COL۱A۱, COL۱A۲, FN۱, and MMP۲ asPromising Prognostic Biomarkers in Gastric Cancer: AnInvestigation

Background: Gastric cancer (GC) is highly lethal, and the absenceof effective biomarkers challenges its diagnosis and treatment.This study aimed to identify novel hub genes associatedwith GC for predicting prognosis and developing better treatmentstrategies.Materials and Methods: The GSE۵۴۱۲۹ dataset was acquiredusing the GEOquery package in R, consisting of ۱۱۱ cancerand ۲۱ normal samples. Differentially expressed genes (DEGs)were identified using the criteria of > ۲ and adj P-value < ۰.۰۵.A protein-protein interaction (PPI) network was constructed usingthe STRING database and analyzed through the Cytoscapesoftware. Hub genes were determined using the MCODE andcytoHubba plugins. Gene Ontology (GO) and Kyoto Encyclopediaof Genes and Genomes (KEGG) pathway analysis wereperformed using DAVID. The prognostic value of hub geneswas performed through the Kaplan-Meier plotter database(http://Kmplot.com/analysis).Results: A total of ۷۶۳ DEGs were identified, of which ۳۵۵were up-regulated and ۴۰۸ were down-regulated. The KEGGpathway analysis revealed that the metabolism of xenobioticsby cytochrome P۴۵۰ (hsa۰۰۹۸۰) pathway was significantlyenriched. According to GO annotation, xenobiotic metabolicprocess (GO:۰۰۰۶۸۰۵), extracellular region (GO:۰۰۰۵۵۷۶),extracellular matrix structural constituent (GO:۰۰۰۵۲۰۱) wereenriched in biological process, cellular component, and molecularfunction, respectively. Hub genes were identified owingto the top ۵ genes in the maximal clique centrality (MCC)algorithm from the pivotal cluster (score = ۲۰.۶۴۵), includingcollagen type I alpha ۱ chain (COL۱A۱), fibronectin ۱ (FN۱),matrix metallopeptidase ۲ (MMP۲), matrix metallopeptidase ۹(MMP۹) and collagen type I alpha ۲ chain (COL۱A۲). Survivalanalysis indicated that overexpression of these genes, exceptfor MMP۹, was associated with a reduced overall survival rate.Conclusion: In the present study, the bioinformatics analysisdistinguished four genes significantly related to poor prognosisof GC, which may be used as potential biomarkers for earlydiagnosis and disease prevention.

کلیدواژه ها:

Gastric cancer ، Prognosis ، Biomarker ، Protein-proteininteraction ، Differentially expressed genes ، Bioinformatics