Introduction
============

Esophageal squamous cell carcinoma (ESCC) is one of the most life-threatening types of cancer worldwide and the major histological type of esophageal cancer in East Asian countries ([@b1-mmr-20-04-3649]). Approximately 455,800 new cases of esophageal cancer and 400,200 cases of esophageal cancer-related mortality occurred in 2012 worldwide; men with esophageal cancer have a three- to four-fold higher mortality rate than women ([@b1-mmr-20-04-3649]). ESCC is an invasive tumor with a poor prognosis and is generally diagnosed only following the onset of symptoms. Although ESCC treatment has improved, the 5-year overall survival (OS) rate of patients with ESCC remains low due to insufficient understanding of its molecular pathogenesis and infrequent early-stage examination ([@b2-mmr-20-04-3649]). Therefore, novel insights into the diagnosis and prognosis of ESCC can be obtained by increasing the level of understanding of its pathogenesis.

Similar to other types of cancer, the development of ESCC involves the gradual accumulation of vital gene mutations involved in cell cycle control, cell growth, differentiation, apoptosis, migration and invasion, or other functions, including the inactivation of tumor suppressor genes and activation of oncogenes ([@b3-mmr-20-04-3649]). Zhang *et al* ([@b4-mmr-20-04-3649]) found that ROC1 is expressed at a high level in ESCC and is associated with poor prognosis. Targeting the overexpressed ROC1 induces G2 cell cycle arrest and apoptosis in esophageal cancer cells. Hers *et al* ([@b5-mmr-20-04-3649]) found that increasing the transduction of the Akt signaling pathway serves an important role in several types of cancer, including breast cancer ([@b6-mmr-20-04-3649]), prostate cancer ([@b7-mmr-20-04-3649]) and gastric cancer ([@b5-mmr-20-04-3649],[@b8-mmr-20-04-3649]). P53 is one of the most commonly mutated genes in human cancer, the overexpression of epidermal growth factor receptor and P53 mutation induces tumor development, invasion and differentiation ([@b9-mmr-20-04-3649]). Although certain genes or proteins are involved in the development of ESCC, the pathogenic mechanisms remain unclear. Therefore, determining the pathogenesis of esophageal cancer-related signaling pathways and predicting the prognosis of esophageal cancer are crucial.

The present study aimed to identify the hub genes ([Table I](#tI-mmr-20-04-3649){ref-type="table"}) related to the occurrence and development of esophageal cancer through bioinformatics analysis, and then examine the signaling pathways involved in these hub genes and their relationship with the prognosis of esophageal cancer. The present study aims to further improve current understanding of the occurrence and development of esophageal cancer.

Materials and methods
=====================

### Microarray data

The GSE38129 gene expression dataset was submitted by Hu *et al* ([@b10-mmr-20-04-3649]) and can be obtained from the publicly accessible Gene Expression Omnibus (GEO) database. The dataset was downloaded and analyzed from the GEO at the National Center for Biotechnology Information website (<https://www.ncbi.nlm.nih.gov/geo/>). The study was based on the GPL571 platform (Affymetrix Human Genome U133A 2.0 Array, Affymetrix; Thermo Fisher Scientific, Inc.). The samples used for gene profile analysis were obtained from 30 patients with ESCC and paired adjacent normal tissues, the patients were from high-risk areas of China, and the most recent update was in April 2017.

### Data processing of differentially expressed genes (DEGs)

GEO2R online software was used for GSE38129 analysis to detect the DEGs between the tumor and normal tissues. GEO2R is an interactive networking tool that helps users to compare various groups of samples in the GEO series and identify DEGs under specific experimental conditions. The adjusted P\<0.01 and \|log fold change (FC)\|\>1 values were used as the cut-off criteria for DEG identification. Subsequently, 928 DEGs were identified following GSE38129 analysis. Among these DEGs, 498 and 430 were upregulated and downregulated, respectively.

### Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

GO analysis (<https://david.ncifcrf.gov/>) ([@b11-mmr-20-04-3649]), which is a bioinformatics tool that can be used to annotate genes and gene products and determine the biological characteristics of high-throughput genome or transcriptome data, includes three categories, namely, biological process (BP), cellular component (CC) and molecular function (MF). The KEGG knowledge database ([@b12-mmr-20-04-3649]) is a group of databases used for all types of biological data and can be used to determine functional and metabolic pathways. P\<0.05 was set as the cut-off criterion and considered to indicate a statistically significant difference ([Fig. S1](#SD1-mmr-20-04-3649){ref-type="supplementary-material"}). The Database for Annotation, Visualization and Integrated Discovery (DAVID) ([@b13-mmr-20-04-3649]) is a web-based online bioinformatics resource and a functional interpretation tool with a large scale gene or protein dataset that can provide comprehensive functional annotation for genes.

