Integrated analysis of differentially expressed genes in esophageal squamous cell carcinoma using bioinformatics

Abstract

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.