profiles were downloaded and analyzed by R language software [11]. Background correction, quartile data normalization, and probe summarization were applied for the original data. The limma [12] method in Bioconductor (http://www.bioconductor.org/) was used to identify genes which were differentially expressed between EC and normal controls; the significance of DEGs was calculated byt-test and was represented by p value. To reduce the risk of false positives, p values were adjusted for multiple testing using the Benjamini-Hochberg False Discovery Rate (FDR) method. The corrected p value was represented by FDR [13]. FDR < 0.05 were considered as the cutoff values for DEG screening.


## 2.3. Gene Ontology Analysis

GO is a useful tool for collecting a large number of gene annotation terms [14]. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) [15], is bioinformatics resources consisting of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological functional annotation from large gene/protein lists, such as being derived from high-throughput genomic experiments. To gain the in-depth understanding of the biological functions of DEGs, DAVID tool was used to obtain the enriched GO terms of DEGs based on the hypergeometric distribution to compute p values, which were corrected by the Benjamini and Hochberg FDR method for multiple hypothesis testing. FDR < 0.05 was set as the threshold value.


## 2.4. KEGG Enrichment Pathways

KEGG is a database resource for understanding functions of genes list from molecular level [16]. GeneCoDis3 is a valuable tool to functionally interpret results from experimental techniques in genomics [17]. This web-based application integrates different sources of information for finding groups of genes with similar biological meaning. The enrichment analysis of GeneCoDis3 is essential in the interpretation of high-throughput experiments. In the study, GeneCoDis3 software was used to test the statistical enrichment of DEGs in KEGG pathways. p<0.05 was set as the threshold value.


## 2.5. PPI Interaction Network

The Biological General Repository for Interaction Datasets (BioGRID:http://thebiogrid.org/) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species including budding yeastSaccharomyces cerevisiae, the fission yeastSchizosaccharomyces pombe, and the model plantArabidopsis thaliana. In a word, BioGRID is a depository for genetic and protein interactions based on experimental verification [18]. The top 10 upregulated genes and top 10 downregulated genes between EC and normal controls were subjected to BioGRID database to get the predicted PPIs of these DEGs. The PPIs were visualized in Cytoscape [17].


## 2.6. qRT-PCR Validation

Total RNA of fresh paired EC tumor and adjacent nontumor specimens were extracted using TRIzol reagent (Invitrogen, CA, USA). The SuperScript III Reverse Transcription Kit (Invitrogen, CA, USA) was used to synthesize the cDNA. qRT-PCR reactions were performed using Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) on the Applied Biosystems 7500 (Foster City, CA, USA).β-actin was used as internal control for mRNA detected. The relative expression of genes was calculated using the comparative Ct methods [19]. The PCR primers were used as shown in supplementary Table S3 in Supplementary Material available online at http://dx.doi.org/10.1155/2016/2968106.


## 3. Results

### 3.1. Identification of DEGs

Five mRNA expression profiles including 208 EC samples and 195 normal controls were downloaded and analyzed, as shown in Table1. 208 EC samples comprised 207 squamous cell carcinoma samples and 1 adenocarcinoma sample. 1955 DEGs were identified in EC compared to normal control, including 919 upregulated and 1036 downregulated genes. The top 10 significantly upregulated and downregulated genes were listed in Table 2. The most significantly up- and downregulated genes were CDK4 and THSD4, respectively. The full list of DEGs in EC was shown in supplementary Table S1.Table 1
The information of gene expression microarrays of EC.
GEO ID Platform Case : control Sample type Country Time Author GSE53625 GPL18109 CBCHomo sapiens lncRNA + mRNA microarray V2.0 179 : 179 Esophageal squamous cell carcinoma China 2014 Li et al. [42] GSE33810 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 2 : 1 Esophageal squamous cell carcinoma HK 2013 Chen et al. [43] GSE17351 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 5 : 5 Esophageal squamous cell carcinoma USA 2009 Long et al. [44] GSE9982 GPL1928 CodeLink Human 20K ver4.1 20 : 2 Esophageal squamous cancer Japan 2006 Shimokuni et al. [45] GSE12737 GPL7262 Human ORESTES NoMatch 4.8k v1.0 2 : 8 Squamous cell & adenocarcinoma Brazil 2009 Mello et al. [46] EC: esophageal carcinoma.Table 2
The top 10 up-regulated and top 10 down-regulated DEGs in EC.
Gene ID Gene symbol Official full name FDR Upregulated (top 10) 1019 CDK4 Cyclin-dependent kinase 4 0.0002252 4605 MYBL2 MYB protooncogene like 2 0.0002252 7203 CCT3 Chaperonin containing TCP1 subunit 3 0.0003378 83461 CDCA3 Cell division cycle associated 3 0.0004504 1033 CDKN3 Cyclin-dependent kinase inhibitor 3 0.0004504 1063 CENPF Centromere protein F 0.0004729 9156 EXO1 Exonuclease 1 0.0004729 79075 DSCC1 DNA replication and sister chromatid cohesion 1 0.0005405 4751 NEK2 NIMA related kinase 2 0.0005405 Downregulated (top 10) 79875 THSD4 Thrombospondin type 1 domain containing 4 0.0002252 79026 AHNAK AHNAK nucleoprotein 0.0004729 6493 SIM2 Single-minded family bHLH transcription factor 2 0.0004729 7881 KCNAB1 Potassium voltage-gated channel subfamily A member regulatory beta subunit 1 0.0005405 90865 IL33 Interleukin 33 0.0008812 55287 TMEM40 Transmembrane protein 40 0.0008812 966 CD59 CD59 molecule 0.0015608 5121 PCP4 Purkinje cell protein 4 0.0015608 22885 ABLIM3 Actin binding LIM protein family member 3 0.0016629 3590 IL11RA Interleukin 11 receptor subunit alpha 0.0016629 EC: esophageal carcinoma; FDR: false discovery rate.