### Protein-protein interaction (PPI) network construction and module analysis

The Search Tool for the Retrieval of Interacting Genes (STRING; <http://string-db.org/>) ([@b14-mmr-20-04-3649]) database is an online tool that contains comprehensive information of various proteins and detects potential associations among the DEGs. The results were input into Cytoscape to visualize the PPI networks of the DEGs. A high combined score indicated reliable PPIs. In the present study, interactions with a combined confidence of \>0.7 were considered significant. The PPI network was constructed using Cytoscape software. The Molecular Complex Detection plug-in of Cytoscape ([@b15-mmr-20-04-3649]) further indicated the essential modules in the PPI networks (degree node score cut-off=0.2, K-Core=2, degree cut-off=2).

### Survival analysis

Gene Expression Profiling Interactive Analysis (GEPIA) ([@b16-mmr-20-04-3649]) is a web-based server for cancer and normal gene expression analyses and interactive analysis on the basis of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data. Multiple types of analyses can be performed, including differential expression analysis, profiling plotting, correlation analysis and patient survival analysis. Through GEPIA analysis, ephrin-A1 (EFNA1) and collagen type IV α1 (COL4A1) were expressed at high levels in ESCC and were associated with a poor prognosis. The low expression of C-X-C chemokine receptor 2 (CXCR2) was not statistically significant.

### Patients and samples

A total of 36 ESCC tissue samples and 35 normal esophageal tissue samples were collected for the present study, which had been surgically removed from Kazakh patients at the First Affiliated Hospital of Shihezi University (Xinjiang, China) between June 2018 to March 2019. The research protocol was approved by the Medical Ethics and Human Clinical Trial Committee of Shihezi University School of Medicine (Xinjiang, China) and all recruited subjects were enrolled following the provision of written informed consent. All surgical samples were used as residual specimens following diagnostic sampling.

### Immunohistochemistry (IHC)

A total of 36 esophageal cancer tissue samples and 35 normal samples from Kazakh patients were selected from formalin-fixed and paraffin-embedded tissue chips. The sample tissue chips, with a diameter of 0.6 mm, were obtained using ALPHELYS. The tissue microarrays were heated in an oven at 65°C for 30 min, rehydrated with graded alcohols, immersed in ethylenediaminetetraacetic acid buffer (pH 9.0) at 130°C, and autoclaved in a microwave oven for 10 min. Following cooling to 30°C, the tissues were incubated at room temperature with 3% H~2~O~2~ solution for 10 min. The tissue sections were then incubated at 4°C with anti-securin antibody \[also termed anti-pituitary tumor transforming gene 1 (PTTG1) antibody, Bioss antibodies, rabbit polyclonal, cat. no. bs-1881R, dilution 1:400\] overnight. The tissue sections were organized and washed in PBS three times for 5 min each and then incubated with secondary antibody \[universal kit (mouse/rabbit polymer method detection system), cat. no. PV6000, ZSGB, Ready-to-use antibody\] at 37°C for 30 min. Diaminobenzidine (DAB) solution was used for 5 min at room temperature and hematoxylin was used to counterstain the sections. The IHC score was performed independently by two pathologists using a light microscope (BX51; Olympus, Tokyo, Japan; magnification, ×400) according to the color intensity as either negative (score 0), weak (score 1), moderate (score 2) or strong (score 3), and coloring area as negative (score 0), ≤10-25% (score 2), 25--50% (score 3) or \>50% (score 4). The final score was determined as the coloring intensity multiplied by the coloring area. Scores 0--4 and 5--12 indicated low and high expression groups, respectively.

### Statistical analysis

Data were assessed using the SPSS (version 17.0; SPSS, Inc.) statistical software package, and GraphPad Prism 5.0 (GraphPad Software, Inc.) was used to describe data. Comparisons of the expression levels of proteins between the ESCC (n=36) and normal tissues (n=35) were performed using the independent-samples t-test and χ^2^ test. All data are presented as the mean ± SD.

