A machine learning-derived aging-related gene signature for diagnosing HCV cirrhosis: Insights into immune microenvironment and therapeutic targets.

BACKGROUND

While direct-acting antiviral (DAA) therapies effectively cure most chronic hepatitis C virus (HCV) infections, patients with pre-existing cirrhosis remain at risk for disease progression. Cellular senescence and immune dysregulation drive persistent liver injury post-clearance, but reliable biomarkers and therapeutic targets remain scarce.

METHODS

Aging-related genes (ARGs) were integrated with differentially expressed genes and key WGCNA modules derived from the GSE14323 dataset, yielding 26 overlapping candidates. Hub genes were screened via protein-protein interaction and refined by LASSO, Random Forest, and Boruta. The diagnostic signature was validated in GSE14323 and GSE6764. Immune infiltration was assessed by CIBERSORT and ssGSEA, with molecular docking for drug screening.

RESULTS

Four hub genes (CTGF, FAS, GJA1, MMP7) were ultimately identified. The signature showed high diagnostic accuracy, achieving AUCs of 1.0 and 0.946 in the training and validation cohorts, respectively. HCV cirrhosis exhibited markedly altered immune infiltration profiles, and high-risk cases were significantly enriched for ECM remodeling, senescence, and TGFβ-SMAD signaling. The hub genes were closely correlated with immune- and fibrosis-related pathways. Furthermore, Marimastat, Obeticholic acid, and Pirfenidone showed strong binding to MMP7.

CONCLUSION

We established a novel ARG-based diagnostic signature for HCV cirrhosis, highlighting its association with immune remodeling and pathogenic pathways. Notably, MMP7 is a promising target, providing insights for post-clearance disease management.