### 3.2. GO Analysis of DEGs

Following GO analyses for up- and downregulated DEGs, significant GO terms including biological process, cellular component, and molecular function were collected. For upregulated DEGs, cell cycle was the most significant enrichment of biological process; membrane-enclosed lumen was the highest enrichment of cellular component; nucleotide binding was the highest enrichment of molecular function, as shown in Table3. For downregulated DEGs, response to wounding was the most significant enrichment of biological process; actin cytoskeleton was the highest enrichment of cellular component and cytoskeletal protein binding was the highest enrichment of molecular function, as shown in Table 4.Table 3
GO annotation of upregulated DEGs in EC.
GO ID GO term Count p-value FDR Biological process GO:0007049 Cell cycle 152 4.10E − 13 7.59E − 10 GO:0022402 Cell cycle process 118 3.44E − 12 6.36E − 09 GO:0022403 Cell cycle phase 90 2.00E − 10 3.71E − 07 GO:0000278 Mitotic cell cycle 82 5.34E − 10 9.87E − 07 GO:0051301 Cell division 67 8.80E − 09 1.63E − 05 GO:0000279 M phase 70 6.27E − 08 1.16E − 04 GO:0000087 M phase of mitotic cell cycle 49 3.38E − 06 0.0062547 GO:0000280 Nuclear division 48 4.65E − 06 0.0086014 GO:0007067 Mitosis 48 4.65E − 06 0.0086014 GO:0048285 Organelle fission 49 6.41E − 06 0.011854 GO:0033554 Cellular response to stress 95 2.02E − 05 0.0373322 Cellular component GO:0031974 Membrane-enclosed lumen 276 1.12E − 10 1.65E − 07 GO:0043233 Organelle lumen 270 2.41E − 10 3.56E − 07 GO:0043232 Intracellular non-membrane-bounded organelle 359 8.74E − 10 1.29E − 06 GO:0043228 Non-membrane-bounded organelle 359 8.74E − 10 1.29E − 06 GO:0070013 Intracellular organelle lumen 259 4.54E − 09 6.71E − 06 GO:0031981 Nuclear lumen 216 1.90E − 08 2.80E − 05 GO:0000775 Chromosome, centromeric region 36 2.52E − 08 3.72E − 05 GO:0005829 Cytosol 192 1.36E − 06 0.0020016 GO:0015630 Microtubule cytoskeleton 92 4.62E − 06 0.0068255 GO:0000793 Condensed chromosome 32 6.75E − 06 0.009972 GO:0000779 Condensed chromosome, centromeric region 21 7.55E − 06 0.011151 GO:0044427 Chromosomal part 69 9.92E − 06 0.0146408 GO:0005635 Nuclear envelope 43 1.37E − 05 0.0202598 GO:0000777 Condensed chromosome kinetochore 19 1.48E − 05 0.0219025 GO:0005694 Chromosome 78 1.75E − 05 0.02589 GO:0000776 Kinetochore 22 2.72E − 05 0.0401619 Molecular function GO:0000166 Nucleotide binding 305 5.53E − 06 0.0090275 GO:0017076 Purine nucleotide binding 266 5.55E − 06 0.0090714 GO:0030554 Adenyl nucleotide binding 223 1.07E − 05 0.0175078 GO:0001883 Purine nucleoside binding 225 1.49E − 05 0.0242774 GO:0032555 Purine ribonucleotide binding 252 2.35E − 05 0.0383944 GO:0032553 Ribonucleotide binding 252 2.35E − 05 0.0383944 GO:0001882 Nucleoside binding 225 2.44E − 05 0.0398342 EC: esophageal carcinoma; FDR: false discovery rate.Table 4
GO annotation of downregulated DEGs in EC.
GO ID GO term Count p value FDR Biological process GO:0009611 Response to wounding 65 1.98E − 08 3.57E − 05 GO:0042060 Wound healing 33 5.75E − 08 1.04E − 04 GO:0030097 Hemopoiesis 32 1.85E − 05 0.0334238 GO:0007167 Enzyme linked receptor protein signaling pathway 41 2.03E − 05 0.0365533 GO:0030036 Actin cytoskeleton organization 31 2.05E − 05 0.0370181 GO:0048534 Hemopoietic or lymphoid organ development 34 2.06E − 05 0.0372021 GO:0007155 Cell adhesion 69 2.10E − 05 0.0378896 GO:0042692 Muscle cell differentiation 21 2.14E − 05 0.0386651 GO:0022610 Biological adhesion 69 2.19E − 05 0.0394751 GO:0007178 Transmembrane receptor protein serine/threonine kinase signaling pathway 19 2.53E − 05 0.0456886 Cellular component GO:0015629 Actin cytoskeleton 36 5.84E − 06 0.008305 GO:0005794 Golgi apparatus 83 7.36E − 06 0.0104637 GO:0005856 Cytoskeleton 118 1.23E − 05 0.0175254 Molecular function GO:0008092 Cytoskeletal protein binding 59 8.55E − 07 0.0013403 EC: esophageal carcinoma; FDR: false discovery rate.