Results
=======

### DEG identification

The DEGs were detected using the GEO2R online analytical tool with adjusted P\<0.01 and \|logFC\|\>1 as cut-off criteria. A total of 928 DEGs were obtained between the ESCC and normal samples, including 498 upregulated and 430 downregulated genes. Eight core genes were selected on the basis of the degree of connectivity and adjusted P-value ([Table I](#tI-mmr-20-04-3649){ref-type="table"}), including checkpoint kinase 1 (CHEK1), BUB1B, PTTG1, COL4A1, CXCR2, adrenoreceptor β2 (ADRB2), acyl-CoA oxidase 2 (ACOX2) and EFNA1. The top 50 DEGs are shown in [Table II](#tII-mmr-20-04-3649){ref-type="table"} (19 upregulated and 31 downregulated genes). Additionally, by setting \|logFC\|\>2.5 and adjusted P\<0.01, 52 DEGs were selected, of which 30 and 22 were upregulated and downregulated, respectively. The heat maps and volcano plots show the different DEG samples ([Figs. 1](#f1-mmr-20-04-3649){ref-type="fig"} and [2](#f2-mmr-20-04-3649){ref-type="fig"}). These volcano plots and heat maps indicate all genes, and the top 52 DEGs, respectively.

### GO and KEGG pathway analyses of DEGs

To appreciate the functions of the DEGs further, DAVID (<https://david.ncifcrf.gov/>) was used to apply the GO function and KEGG pathway for enrichment analysis. The BPs, CCs and MFs of the DEGs were annotated and classified by GO analysis. The present study identified 39 GO terms on the basis of the DEGs of modules with a false discovery rate (FDR) \<0.05 and count of \>2 as thresholds and these terms were then sorted by the P-value. The top five enriched GO terms for the BPs, CCs and MFs were selected from the GO terms ([Fig. 3](#f3-mmr-20-04-3649){ref-type="fig"} and [Table III](#tIII-mmr-20-04-3649){ref-type="table"}). The signaling pathways were obtained through the KEGG database, and the major signaling pathways included 'cell cycle', 'ECM-receptor interaction', 'p53 signaling pathway', 'protein digestion and uptake', 'small cell lung cancer' and 'proteoglycans in cancer' ([Table IV](#tIV-mmr-20-04-3649){ref-type="table"}).

### PPI network construction and module analysis

The STRING database was used to predict the interaction between 928 DEGs (minimum required interaction score of \>0.7). To select important modules in the PPI network, the MCODE plug-in was used and 25 modules were found. The top five modules were also selected for further analysis, which included 60, 15, 13, 32 and 7 genes ([Table V](#tV-mmr-20-04-3649){ref-type="table"}). The DEGs in the top five modules were also enriched in important pathways ([Fig. 4A-J](#f4-mmr-20-04-3649){ref-type="fig"}). Module A had 60 nodes and 1,643 interactions, and all of the DEGs were upregulated in this module. The genes in this module, including CHEK1, cyclin A2 (CCNA2) and TTK, were considerably enriched in the cell cycle and p53 signaling pathway-related functions ([Fig. 4B](#f4-mmr-20-04-3649){ref-type="fig"}).

### Survival analysis

Gene expression and survival analyses were performed by GEPIA in the TCGA database. The resulting box plots ([Fig. 5A-H](#f5-mmr-20-04-3649){ref-type="fig"}) showed that EFNA1 and COL4A1 were expressed at a high level in ESCC ([Fig. 5B and C](#f5-mmr-20-04-3649){ref-type="fig"}), whereas the expression of CXCR2 was low in ESCC ([Fig. 5E](#f5-mmr-20-04-3649){ref-type="fig"}). Survival analysis ([Fig. 6A-H](#f6-mmr-20-04-3649){ref-type="fig"}) further showed that EFNA1 and COL4A1 were associated with poor prognosis and exhibited statistically significant differences ([Fig. 6B and C](#f6-mmr-20-04-3649){ref-type="fig"}).

