IDENTIFICATION OF CANDIDATE TREATMENT TARGETS IN ESOPHAGEAL CANCER: A COMPREHENSIVE BIOINFORMATIC ANALYSIS

Esophageal cancer, is among the leading causes of cancer-related mortality [۱]. Despiterecent progress in early diagnosis and more effective treatment options, patients have variable prognosis[۲]. We conducted a comprehensive bioinformatic approach to identify novel therapeutic and prognostictargets.The gene expression omnibus (GEO) database was used to compare transcriptomic profiles ofesophageal tumor and normal tissue and identify differentially expressed genes (DEG)s. The DEGswere used to construct an interaction network using the STRING. We limited the number of our genesusing centrality parameters such as degree, closeness, and betweenness. Next, pathway enrichmentanalysis was conducted. Hub genes of the network were selected based on the effect of genes on overallsurvival (OS) in the cancer genome atlas database (TCGA) and their effect on clinical staging of thedisease was interrogated. Finally immunohistochemical (IHC) staining of hub proteins in esophagustumors compared to the normal tissue was examined using Human Protein Atlas and mutational profileof the hub genes was investigated in the Gene cBioPortal database.We identified IDO۱, COL۴A۱, and ATF۳ as the main hub genes. Four modules were identified in thenetwork that showed the most correlation with Cytokine-cytokine receptor interaction, proteindigestion, ECM-receptor interaction, relaxin signaling pathway and Epithelial cell signaling inHelicobacter pylori infection pathways in enrichment analysis. Surveying Drug Bank database for hubgenes as target genes recommended Cannabidiol as a drug candidate with an inhibitory effect onoverexpression of IDO۱ in tumor tissues.Using a multi-aspect systems biology approach, we identified genes with the main regulatory role andsurvival impact on esophageal cancer. Targeting these genes with currently available or novel syntheticagents can be of potential survival advantage.

کلیدواژه ها:

Esophageal Cancer ، Systems Biology ، Hub Gene ، Drug Target