### 3.3. KEGG Enrichment Pathways of DEGs

Following KEGG enrichment analysis for DEGs, significant KEGG terms were collected. The pathways enriched by 919 upregulated DEGs were mainly related to cell cycle, RNA transport, and p53 signaling pathway (Table5). 1036 downregulated DEGs were significantly enriched in Endocytosis, focal adhesion, and vascular smooth muscle contraction, as shown in Table 6.Table 5
The KEGG pathway enrichment of up-regulated DEGs in EC.
KEGG ID KEGG terms Count FDR Genes hsa04110 Cell cycle 19 7.86E − 08 CDK6, CCNE2, CCNB2, FZR1, CCNA2, CDC7, YWHAQ, MCM7, CCNE1, CDK4, E2F5, CCNB1, MAD2L1, CDC25B, MCM6, BUB1, RBL1, MCM2, CDK1 hsa03013 RNA transport 20 1.09E − 07 RAN, EIF3H, NUP43, UBE2I, NUP133, MAGOHB, POP5, THOC5, CLNS1A, NUP205, GEMIN6, NUP93, NUP62, SUMO1, EIF2S2, NUP153, RANGAP1, NUP160, RPP25, DDX20 hsa04115 p53 signaling pathway 5 2.90E − 06 CCNE2, CCNB2, CCNE1, CCNB1, CDK1 hsa04914 Progesterone-mediated oocyte maturation 8 1.42E − 05 CCNB2, FZR1, CCNA2, CCNB1, MAD2L1, CDC25B, BUB1, CDK1 hsa03050 Proteasome 9 1.56E − 05 PSMD7, SHFM1, PSMD3, PSMA5, PSMB1, PSMB3, PSMA3, PSMD4, PSMA7 hsa03040 Spliceosome 15 1.66E − 05 SNRPC, SRSF9, XAB2, MAGOHB, NAA38, BUD31, SNRPF, NHP2L1, SRSF3, PQBP1, USP39, SNRNP40, SNRPD1, SNRPD2, SF3B2 hsa03030 DNA replication 8 4.24E − 05 RNASEH2A, RNASEH1, MCM7, POLE2, MCM6, RNASEH2C, MCM2, RFC4 hsa03008 Ribosome biogenesis in eukaryotes 11 4.37E − 05 UTP18, RAN, UTP15, NOP56, DKC1, POP5, FBL, NHP2L1, TCOF1, GNL3L, RPP25 hsa03440 Homologous recombination 7 4.37E − 05 SHFM1, MRE11A, RAD54B, XRCC2, RAD54L, BLM, TOP3A hsa04114 Oocyte meiosis 8 8.96E − 05 CCNE2, CCNB2, YWHAQ, CCNE1, CCNB1, MAD2L1, BUB1, CDK1 hsa05162 Measles 4 0.0001531 CDK6, CCNE2, CCNE1, CDK4 hsa05222 Small cell lung cancer 4 0.0001531 CDK6, CCNE2, CCNE1, CDK4 hsa05200 Pathways in cancer 23 0.0001815 VEGFB, CDK6, MTOR, FH, CCNE2, LEF1, BIRC5, CCNE1, CDK4, TCEB1, MSH6, EGF, FZD2, TFG, CKS1B, TRAF4, HSP90AA1, TRAF3, PPARG, HSP90AB1, FGF12, PIAS4, STK4 hsa00510 N-Glycan biosynthesis 8 0.000312 RFT1, ALG10, RPN2, ALG10B, ALG1, MOGS, ALG5, B4GALT2 EC: esophageal carcinoma; FDR: false discovery rate.Table 6
The KEGG pathway enrichment of downregulated DEGs in EC.
KEGG ID KEGG terms Count FDR Genes hsa04144 Endocytosis 23 5.22E − 06 STAMBP, RAB11FIP5, SH3KBP1, KIT, FOLR2, F2R, TGFBR2, VPS4B, SH3GLB1, CHMP5, CXCR2, PDGFRA, CLTB, FOLR1, STAM2, ARAP2, DAB2, EEA1, PDCD6IP, RAB11FIP2, CBL, EPN3, VPS37B hsa04510 Focal adhesion 19 0.000354 ITGA1, ZYX, LAMB2, MYLK, IGF1, CCND2, ITGA2, RAP1A, PDGFRA, ITGA5, TNXB, VWF, PIK3R1, JUN, COL6A2, BCL2, ROCK1, MYL12A, THBS3 hsa04270 Vascular smooth muscle contraction 14 0.000383 JMJD7-PLA2G4B, MYLK, ADCY9, GNA13, PRKG1, ITPR2, PPP1R12B, GNAQ, MYH11, ACTG2, ROCK1, PLA2G2A, MRVI1, ITPR1 hsa00330 Arginine and proline metabolism 4 0.000425 ALDH7A1, MAOB, GATM, MAOA hsa04360 Axon guidance 15 0.000456 EPHA1, ROBO1, SEMA4B, DPYSL2, ABLIM3, PPP3CC, NCK2, GNAI2, SEMA3F, PPP3CA, RGS3, NTN1, ROCK1, PPP3CB, EFNB2 hsa04020 Calcium signaling pathway 5 0.00046 PPP3CC, ITPR2, PPP3CA, PPP3CB, ITPR1 hsa04662 B cell receptor signaling pathway 4 0.000508 PPP3CC, JUN, PPP3CA, PPP3CB hsa05014 Amyotrophic lateral sclerosis 3 0.000583 PPP3CC, PPP3CA, PPP3CB hsa00340 Histidine metabolism 3 0.000583 ALDH7A1, MAOB, MAOA hsa04720 Long-term potentiation 6 0.000623 PPP3CC, ITPR2, GNAQ, PPP3CA, PPP3CB, ITPR1 hsa04114 Oocyte meiosis 6 0.00068 ADCY9, PPP3CC, ITPR2, PPP3CA, PPP3CB, ITPR1 hsa04730 Long-term depression 10 0.000701 JMJD7-PLA2G4B, IGF1, GNA13, PRKG1, ITPR2, PPP2CB, GNAQ, GNAI2, PLA2G2A, ITPR1 hsa04141 Protein processing in endoplasmic reticulum 16 0.000709 SEC63, UBE2J1, EIF2AK3, ATF6, CRYAB, UBE2D3, DNAJB2, SEC31B, MAN1A1, ERO1L, BCL2, HERPUD1, DNAJC3, UBQLN2, RAD23B, LMAN1 hsa04912 GnRH signaling pathway 12 0.000736 JMJD7-PLA2G4B, MMP2, ADCY9, MAP3K3, HBEGF, ITPR2, MAPK7, GNAQ, MAP3K4, JUN, PLA2G2A, ITPR1 hsa00280 Valine, leucine, and isoleucine degradation 8 0.000738 ALDH7A1, ACADM, HMGCS1, MUT, ABAT, ACADSB, ACAD8, AUH EC: esophageal cancer; FDR: false discovery rate.


