Esophageal squamous cell carcinoma (ESCC) is a deadly disease. To identify key genes in esophageal squamous cell carcinoma, we followed a strategy utilizing the laiurger microarray dataset (GSE38129) as the training set and another independent microarray dataset (GSE20347) as the validation set. Following quality control, differentially expressed genes (DEGs) were obtained using R software. Functional enrichment analysis was performed using DAVID database and the DEG co-expression network was established with Weighted Gene Co-Expression Network Analysis (WGCNA) and visualized by Cytoscape. The prognosis-related hub genes were then identified by Kaplan-Meier analysis based on the TCGA database. A total of 188 DEGs were obtained; 88 up-regulated genes and 100 down-regulated. The up-regulated DEGs were significantly associated with extracellular matrix organization and disassembly while down-regulated DEGs were significantly related to keratinocyte differentiation. Blue and turquoise co-expression modules were established and 18 hub genes were identified. The blue module was associated with mitotic nuclear division, cell division and mitotic cytokinesis and the turquoise module was associated with collagen catabolic process, extracellular matrix organization and keratinocyte differentiation. We established that the TPX2, CDK1 and CEP55 blue module hub genes were associated with relapse-free survival, and our overall results not only identify key genes but also provide potential novel biomarkers for ESCC diagnosis and treatment.

PurposeEsophageal adenocarcinoma (EAC) is the most common type of esophageal cancer in Western countries. It is usually detected at an advanced stage and has a poor prognosis. The aim of this study was to identify key genes and miRNAs in EAC.MethodsThe mRNA microarray data sets GSE1420, GSE26886, and GSE92396 and miRNA data set GSE16456 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were obtained using R software. Functional enrichment analysis was performed using the DAVID database. A protein–protein interaction (PPI) network and functional modules were established using the STRING database and visualized by Cytoscape. The targets of the DEMs were predicted using the miRecords database, and overlapping genes between DEGs and targets were identified. The prognosis-related overlapping genes were identified using Kaplan–Meier analysis and Cox proportional hazard analysis based on The Cancer Genome Atlas (TCGA) database. The differential expression of these prognosis-related genes was validated using the expression matrix in the TCGA database.ResultsSeven hundred and fifteen DEGs were obtained, consisting of 313 upregulated and 402 downregulated genes. The PPI network consisted of 281 nodes; 683 edges were constructed and 3 functional modules were established. Forty-four overlapping genes and 56 miRNA– mRNA pairs were identified. Five genes, FAM46A, RAB15, SLC20A1, IL1A, and ACSL1, were associated with overall survival or relapse-free survival. FAM46A and IL1A were found to be independent prognostic indicators for overall survival, and FAM46A, RAB15, and SLC20A1 were considered independent prognostic indicators for relapse-free survival. Among them, the overexpression of RAB15 and SLC20A1 and lower expression of ACSL1 were also identified in EAC tissues based on the expression matrix in the TCGA database.ConclusionThese prognosis-related genes and differentially expressed miRNA have provided potential biomarkers for EAC diagnosis and treatment.

Esophageal adenocarcinoma (EAC) is the predominant pathological subtype of esophageal cancer in Europe and the USA. The present bioinformatics study analyzed a high-throughput sequencing dataset, GSE94869, to determine differentially expressed genes (DEGs) in order to identify key genes, biological processes and pathways associated with EAC. Functional enrichment analysis was performed using the Database for Annotation Visualization and Integrated Discovery. The co-expression network of the DEGs was established using Weighted Gene Co-Expression Network Analysis and visualized using Cytoscape. A Kaplan-Meier analysis based on The Cancer Genome Atlas (TCGA) database was used to identify prognosis-associated genes. Univariate and multivariate Cox proportional hazard models were used to identify genes with a prognostic value regarding relapse-free survival (RFS), while validation of the differential expression of prognosis-associated genes was performed using a box plot based on data from TCGA and another microarray dataset, GSE26886. A total of 130 DEGs, comprising 82 upregulated and 48 downregulated genes, were identified. The upregulated DEGs were significantly associated with extracellular matrix organization, disassembly, and the phosphoinositide-3 kinase/AKT, Rap1 and Ras signaling pathways, while the downregulated genes were associated with the Wnt signalling pathway. Subsequently, two co-expression modules were established and 20 hub genes were identified. The blue module was associated with the Rap1 signaling pathway, while the turquoise module was associated with the Ras and Rap1 signaling pathways. Among them, methyltransferase like 7B (METTL7B) was associated with RFS. Furthermore, the overexpression of METTL7B in EAC was successfully validated using data from TCGA and GSE26886. The present study identified key genes and provides potential biomarkers for the diagnosis and treatment of EAC.