### IHC features

According to the degree of connectivity and adjusted P-value, eight core genes were selected ([Table I](#tI-mmr-20-04-3649){ref-type="table"}), among which the most strongly correlated genes were CHEK1 (degree of connectivity=88, adjusted P=1.64E-08), BUB1B (degree of connectivity=84, adjusted P=2.10E-08), PTTG1 (degree of connectivity=64, adjusted P=1.62E-04), COL4A1 (degree of connectivity=16, adjusted P=2.26E-04), and CXCR2 (degree of connectivity=15, adjusted P=1.15E-08). No significant prognostic significance was found for CHEK1 or BUB1B ([Fig. 6G and H](#f6-mmr-20-04-3649){ref-type="fig"}). Relevant references were also reviewed and it was found that PTTG1 is an oncogene that is overexpressed in several tumors. The high expression of PTTG1 also exhibited a relatively poor prognosis through GEPIA survival analysis ([Fig. 6A](#f6-mmr-20-04-3649){ref-type="fig"}). Therefore, PTTG1 was selected for IHC analysis. IHC was used to detect the expression of PTTG1 in 36 ESCC tissue samples and 35 normal tissue samples of the Kazakh patients. The results showed that the expression of PTTG1 ([Fig. 7A and B](#f7-mmr-20-04-3649){ref-type="fig"}) in esophageal cancer tissues was significantly higher than that in normal tissues, and the difference was statistically significant (P=0.002). In addition, the PTTG1 IHC staining scores in the ESCC and normal tissues were compared using independent-samples t-test analysis, and the difference was statistically significant (P\<0.001; [Fig. 7C](#f7-mmr-20-04-3649){ref-type="fig"}).

Discussion
==========

ESCC is a digestive tract tumor, is the fourth highest cause of cancer-associated mortality and is one of the most aggressive malignancies in China ([@b17-mmr-20-04-3649]). In the present study, the online GEO2R tool was used between ESCC and normal samples to detect 928 DEGs, including 498 upregulated and 430 downregulated genes. Using several bioinformatics tools, the DEGs were found to be mainly related to cell cycle, DNA replication and ECM-receptor interactions. A PPI network of the DEGs was also constructed, and the first five modules were selected for further analysis. All the genes enriched in module 1, including BUB1B, CCNA2, CHEK1, BUB1, CCNB1 and CCNB2, were upregulated. These genes were mainly related to cell cycle, progesterone-mediated oocyte, and the p53 signaling pathway. The COL family of genes was mainly enriched in module 2, including COL11A1, COL1A1, COL1A2, COL5A1, COL5A2 and COL6A3, which was involved in the ECM-receptor interaction and PI3K-Akt signaling pathways. EFNA1 and COL4A1 were also associated with the prognosis of patients with ESCC.

EFNA1 is an angiogenic factor. EFNA1 was originally separated from human umbilical vein endothelial cells as a secretory protein and treated with tumor necrosis factor-α. Tumor necrosis factor-α-induced ([@b18-mmr-20-04-3649]) EFNA1 and its receptor, Eph receptor 2, are associated with various types of cancer, including bladder cancer ([@b19-mmr-20-04-3649]) and gastric cancer ([@b20-mmr-20-04-3649]). High expression of EFNA1 is also involved in colorectal cancer ([@b21-mmr-20-04-3649]) and its low expression is associated with a poor prognosis in clear cell renal cell carcinoma ([@b22-mmr-20-04-3649]). High expression of COL4A1 is associated with advanced tumors and poor OS and disease-free survival in patients with HCC ([@b23-mmr-20-04-3649]). COL4A1 knockdown decreases cell viability and cell cycle in breast cancer cells ([@b24-mmr-20-04-3649]). Therefore, EFNA1 and COL4A1 may be associated with the prognosis of esophageal cancer. The GEPIA database in the TCGA was used in the present study for the survival analysis and it was found that EFNA1 and COL4A1 were associated with a poor prognosis in ESCC.

The cell cycle is a process in which a cell completely divides, including interphase and division phases. The mechanism of cell cycle disorder in any condition causes the development of cancer, as cancer is closely associated with cell proliferation and growth ([@b25-mmr-20-04-3649]). An important hallmark of cancer is uncontrolled cell proliferation. Tumor cells generally exhibit damage to genes that directly regulate cell cycle ([@b26-mmr-20-04-3649]). In the present study, several DEGs were enriched in the cell cycle. COL1A1 and COL1A2 encode the α1 and α2 chains of type I collagen, respectively ([@b27-mmr-20-04-3649]). The cell adhesion molecule COL1A1 is expressed at a high level in ESCC, which is essential for ESCC carcinogenesis ([@b28-mmr-20-04-3649]). CHEK1 is also type of protein-coding gene. The protein encoded by CHEK1 belongs to the Ser/Thr protein kinase family. Checkpoints that mediate cell cycle arrest require the presence of DNA damage or unreplicated DNA. The high cytoplasmic expression of phosphorylated CHEK1 was associated with the poor prognosis of breast cancer ([@b29-mmr-20-04-3649]) and also exhibited high expression in ovarian and oral squamous cell carcinoma ([@b30-mmr-20-04-3649],[@b31-mmr-20-04-3649]). Therefore, the targeted regulation of CHEK1 may become a novel method for cancer treatment.

