Integrated Analysis of Multiple Microarray Datasets Reveals Key Genes in Gastric Cancer

Gastric cancer (GC) is still one of the main contributors to deaths from cancer globally; therefore, searching for molecular biomarkers is essential for the early detection of the disease. The current study focuses on searching for reliable differentially expressed genes (DEGs), pathways, and prognostic hub genes in patients with GC using the bioinformatics approach. Three publicly available microarray datasets (GSE۵۴۱۲۹, GSE۷۹۹۷۳, and GSE۶۶۲۲۹) based on the GPL۵۷۰ array were downloaded from the GEO database. Analysis of differential expression was carried out using the limma software, whereas robust DEGs were selected with the help of the Robust Rank Aggregation (RRA) approach. Functional enrichment of the DEGs, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations, was performed with the DAVID resource. The PPI network was created to search for hub genes using the maximal clique centrality (MCC) method. The expression and prognostic significance of hub genes were further validated using GEPIA۲. The total number of ۱۰۵ upregulated and ۱۳۱ downregulated DEGs were found. Functional enrichment showed that upregulated DEGs participated in ECM organization, cell adhesion, and cancers, while downregulated genes were responsible for gastric functions and metabolism. Through the PPI network analysis, a total of ten hub genes were identified, and all of these genes showed significant differential expression in gastric cancer. Out of them, COL۱A۱, COL۴A۱, COL۵A۲, and COL۱۲A۱ showed significant overall survival, indicating their prognostic role. These findings provide further insight into the molecular mechanisms underlying gastric cancer and identify promising biomarkers and potential therapeutic targets that warrant further experimental validation.

کلیدواژه ها:

Gastric cancer ، Bioinformatics ، Robust Rank Aggregation ، Hub genes ، Prognostic biomarkers