Integrated Analysis of Hub Genes and Pathways In Esophageal Carcinoma Based on NCBI’s Gene Expression Omnibus (GEO) Database: A Bioinformatics Analysis
Tan Yu-jing, Tang Wen-jing, Tang Biao
Department of Physiology, Hunan University of Chinese Medicine, Changsha, Hunan, China (mainland)
Med Sci Monit 2020; 26:e923934
Available online: 2020-06-10
Esophageal carcinoma (ESCA) is a health challenge with poor prognosis and limited treatment options. Our aim is to screen for hub genes and pathways associated with ESCA pathology as diagnostic or therapeutic targets.
MATERIAL AND METHODS: We downloaded 2 ESCA-related datasets from the Gene Expression Omnibus (GEO) database. Subsequently, differentially expressed genes (DEGs) of ESCA were determined by statistical analysis. Both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using online analytic tools. Network analysis was employed to construct a protein-protein interaction (PPI) network and to filter hub genes. We evaluated the expression level and impact of hub genes on survival of ESCA patients using the OncoLoc webserver.
RESULTS: A total of 210 DEGs were identified. The GO analysis showed that the DEGs were enriched in cell division. The KEGG pathway analysis showed DEGs that were enriched in cell cycle regulation, known cancer pathways, the PI3K-Akt signaling pathway, and the cGMP-PKG signaling pathway. The top 10 hub genes were markedly upregulated in ESCA tissue compared with normal esophageal tissue. Moreover, the expression level of the hub genes was different at different pathological stages of ESCA. Further prognostic analysis identified that the top 10 hub genes were related to late survival of ESCA patients, while exhibiting few associations with early survival time.
CONCLUSIONS: The signaling pathways involving the DEGs probably represent the pathological mechanism underlying ESCA. The hub genes were associated with survival of ESCA patients, and as such have the potential to serve as diagnostic indicators and therapeutic targets.
Keywords: Computational Biology, Esophageal Neoplasms, Gene Expression