PTTG1 is an oncogene that is overexpressed in several tumor types. The expression of PTTG1 is high in bladder cancer. PTTG1 knockdown significantly inhibits bladder cancer cell migration, invasion, metastasis and growth, and induces G0/G1 phase senescence and cell cycle arrest ([@b32-mmr-20-04-3649]). Feng *et al* ([@b33-mmr-20-04-3649]) reported that PTTG1, via activating the expression of GLI1 in ESCC, was involved in the epithelial-mesenchymal transformation (EMT) process, and promoted the metastasis in ESCC cell lines and tissues by inducing EMT. Particularly in cells with lymph node metastasis. TTK, also referred to as Mps1, is overexpressed in human pancreatic cancer and primary liver cancer ([@b34-mmr-20-04-3649],[@b35-mmr-20-04-3649]). BUB1B, which is a mitotic checkpoint serine/threonine kinase B, is a member of the spindle assembly checkpoint protein family and is involved in various types of cancer. The expression of BUB1B is high in prostate cancer and associated with poor prognosis ([@b36-mmr-20-04-3649]). BUB1B is also expressed at a high level in lung adenocarcinoma, and the overexpression of BUB1B is associated with poor disease progression and poor survival rates in patients with lung adenocarcinoma ([@b37-mmr-20-04-3649]). Certain transcription factors can regulate ESCC cancer cell cycle by regulating BUB1B, which is a cell cycle-related DEG, thereby promoting the development of ESCC ([@b38-mmr-20-04-3649]). Therefore, BUB1B may promote the development of ESCC by deregulating the cell cycle.

The present study identified DEGs through bioinformatics analysis, some of which may serve an important role in the development, progression and prognosis of ESCC. CHEK1 and BUB1B are primarily related to the cell cycle, and COL5A1, COL11A1 and COL1A1 are related to the main ECM-receptor interaction pathway. Through KEGG analysis, these differentially expressed genes were mainly related to cell cycle and ECM receptors. CHEK1, BUB1B, COL5A1, PTTG1, TTK and COL1A1 have also been associated with the development of various types of cancer. EFNA1 and COL4A1 were associated with the prognosis of ESCC. The IHC results showed that the expression of PTTG1 in ESCC tissues was significantly higher than that in normal esophageal tissues, with statistical significance. However, the present study used mostly consultation cases from the People\'s Hospital of Xinjiang Uyghur Autonomous Region, and the Xinjiang Yili Prefecture Friendship Hospital; ESCC fresh samples are difficult to obtain due to the lack of patients in this region, therefore, it is difficult to collect proteins for further analysis. In future research, when additional fresh tissue samples are collected, reverse transcription-PCR and western blot analyses will be performed for the validation of these identified target genes in clinical samples. In conclusion, the genes identified may serve an important role in the occurrence and prognosis of ESCC. However, their mechanism in ESCC requires further investigation.

Supplementary Material
======================

###### Supporting Data

Not applicable.

Funding
=======

This study was supported by grants from the National Natural Science Foundation of China (grant nos. 81460362, 81773116, 81760436, 81560399 and 81360358), the Medical And Health Science And Technology Project Of Suzhou High Tech Zone (grant no. 2017Z006), the Applied Basic Research Projects of Xinjiang Production and Construction Corps (grant no. 2016AG020), the Major Science And Technology Projects of Shihezi University (grant no. gxjs2014-zdgg06), the High-Level Talent Project of Shihezi University (grant no. RCZX201533) and the Foundation for Distinguished Young Scholars of Shihezi University (grant no. 2015ZRKXJQ02). The funders were not involved in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials
==================================

The datasets analyzed in the present study are available from the GEO repository (<https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38129>).

Authors\' contributions
=======================

XBC and FFC conceived and designed the study. YZC, HP and SRZ collected the expression data and screened for the differentially expressed genes. FFC and HP analyzed and interpreted the data. FFC wrote the manuscript. XBC and YZC reviewed and edited the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate
==========================================

The present study was approved by the Medical Ethics and Human Clinical Trial Committee of Shihezi University School of Medicine, and all recruited subjects were enrolled following the provision of written informed consent.

Patient consent for publication
===============================

Not applicable.

Competing interests
===================

The authors declare that they have no competing interests.