### 3.4. PPI Network Construction

Based on data from the BioGRID database, the PPI network was the top 10 upregulated and downregulated DEGs which were constructed by Cytoscape software (Figure1). The network consisted of 451 nodes and 499 edges. In the PPI networks the nodes with high degree are defined as hub proteins. The most significant hub proteins in the PPI network were CDK4 (degree = 132) and CCT3 (degree = 127); as shown in Figure 1, the red circular nodes represent upregulated DEGs and green circular nodes represent downregulated DEGs, respectively.Figure 1
The protein-protein network of top 10 up- and downregulated DEGs in EC. The green circular nodes represent downregulation DEGs in EC; the red circular nodes represent downregulation DEGs in EC. Solid lines indicate interaction between DEGs and proteins.


### 3.5. qRT-PCR Validation of DEGs in EC Tissues

To validate the microarray analysis data, the expression of DEGs including CCT3, CDK4, MYBL2, CENPF, CDKN3, CDCA3, THSD4, and SIM2 was detected by qRT-PCR in 5 paired EC tumor and adjacent nontumor tissues. The 5 patients received surgery treatment in Fourth Hospital of Hebei Medical University. The histological type of 5 subjects was ESCC and the detailed information of subjects was shown in supplementary Table S2. As shown in Figures2(a) and 2(b) the expression level of CCT3 and MYBL2 was significantly upregulated in ESCC. CDK4, CENPF, CDKN3, and CDCA3 had the upregulation tendency in ESCC (Figures 2(c)–2(f)), respectively. SIM2 was significantly downregulated in ESCC (Figure 2(g)). THSD4 had the downregulation tendency in ESCC (Figure 2(h)). The qRT-PCR results were matched with the microarray analysis.Figure 2
The qRT-PCR validation of the expression level of DEGs in EC compared to adjacent nontumor tissues. (a) CCT3; (b) MYBL2; (c) CDK4; (d) CENPF; (e) CDKN3; (f) CDCA3; (g) SIM2; (h) THSD4. EC: esophageal carcinoma; CON: adjacent nontumor tissues of ESCC. At least three independent experiments were performed for statistical evaluation. qRT-PCR experimental data were expressed as means ± SD. The statistical significance was evaluated using Student’st-test and p<0.05 was considered as a significant difference.
(a) (b) (c) (d) (e) (f) (g) (h)


