Bioinformatics Characterization of Candidate Genes Associated with Gene Network and miRNA Regulation in Esophageal Squamous Cell Carcinoma Patients

Abstract

Featured Application: Esophageal squamous cell carcinoma (ESCC) is the most common and frequent epithelial malignancy of the esophageal diagnosed among esophageal carcinoma (ESCA) worldwide. Despite enormous studies, the effect of gene overexpression and the overall survival rate associated with a genes mutation is still poorly understood. A biomarker to predict survival is a critical need in ESCC treatment. We hypothesized that a top ten hub gene (HGsT10) could be used as a predictive biomarker for the treatment response in metastatic ESCC. We found that HGsT10 expressed highly at the tumor state, reflecting the tumor development and poor survival rate in ESCC. Abstract: The present study aimed to identify potential therapeutic targets for esophageal squamous cell carcinoma (ESCC). The gene expression profile GSE161533 contained 84 samples, in that 28 tumor tissues and 28 normal tissues encoded as ESCC patients were retrieved from the Gene Expression Omnibus database. The obtained data were validated and screened for differentially expressed genes (DEGs) between normal and tumor tissues with the GEO2R tool. Next, the protein–protein network (PPI) was constructed using the (STRING 2.0) and reconstructed with Cytoscape 3.8.2, and the top ten hub genes (HGsT10) were predicted using the Maximal Clique Centrality (MCC) algorithm of the CytoHubba plugin. The identified hub genes were mapped in GSE161533, and their expression was determined and compared with The Cancer Genome Atlas (TCGA.) ESCC patient’s samples. The overall survival rate for HGsT10 wild and mutated types was analyzed with the Gene Expression Profiling Interactive Analysis2 (GEPIA2) server and UCSC Xena database. The functional and pathway enrichment analysis was performed using the WebGestalt database with the reference gene from lumina human ref 8.v3.0 version. The promoter methylation for the HGsT10 was identified using the UALCAN server. Additionally, the miRNA-HGsT10 regulatory network was constructed to identify the top ten hub miRNAs (miRT10). Finally, we identified the top ten novel driving genes from the DEGs of GSE161533 ESCC patient’s sample using a multi-omics approach. It may provide new insights into the diagnosis and treatment for the ESCC affected patients early in the future.