![Heatmap of DEGs in ESCC and normal esophageal samples. The heatmap shows the DEGs between the ESCC and normal samples. When \|logFC\| was set to \>2.5 and the P-value was adjusted to \<0.01, 52 differential genes, including 30 upregulated and 22 downregulated genes, were identified. The clustering of the samples is shown above the dendrogram and the level of gene expression signal us indicated by the color (red and green for high and low expression levels, respectively). ESCC, esophageal squamous cell carcinoma; DEGs, differentially expressed genes.](MMR-20-04-3649-g00){#f1-mmr-20-04-3649}

![Volcano plot of all genes. A volcano plot of the differentially expressed genes between esophageal squamous cell carcinoma and normal samples is shown. The red dots on the left indicate upregulated genes, the red dots on the left and right and black dots indicate upregulated, downregulated and unregulated genes, respectively. FC, fold-change.](MMR-20-04-3649-g01){#f2-mmr-20-04-3649}

![GO term enrichment analysis. (A) Top five clusters of DEGs in BPs (red). (B) Top five clusters of DEGs in CCs (blue). (C) Top five clusters of DEGs in MFs (orange). These groups are ranked and presented as bar plots according to their Fisher\'s exact P-value. DEGs, differentially expressed genes; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.](MMR-20-04-3649-g02){#f3-mmr-20-04-3649}

![Top five modules from the protein-protein interaction network. (A) Module 1, (B) enriched pathways of module 1; (C) module 2, (D) enriched pathways of module 2; (E) module 3, (F) enriched pathways of module 3; (G) module 4, (H) enriched pathways of module 4; (I) module 5, (J) enriched pathways of module 5. Green and red nodes represent downregulated and upregulated genes, respectively.](MMR-20-04-3649-g03){#f4-mmr-20-04-3649}

![Box plots of ESCC and normal tissue expression levels in The Cancer Genome Atlas database. Box plots indicate the expression of different genes in tumor and normal tissues. The expression levels of (A) PTTG1 and (B) EFNA1 in ESCC tissues were higher than those in normal tissues, although there was no statistical significance. (C) COL4A1, (D) ACOX2, (E) CXCR2, (F) ADRB2, (G) CHEK1 and (H) BUB1B exhibited statistically significant differences. \*P\<0.05 between ESCC and normal tissues. ESCC tissues are shown in red, normal tissues are shown in black. Y-axis: \|log2FC\| cut-off. X-axis: num (T)=182, num (N)=286. ESCC, esophageal squamous cell carcinoma; FC, fold-change; T, tumor; N, normal tissues.](MMR-20-04-3649-g04){#f5-mmr-20-04-3649}

![Prognostic value of eight hub genes in GSE38129. Relationships between gene expression and OS in 182 patient prognostic values in the GSE38129 of (A) PTTG1, (B) EFNA1, (C) COL4A1, (D) ACOX2, (E) CXCR2, (F) ADRB2, (G) CHEK1 and (H) BUB1B were obtained from Gene Expression Profiling Interactive Analysis. The results showed that the patients with high expression levels of EFNA1 (P=0.0026) and COL4A1 (P=0.037) had a poorer prognosis than those with low expression levels of EFNA1 and COL4A1. \*P\<0.05. OS, overall survival; HR, hazard ratio; TPM, number of transcripts per million reads.](MMR-20-04-3649-g05){#f6-mmr-20-04-3649}

![Protein expression of PTTG1 in ESCC and normal tissues. (A) Expression of PTTG1 in ESCC, cytoplasmic staining (magnification, ×100, ×200, ×400). (B) Expression of PTTG1 in normal tissues, cytoplasmic staining (magnification, ×100, ×200, ×400). (C) Expression of PTTG1 in ESCC tissues (n=36) was significantly higher than that in normal tissues (n=35), and the difference was statistically significant (P=0.002). (D) PTTG1 IHC staining score in ESCC and normal tissues using independent-samples t-test analysis; each bar represents the mean ± SD. \*\*\*P\<0.001. ESCC, esophageal squamous cell carcinoma; IHC, immunohistochemistry.](MMR-20-04-3649-g06){#f7-mmr-20-04-3649}

###### 

Eight hub genes with a high degree of connectivity.

  Gene     Degree of connectivity   adj.P.Val
  -------- ------------------------ -----------
  CHEK1    88                       1.64E-08
  BUB1B    84                       2.10E-08
  PTTG1    64                       1.62E-04
  COL4A1   16                       2.26E-04
  CXCR2    15                       1.15E-08
  ADRB2    12                       2.15E-05
  ACOX2      5                      5.82E-06
  EFNA1      4                      1.27E-06

adj.P.Val, adjusted P-value.

