Background:
Clear cell renal cell carcinoma (ccRCC), hepatocellular carcinoma (HCC), lung adenocarcinoma (LUAD), and pancreatic ductal adenocarcinoma (PDAC) are highly lethal malignancies originating from distinct tissues yet sharing convergent molecular mechanisms that drive tumour progression. Although cancer-specific biomarkers have been reported, systematic pan-cancer analyses aimed at identifying shared molecular biomarkers across these malignancies remain limited.
Objective:
This study aimed to identify common dysregulated genes and potential pan-cancer biomarkers across ccRCC, HCC, LUAD, and PDAC using integrative bioinformatics and survival analyses.
Methods:
Microarray expression datasets for the four cancer types were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to identify differentially expressed genes (DEGs). Disease-associated gene targets were collected from the Comparative Toxicogenomics Database (CTD), DISEASES, and GeneCards. Shared genes across all cancers were identified using Venn diagram-based intersection analysis. Functional enrichment analysis was performed using ShinyGO to elucidate biological processes and pathways. Protein–protein interaction (PPI) networks were constructed using STRING and further analyzed in Cytoscape with the CytoHubba plugin to identify hub genes. Prognostic relevance was assessed using Kaplan–Meier survival analysis via KM-Plotter.
Results:
Ten candidate biomarker genes, HGF, CDK1, CCNB1, RRM2, KIF14, DCN, SERPINE1, CCNA2, DLGAP5, and MAD2L1, were consistently dysregulated across ccRCC, HCC, LUAD, and PDAC. Functional enrichment revealed significant involvement of these genes in key oncogenic pathways, particularly cell cycle regulation and tumour proliferation. Survival analyses demonstrated that aberrant expression of these hub genes was significantly associated with poor prognosis across multiple cancer types.
Conclusion:
This pan-cancer integrative analysis identifies shared molecular drivers underlying diverse malignancies and highlights robust candidate biomarkers with prognostic and therapeutic relevance. These findings provide a foundation for cross-cancer biomarker development; future experimental studies are required to confirm their translational utility.