## 3.1. Identification of DEGs

Five mRNA expression profiles including 208 EC samples and 195 normal controls were downloaded and analyzed, as shown in Table1. 208 EC samples comprised 207 squamous cell carcinoma samples and 1 adenocarcinoma sample. 1955 DEGs were identified in EC compared to normal control, including 919 upregulated and 1036 downregulated genes. The top 10 significantly upregulated and downregulated genes were listed in Table 2. The most significantly up- and downregulated genes were CDK4 and THSD4, respectively. The full list of DEGs in EC was shown in supplementary Table S1.Table 1
The information of gene expression microarrays of EC.
GEO ID Platform Case : control Sample type Country Time Author GSE53625 GPL18109 CBCHomo sapiens lncRNA + mRNA microarray V2.0 179 : 179 Esophageal squamous cell carcinoma China 2014 Li et al. [42] GSE33810 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 2 : 1 Esophageal squamous cell carcinoma HK 2013 Chen et al. [43] GSE17351 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 5 : 5 Esophageal squamous cell carcinoma USA 2009 Long et al. [44] GSE9982 GPL1928 CodeLink Human 20K ver4.1 20 : 2 Esophageal squamous cancer Japan 2006 Shimokuni et al. [45] GSE12737 GPL7262 Human ORESTES NoMatch 4.8k v1.0 2 : 8 Squamous cell & adenocarcinoma Brazil 2009 Mello et al. [46] EC: esophageal carcinoma.Table 2
The top 10 up-regulated and top 10 down-regulated DEGs in EC.
Gene ID Gene symbol Official full name FDR Upregulated (top 10) 1019 CDK4 Cyclin-dependent kinase 4 0.0002252 4605 MYBL2 MYB protooncogene like 2 0.0002252 7203 CCT3 Chaperonin containing TCP1 subunit 3 0.0003378 83461 CDCA3 Cell division cycle associated 3 0.0004504 1033 CDKN3 Cyclin-dependent kinase inhibitor 3 0.0004504 1063 CENPF Centromere protein F 0.0004729 9156 EXO1 Exonuclease 1 0.0004729 79075 DSCC1 DNA replication and sister chromatid cohesion 1 0.0005405 4751 NEK2 NIMA related kinase 2 0.0005405 Downregulated (top 10) 79875 THSD4 Thrombospondin type 1 domain containing 4 0.0002252 79026 AHNAK AHNAK nucleoprotein 0.0004729 6493 SIM2 Single-minded family bHLH transcription factor 2 0.0004729 7881 KCNAB1 Potassium voltage-gated channel subfamily A member regulatory beta subunit 1 0.0005405 90865 IL33 Interleukin 33 0.0008812 55287 TMEM40 Transmembrane protein 40 0.0008812 966 CD59 CD59 molecule 0.0015608 5121 PCP4 Purkinje cell protein 4 0.0015608 22885 ABLIM3 Actin binding LIM protein family member 3 0.0016629 3590 IL11RA Interleukin 11 receptor subunit alpha 0.0016629 EC: esophageal carcinoma; FDR: false discovery rate.


## 3.2. GO Analysis of DEGs

Following GO analyses for up- and downregulated DEGs, significant GO terms including biological process, cellular component, and molecular function were collected. For upregulated DEGs, cell cycle was the most significant enrichment of biological process; membrane-enclosed lumen was the highest enrichment of cellular component; nucleotide binding was the highest enrichment of molecular function, as shown in Table3. For downregulated DEGs, response to wounding was the most significant enrichment of biological process; actin cytoskeleton was the highest enrichment of cellular component and cytoskeletal protein binding was the highest enrichment of molecular function, as shown in Table 4.Table 3
GO annotation of upregulated DEGs in EC.
GO ID GO term Count p-value FDR Biological process GO:0007049 Cell cycle 152 4.10E − 13 7.59E − 10 GO:0022402 Cell cycle process 118 3.44E − 12 6.36E − 09 GO:0022403 Cell cycle phase 90 2.00E − 10 3.71E − 07 GO:0000278 Mitotic cell cycle 82 5.34E − 10 9.87E − 07 GO:0051301 Cell division 67 8.80E − 09 1.63E − 05 GO:0000279 M phase 70 6.27E − 08 1.16E − 04 GO:0000087 M phase of mitotic cell cycle 49 3.38E − 06 0.0062547 GO:0000280 Nuclear division 48 4.65E − 06 0.0086014 GO:0007067 Mitosis 48 4.65E − 06 0.0086014 GO:0048285 Organelle fission 49 6.41E − 06 0.011854 GO:0033554 Cellular response to stress 95 2.02E − 05 0.0373322 Cellular component GO:0031974 Membrane-enclosed lumen 276 1.12E − 10 1.65E − 07 GO:0043233 Organelle lumen 270 2.41E − 10 3.56E − 07 GO:0043232 Intracellular non-membrane-bounded organelle 359 8.74E − 10 1.29E − 06 GO:0043228 Non-membrane-bounded organelle 359 8.74E − 10 1.29E − 06 GO:0070013 Intracellular organelle lumen 259 4.54E − 09 6.71E − 06 GO:0031981 Nuclear lumen 216 1.90E − 08 2.80E − 05 GO:0000775 Chromosome, centromeric region 36 2.52E − 08 3.72E − 05 GO:0005829 Cytosol 192 1.36E − 06 0.0020016 GO:0015630 Microtubule cytoskeleton 92 4.62E − 06 0.0068255 GO:0000793 Condensed chromosome 32 6.75E − 06 0.009972 GO:0000779 Condensed chromosome, centromeric region 21 7.55E − 06 0.011151 GO:0044427 Chromosomal part 69 9.92E − 06 0.0146408 GO:0005635 Nuclear envelope 43 1.37E − 05 0.0202598 GO:0000777 Condensed chromosome kinetochore 19 1.48E − 05 0.0219025 GO:0005694 Chromosome 78 1.75E − 05 0.02589 GO:0000776 Kinetochore 22 2.72E − 05 0.0401619 Molecular function GO:0000166 Nucleotide binding 305 5.53E − 06 0.0090275 GO:0017076 Purine nucleotide binding 266 5.55E − 06 0.0090714 GO:0030554 Adenyl nucleotide binding 223 1.07E − 05 0.0175078 GO:0001883 Purine nucleoside binding 225 1.49E − 05 0.0242774 GO:0032555 Purine ribonucleotide binding 252 2.35E − 05 0.0383944 GO:0032553 Ribonucleotide binding 252 2.35E − 05 0.0383944 GO:0001882 Nucleoside binding 225 2.44E − 05 0.0398342 EC: esophageal carcinoma; FDR: false discovery rate.Table 4
GO annotation of downregulated DEGs in EC.
GO ID GO term Count p value FDR Biological process GO:0009611 Response to wounding 65 1.98E − 08 3.57E − 05 GO:0042060 Wound healing 33 5.75E − 08 1.04E − 04 GO:0030097 Hemopoiesis 32 1.85E − 05 0.0334238 GO:0007167 Enzyme linked receptor protein signaling pathway 41 2.03E − 05 0.0365533 GO:0030036 Actin cytoskeleton organization 31 2.05E − 05 0.0370181 GO:0048534 Hemopoietic or lymphoid organ development 34 2.06E − 05 0.0372021 GO:0007155 Cell adhesion 69 2.10E − 05 0.0378896 GO:0042692 Muscle cell differentiation 21 2.14E − 05 0.0386651 GO:0022610 Biological adhesion 69 2.19E − 05 0.0394751 GO:0007178 Transmembrane receptor protein serine/threonine kinase signaling pathway 19 2.53E − 05 0.0456886 Cellular component GO:0015629 Actin cytoskeleton 36 5.84E − 06 0.008305 GO:0005794 Golgi apparatus 83 7.36E − 06 0.0104637 GO:0005856 Cytoskeleton 118 1.23E − 05 0.0175254 Molecular function GO:0008092 Cytoskeletal protein binding 59 8.55E − 07 0.0013403 EC: esophageal carcinoma; FDR: false discovery rate.