###### 

Top 50 DEGs, including 19 upregulated and 31 downregulated genes.

  ID              adj.P.Val   P-value    logFC         Gene
  --------------- ----------- ---------- ------------- -----------------
  Downregulated                                        
  207802_at       2.95E-08    5.42E-10   −5.38992228   CRISP3
  220090_at       6.96E-06    4.36E-07   −4.46587146   CRNN
  204777_s\_at    1.29E-07    3.34E-09   −4.3672882    MAL
  220620_at       8.69E-06    5.70E-07   −3.9442306    CRCT1
  206004_at       4.68E-06    2.68E-07   −3.94095422   TGM3
  209613_s\_at    1.56E-08    2.40E-10   −3.67537874   ADH1B
  220026_at       1.61E-04    1.80E-05   −3.65426494   CLCA4
  214536_at       1.05E-06    4.61E-08   −3.56033721   SLURP1
  206884_s\_at    6.42E-05    6.13E-06   −3.51463756   SCEL
  219529_at       3.15E-06    1.67E-07   −3.47635301   CLIC3
  203914_x\_at    1.03E-09    7.43E-12   −3.44800172   HPGD
  206605_at       1.49E-08    2.28E-10   −3.43754016   ENDOU
  215704_at       7.39E-07    2.90E-08   −3.37242552   FLG
  213240_s\_at    3.44E-04    4.35E-05   −3.31829107   KRT4
  205185_at       1.44E-04    1.59E-05   −3.20158834   SPINK5
  204284_at       2.66E-10    1.34E-12   −3.14636945   PPP1R3C
  210096_at       1.07E-07    2.68E-09   −3.13376609   CYP4B1
  220431_at       1.03E-04    1.08E-05   −3.13073801   TMPRSS11E
  207008_at       1.15E-08    1.61E-10   −3.11373209   CXCR2
  205783_at       4.37E-05    3.91E-06   −3.08906259   KLK13
  206199_at       9.59E-06    6.39E-07   −3.06841828   CEACAM7
  201348_at       1.59E-11    2.07E-14   −3.06249067   GPX3
  205767_at       5.58E-08    1.22E-09   −2.96580978   EREG
  212681_at       1.24E-11    1.40E-14   −2.9062456    EPB41L3
  201325_s\_at    9.91E-08    2.46E-09   −2.78110281   EMP1
  209365_s\_at    4.24E-07    1.47E-08   −2.72959968   ECM1
  211726_s\_at    7.83E-06    5.04E-07   −2.71946544   FMO2
  205319_at       1.03E-06    4.47E-08   −2.67917377   PSCA
  211026_s\_at    4.92E-12    2.43E-15   −2.61375129   MGLL
  207980_s\_at    1.36E-10    5.35E-13   −2.57860425   CITED2
  204614_at       1.34E-03    2.18E-04   −2.55597414   SERPINB2
  Upregulated                                          
  204620_s\_at    4.60E-08    9.68E-10   2.57111875    VCAN
  212353_at       8.15E-08    1.92E-09   2.57383841    SULF1
  204779_s\_at    2.10E-12    7.56E-16   2.57925018    HOXB7
  214612_x\_at    1.06E-03    1.66E-04   2.61538749    MAGEA6
  209942_x\_at    8.45E-04    1.27E-04   2.63065096    MAGEA6///MAGEA3
  202859_x\_at    1.18E-06    5.33E-08   2.6515207     CXCL8
  205680_at       3.82E-06    2.11E-07   2.72388472    MMP10
  206632_s\_at    1.86E-08    3.05E-10   2.77430412    APOBEC3B
  204475_at       1.12E-09    8.31E-12   4.8371858     MMP1
  202404_s\_at    1.57E-07    4.29E-09   2.84748214    COL1A2
  202310_s\_at    3.58E-08    6.90E-10   3.00373934    COL1A1
  206224_at       1.58E-08    2.43E-10   3.04664986    CST1
  206291_at       6.57E-04    9.38E-05   3.19271902    NTS
  210809_s\_at    4.97E-07    1.78E-08   3.22856265    POSTN
  205157_s\_at    3.12E-06    1.65E-07   3.32873111    KRT17///JUP
  217428_s\_at    1.24E-11    1.47E-14   3.34429678    COL10A1
  204580_at       2.92E-09    2.93E-11   3.47409534    MMP12
  37892_at        4.08E-11    8.94E-14   4.18462587    COL11A1
  209875_s\_at    3.55E-10    1.94E-12   4.46479482    SPP1

FC, fold change; adj.P.Val, adjusted P-value.

