Construction and Validation of a Hepatocellular Carcinoma Prognostic Model Based on Bile Acid Metabolism and Immune-Related Genes

BAMIRGs were extracted from TCGA and GEO databases. Models were constructed using non-negative matrix factorization clustering and minimally absolute shrinkage and selection operator (MASS) Cox regression for performance evaluation. Accuracy was compared against other published HCC prognostic prediction models.Results BAMIRGs differentiated HCC patients across subtypes and influenced the immune microenvironment.

The BAMIRGs model demonstrated favorable predictive performance (C-index=0.668), outperforming previously published models, particularly for patients with diverse clinical characteristics. The risk score emerged as an independent prognostic factor for HCC. Model risk scores showed significant correlations with immunosuppressive microenvironments and tumor mutational burden.

The high-risk HCC group was associated with immune infiltration processes, while the low-risk group correlated with metabolic processes.Conclusion The BAMIRGs model effectively predicts HCC prognostic risk and immune microenvironment, offering a promising tool for HCC risk assessment and personalized treatment.