## 3.3. KEGG Enrichment Pathways of DEGs

Following KEGG enrichment analysis for DEGs, significant KEGG terms were collected. The pathways enriched by 919 upregulated DEGs were mainly related to cell cycle, RNA transport, and p53 signaling pathway (Table5). 1036 downregulated DEGs were significantly enriched in Endocytosis, focal adhesion, and vascular smooth muscle contraction, as shown in Table 6.Table 5
The KEGG pathway enrichment of up-regulated DEGs in EC.
KEGG ID KEGG terms Count FDR Genes hsa04110 Cell cycle 19 7.86E − 08 CDK6, CCNE2, CCNB2, FZR1, CCNA2, CDC7, YWHAQ, MCM7, CCNE1, CDK4, E2F5, CCNB1, MAD2L1, CDC25B, MCM6, BUB1, RBL1, MCM2, CDK1 hsa03013 RNA transport 20 1.09E − 07 RAN, EIF3H, NUP43, UBE2I, NUP133, MAGOHB, POP5, THOC5, CLNS1A, NUP205, GEMIN6, NUP93, NUP62, SUMO1, EIF2S2, NUP153, RANGAP1, NUP160, RPP25, DDX20 hsa04115 p53 signaling pathway 5 2.90E − 06 CCNE2, CCNB2, CCNE1, CCNB1, CDK1 hsa04914 Progesterone-mediated oocyte maturation 8 1.42E − 05 CCNB2, FZR1, CCNA2, CCNB1, MAD2L1, CDC25B, BUB1, CDK1 hsa03050 Proteasome 9 1.56E − 05 PSMD7, SHFM1, PSMD3, PSMA5, PSMB1, PSMB3, PSMA3, PSMD4, PSMA7 hsa03040 Spliceosome 15 1.66E − 05 SNRPC, SRSF9, XAB2, MAGOHB, NAA38, BUD31, SNRPF, NHP2L1, SRSF3, PQBP1, USP39, SNRNP40, SNRPD1, SNRPD2, SF3B2 hsa03030 DNA replication 8 4.24E − 05 RNASEH2A, RNASEH1, MCM7, POLE2, MCM6, RNASEH2C, MCM2, RFC4 hsa03008 Ribosome biogenesis in eukaryotes 11 4.37E − 05 UTP18, RAN, UTP15, NOP56, DKC1, POP5, FBL, NHP2L1, TCOF1, GNL3L, RPP25 hsa03440 Homologous recombination 7 4.37E − 05 SHFM1, MRE11A, RAD54B, XRCC2, RAD54L, BLM, TOP3A hsa04114 Oocyte meiosis 8 8.96E − 05 CCNE2, CCNB2, YWHAQ, CCNE1, CCNB1, MAD2L1, BUB1, CDK1 hsa05162 Measles 4 0.0001531 CDK6, CCNE2, CCNE1, CDK4 hsa05222 Small cell lung cancer 4 0.0001531 CDK6, CCNE2, CCNE1, CDK4 hsa05200 Pathways in cancer 23 0.0001815 VEGFB, CDK6, MTOR, FH, CCNE2, LEF1, BIRC5, CCNE1, CDK4, TCEB1, MSH6, EGF, FZD2, TFG, CKS1B, TRAF4, HSP90AA1, TRAF3, PPARG, HSP90AB1, FGF12, PIAS4, STK4 hsa00510 N-Glycan biosynthesis 8 0.000312 RFT1, ALG10, RPN2, ALG10B, ALG1, MOGS, ALG5, B4GALT2 EC: esophageal carcinoma; FDR: false discovery rate.Table 6
The KEGG pathway enrichment of downregulated DEGs in EC.
KEGG ID KEGG terms Count FDR Genes hsa04144 Endocytosis 23 5.22E − 06 STAMBP, RAB11FIP5, SH3KBP1, KIT, FOLR2, F2R, TGFBR2, VPS4B, SH3GLB1, CHMP5, CXCR2, PDGFRA, CLTB, FOLR1, STAM2, ARAP2, DAB2, EEA1, PDCD6IP, RAB11FIP2, CBL, EPN3, VPS37B hsa04510 Focal adhesion 19 0.000354 ITGA1, ZYX, LAMB2, MYLK, IGF1, CCND2, ITGA2, RAP1A, PDGFRA, ITGA5, TNXB, VWF, PIK3R1, JUN, COL6A2, BCL2, ROCK1, MYL12A, THBS3 hsa04270 Vascular smooth muscle contraction 14 0.000383 JMJD7-PLA2G4B, MYLK, ADCY9, GNA13, PRKG1, ITPR2, PPP1R12B, GNAQ, MYH11, ACTG2, ROCK1, PLA2G2A, MRVI1, ITPR1 hsa00330 Arginine and proline metabolism 4 0.000425 ALDH7A1, MAOB, GATM, MAOA hsa04360 Axon guidance 15 0.000456 EPHA1, ROBO1, SEMA4B, DPYSL2, ABLIM3, PPP3CC, NCK2, GNAI2, SEMA3F, PPP3CA, RGS3, NTN1, ROCK1, PPP3CB, EFNB2 hsa04020 Calcium signaling pathway 5 0.00046 PPP3CC, ITPR2, PPP3CA, PPP3CB, ITPR1 hsa04662 B cell receptor signaling pathway 4 0.000508 PPP3CC, JUN, PPP3CA, PPP3CB hsa05014 Amyotrophic lateral sclerosis 3 0.000583 PPP3CC, PPP3CA, PPP3CB hsa00340 Histidine metabolism 3 0.000583 ALDH7A1, MAOB, MAOA hsa04720 Long-term potentiation 6 0.000623 PPP3CC, ITPR2, GNAQ, PPP3CA, PPP3CB, ITPR1 hsa04114 Oocyte meiosis 6 0.00068 ADCY9, PPP3CC, ITPR2, PPP3CA, PPP3CB, ITPR1 hsa04730 Long-term depression 10 0.000701 JMJD7-PLA2G4B, IGF1, GNA13, PRKG1, ITPR2, PPP2CB, GNAQ, GNAI2, PLA2G2A, ITPR1 hsa04141 Protein processing in endoplasmic reticulum 16 0.000709 SEC63, UBE2J1, EIF2AK3, ATF6, CRYAB, UBE2D3, DNAJB2, SEC31B, MAN1A1, ERO1L, BCL2, HERPUD1, DNAJC3, UBQLN2, RAD23B, LMAN1 hsa04912 GnRH signaling pathway 12 0.000736 JMJD7-PLA2G4B, MMP2, ADCY9, MAP3K3, HBEGF, ITPR2, MAPK7, GNAQ, MAP3K4, JUN, PLA2G2A, ITPR1 hsa00280 Valine, leucine, and isoleucine degradation 8 0.000738 ALDH7A1, ACADM, HMGCS1, MUT, ABAT, ACADSB, ACAD8, AUH EC: esophageal cancer; FDR: false discovery rate.