###### 

Top five BPs, CCs and MFs in the analysis of differentially expressed genes between ECSS and normal tissues.

  Term                                                     Count   P-value    FDR        Functional group
  -------------------------------------------------------- ------- ---------- ---------- ------------------
  GO:0051301 cell division                                 59      4.21E-16   8.10E-13   BP
  GO:000008 G1/S transition of mitotic cell cycle          30      7.47E-15   1.36E-11   BP
  GO:0030198 Extracellular matrix organization             41      1.50E-14   2.75E-11   BP
  GO:0006260 DNA replication                               35      1.60E-13   2.93E-10   BP
  GO:0030574 collagen catabolic process                    22      1.79E-12   3.28E-09   BP
  GO:0070062 extracellular exosome                         229     1.55E-17   2.27E-14   CC
  GO:0005615 extracellular space                           127     1.20E-13   1.76E-10   CC
  GO:0031012 extracellular matrix                          47      1.32E-12   1.93E-09   CC
  GO:0005737 cytoplasm                                     333     3.71E-10   5.43E-07   CC
  GO:0005654 nucleoplasm                                   199     1.59E-09   2.32E-06   CC
  GO:0005515 protein binding                                 515   2.55E-09   4.07E-06   MF
  GO:0005201 extracellular matrix structural constituent   17      1.07E-07   1.71E-04   MF
  GO:0043142 single-stranded DNA-dependent                 7       2.46E-06   0.003918   MF
  ATPase activity                                                                        
  GO:0005518 collagen binding                              14      5.39E-06   0.008585   MF
  GO:0003678 DNA helicase activity                         9       1.20E-05   0.019143   MF

GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; FDR, false discovery rate; Count, enriched gene number in the category.

###### 

KEGG pathway analysis of differentially expressed genes.

  Category    Term                                      Count   P-value       FDR
  ----------- ----------------------------------------- ------- ------------- -------------
  hsa04110    Cell cycle                                29      3.86E-10      5.05E-07
  hsa03030    DNA replication                           16      4.92E-10      6.43E-07
  hsa04512:   ECM-receptor interaction                  23      3.24E-09      4.24E-06
  hsa05146    Amoebiasis                                19      3.94E-05      0.051506178
  hsa04974    Protein digestion and absorption          17      4.48E-05      0.058547669
  hsa04510    Focal adhesion                            28      6.69E-05      0.087469624
  hsa04115    p53 signaling pathway                     14      1.16E-04      0.151932901
  hsa05222    Small cell lung cancer                    14      0.001286708   1.670849429
  hsa03430    Mismatch repair                           7       0.001721063   2.229032091
  hsa05200    Pathways in cancer                        38      0.002816977   3.624393493
  hsa05202    Transcriptional misregulation in cancer   19      0.009704623   11.98140955
  hsa04114    Oocyte meiosis                            14      0.011336843   13.861154
  hsa04151    PI3K-Akt signaling pathway                31      0.019748427   22.97410608
  hsa04972    Pancreatic secretion                      12      0.020140081   23.37588312
  hsa05144    Malaria                                   8       0.023512051   26.75579741
  hsa05205    Proteoglycans in cancer                   20      0.026031699   29.1909003
  hsa00590    Arachidonic acid metabolism               9       0.027995297   31.03652764
  hsa04914    Progesterone-mediated oocyte maturation   11      0.031059516   33.82807896
  hsa05219    Bladder cancer                            7       0.03140939    34.14009186
  hsa00410    beta-Alanine metabolism                   6       0.033090282   35.62023199
  hsa03320    PPAR signaling pathway                    9       0.041960173   42.93477387
  hsa00071    Fatty acid degradation                    7       0.042590557   43.42421223

KEGG, Kyoto Encyclopedia of Gene and Genome; FDR, false discovery rate; Count, enriched gene number in the category.

###### 

Modules of networks.

  Module name   Nodes   Edges   Cluster scores
  ------------- ------- ------- ----------------
  A             60      1643    55.695
  B             15      102     14.571
  C             13        78    13.000
  D             32      144     9.290
  E               7       20    6.667

[^1]: Contributed equally
