CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling

Abstract

Abstract Background: Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered as the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis and therefore, discovery of drugs can be helpful in this case. Methods: Differentially expressed genes were explored between human gastric cancer and noncancerous tissues in Gene Expression Omnibus dataset (GSE54129) using the GEO2R tool, and their functions were annotated with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses in the STRING database. Hub genes were identified based on the protein-protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further analysis, and their expression patterns in gastric cancer patients were investigated using the GEPIA2 database. Results: Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients. A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. Conclusions: The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis acts as a hypothesis generator that requires further clinical validation.