## 3.4. PPI Network Construction

Based on data from the BioGRID database, the PPI network was the top 10 upregulated and downregulated DEGs which were constructed by Cytoscape software (Figure1). The network consisted of 451 nodes and 499 edges. In the PPI networks the nodes with high degree are defined as hub proteins. The most significant hub proteins in the PPI network were CDK4 (degree = 132) and CCT3 (degree = 127); as shown in Figure 1, the red circular nodes represent upregulated DEGs and green circular nodes represent downregulated DEGs, respectively.Figure 1
The protein-protein network of top 10 up- and downregulated DEGs in EC. The green circular nodes represent downregulation DEGs in EC; the red circular nodes represent downregulation DEGs in EC. Solid lines indicate interaction between DEGs and proteins.


## 3.5. qRT-PCR Validation of DEGs in EC Tissues

To validate the microarray analysis data, the expression of DEGs including CCT3, CDK4, MYBL2, CENPF, CDKN3, CDCA3, THSD4, and SIM2 was detected by qRT-PCR in 5 paired EC tumor and adjacent nontumor tissues. The 5 patients received surgery treatment in Fourth Hospital of Hebei Medical University. The histological type of 5 subjects was ESCC and the detailed information of subjects was shown in supplementary Table S2. As shown in Figures2(a) and 2(b) the expression level of CCT3 and MYBL2 was significantly upregulated in ESCC. CDK4, CENPF, CDKN3, and CDCA3 had the upregulation tendency in ESCC (Figures 2(c)–2(f)), respectively. SIM2 was significantly downregulated in ESCC (Figure 2(g)). THSD4 had the downregulation tendency in ESCC (Figure 2(h)). The qRT-PCR results were matched with the microarray analysis.Figure 2
The qRT-PCR validation of the expression level of DEGs in EC compared to adjacent nontumor tissues. (a) CCT3; (b) MYBL2; (c) CDK4; (d) CENPF; (e) CDKN3; (f) CDCA3; (g) SIM2; (h) THSD4. EC: esophageal carcinoma; CON: adjacent nontumor tissues of ESCC. At least three independent experiments were performed for statistical evaluation. qRT-PCR experimental data were expressed as means ± SD. The statistical significance was evaluated using Student’st-test and p<0.05 was considered as a significant difference.
(a) (b) (c) (d) (e) (f) (g) (h)


