Gastric cancer (GC) remains one of the most prevalent and fatal cancers worldwide, with high rates of both incidence and mortality. Unfortunately, most patients are diagnosed at advanced stages, often through routine screenings, missing the best time for intervention. In this research, weighted gene co-expression network analysis (WGCNA) was used to identify key gene modules and hub genes associated with GC. Using the “limma” package in R, differentially expressed genes (DEGs) were examined from TCGA’s GC dataset, resulting in the identification of 4892 DEGs. To better understand their biological roles, Gene ontology (GO) enrichment and KEGG pathway analysis were performed, which revealed that the DEGs were strongly involved in processes such as extracellular matrix organization, DNA replication, the cell cycle, and the p53 signaling pathway. WGCNA was also applied to identify gene modules associated with clinical characteristics in both GC and normal tissue samples. Six distinct gene modules were identified, with two showing significant association with GC. Hub genes in these modules were determined through survival and expression analyses. In addition, one-way ANOVA was used to examine how these hub genes were expressed across different stages of GC compared to normal tissues. This analysis revealed 19 genes with significant differential expression and positive prognosis implications. These hub genes showed significant differences in expression levels between normal and different GC stages, highlighting their potential as biomarkers for early detection of GC and as valuable tools to aid in diagnosis at earlier stages.
