Identification of differentially expressed genes and signaling pathways in Esophageal squamous cell carcinoma using bioinformatics analysis

Abstract Background: Esophageal squamous cell carcinoma (ESCC) is one of the histological types of esophageal cancers, with more than 80% of esophageal cancers being ESCC. Meanwhile, in Asia, ESCC has higher morbidity and mortality compared with western countries. Due to lack of effective molecular targets and treatments options, the prognosis and 5-year survival rate of ESCC are extremely poor. Therefore, there is an urgent need to identify key pathogenic genes involved in ESCC and reveal potential molecular mechanisms. Methods: To explore potential therapeutic targets for ESCC, we analyzed three microarray datasets (GSE20347, GSE161533, and GSE38129) derived from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI). We used the GEO2R tool to screen out differentially expressed genes (DEGs) between tumourtissues and normal tissues. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/). The Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software were used to construct a protein-protein interaction (PPI) network of these DEGs. Furthermore, we used the online GEPIA database to carry out survival analysis to evaluate the prognostic value of hub genes expression in ESCC patients. Results: A total of 32 upregulated DEGs and 42 downregulated DEGs were identified in ESCC. Among them, we picked out ten hub genes with a high degree of connectivity. Overexpression of these some hub genes was associated with unfavorable prognosis of ESCC. Particularly, the overexpression of COL10A1 and SERPINE1 was observed using the qRT-PCR and indicated poor outcome of ESCC. Simultaneously, low expression of some hub genes was associated with shorter overall survival, such as ACPP and LDHA genes. Conclusion The results in this study might provide some directive significance for further exploring the potential biomarkers for diagnosis and prognosis prediction of ESCC patients. Meanwhile, further study is needed to explore the value of hub genes in the treatment of ESCC.