## 4. Discussion

CDK4 was identified as the most significantly upregulated gene in our microarray analysis and it had an upregulated tendency in EC tissues through the qRT-PCR validation. CDK4 was the hub protein and interacted with 132 genes in the regulatory network. CDK4 was significantly enriched in cell cycle, measles, small cell lung cancer, and pathways in cancer. CDK4 encodes cyclin-dependent kinase 4, a member of the Ser/Thr protein kinase family, which plays an important role in cell cycle G1 phase progression and G1/S transition. In our study, CDK1, CDK6, and CDK10 showed upregulation in EC. CDK1, CDK6, and CDK4 were significantly enriched in cell cycle pathway. CDK4 is overexpression in several cancer comprising of breast cancer, pancreas cancer, clear cell renal cell carcinoma, and colorectal cancer [20–23]. Downregulation of MALAT1 (long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1) inhibits breast cancer cell proliferation and cell cycle progressionin vitro andin vivo through miR-124 downregulation and CDK4 upregulation [20, 24]. Overexpression of cyclin D1/CDK4 is regulated by CEACAM6 and promotes cell proliferation in human pancreatic carcinoma [21]. CDK4 and CDK6 expression are decreased by miR-1 and contribute to inhibition of cell cycle progression and metastasis in clear cell renal cell carcinoma [22].CCT3 was the top 3 upregulation DEGs in EC (Table2). The qRT-PCR displayed that CCT3 was significantly upregulated in EC, which was in accordance with our microarray analysis (Figure 2). CCT3 interacted with 127 genes in the PPI network (Figure 1). CCT3 encodes chaperonin containing TCP1 subunit 3, a molecular chaperone, which is a member of the chaperonin containing TCP1 complex (CCT). In our study, CCT2, CCT4, CCT5, and CCT7 were upregulated in EC compared to normal controls, respectively. CCT3 depletion suppresses cell proliferation by inducing mitotic arrest at prometaphase and apoptosis eventually in HCCin vitro. Clinically, overexpression of CCT3 predicts poor prognosis in hepatocellular carcinoma patients after hepatectomy [25, 26]. CCT3 is significantly associated with carboplatin resistance in ovarian cancer patients after surgery treatment [27]. The proteomic-based study shows that patients with cholangiocarcinoma (CCA) which are positive for CCT3 and CCT3 might be potential biomarker for the diagnosis of CCA [28]. To our knowledge, this is the first report about CCT3 expressed status in EC and the biological function of upregulated CCT3 in EC needs further exploration.THSD4 was the most downregulated DGE in EC through microarray analysis. The expression level of THSD4 had no significance in EC compared to normal controls but had the downregulated tendency in EC. THSD4 encodes thrombospondin type 1 domain containing 4. The methylated status of THSD4 shows positive correlation with short survival in glioblastoma patients and hypermethylation of THSD4 indicates poor survival [29]. The expression of THSD4 is regulated by GATA3 and mediates transformation of normal cells into breast cancer through deregulation of THSD4 [30]. The role of downregulated THSD4 in EC is unclear, and the investigation needs to be carried out in the future.SIM2 was significantly downregulated in EC (Figure2). SIM2 encodes single-minded family bHLH transcription factor 2. SIM2-s was dysregulated in glioma, prostate cancer, breast cancer, colorectal cancer, and ESCC [31–35]. SIM2s is downregulated in human breast cancer samples and it suppresses tumor activity through decreased expression of matrix metalloprotease-3. In breast cancer, SIM2s is downregulated. It is a key regulator of mammary-ductal development. SIM2s inhibition is associated with cell invasive and EMT-like phenotype through regulating matrix metalloprotease-3 expression [34, 36] It is reported that SIM2s is downregulated in 70% ESCC tissues, which is consistent with our qRT-PCR verification [35]. SIM2 overexpression results in increase of drug- and radio-sensitivities in ESCCin vivo andin vitro and patients with high expression level of SIM2 are associated with favorable prognosis before chemotherapy [35]. It is suggested that SIM2 plays vital roles in EC onset and progression.MYBL2, CENPF, CDKN3, and CDCA3 were upregulated in EC tissues (Figure2).MYBL2 is frequently amplified in gastroesophageal cancer cell lines and Barrett’s adenocarcinoma [37, 38].CENPF is frequently amplified in region around 1q32-q41 and is overexpressed in ESCC cell line [39]. CDKN3 is upregulated in 68.0% of the epithelial ovarian cancer samples and lung adenocarcinoma patients and is correlated with poor patient survival [40, 41]. CDCA3 expression status in EC was firstly reported in our study. The molecular mechanism of MYBL2, CENPF, CDKN3, and CDCA3 in EC is needed to be explored.


## 5. Conclusions

We identified 1955 DEGs comprising 919 upregulated genes and 1036 downregulated genes in EC. DEGs including CDK4, CCT3, THSD4, and SIM2 were verified in EC tissues through qRT-PCR. CDK4 and CCT3 were hub proteins in the PPI interaction network. We found that some genes including CDK4, CCT3, THSD4, and SIM2 may play essential roles in EC through cell cycle, RNA transport, Endocytosis, and focal adhesion signaling pathways. The genes could also be considered as potential candidate biomarkers for therapeutic targets for this malignancy. Furthermore, our study would shed light on the molecular mechanism underlying tumorigenesis of EC.



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*Source: 2968106-2016-10-12.xml*