Machine learning identifies a 10-gene signature predicting hepatocellular carcinoma recurrence and immune-metabolic...

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality, with frequent recurrence after curative treatment. Conventional clinicopathological prognostic systems fail to capture the molecular heterogeneity underlying recurrence, highlighting the need for biologically informed biomarkers.

We developed a transcriptome-based prognostic model for HCC recurrence using a machine learning-guided feature selection strategy designed to reduce survival-time bias. LASSO-based gene selection was integrated with multivariable Cox regression, and model performance was assessed through stratified cross-validation and independent external validation.

Analysis of the TCGA-LIHC cohort (n = 344) identified a stable 38-gene recurrence-associated signature (AUC: 0.715-0.847), which was distilled into a reproducible 10-gene classifier combined with tumor stage. This integrated model effectively stratified patients into high- and low-risk groups (log-rank P < 0.0001) and was independently validated in the HCCDB25 cohort (n = 158; P = 0.031). High-risk tumors exhibited an immune-excluded phenotype with reduced cytotoxic immune infiltration. Gene set enrichment analyses revealed progressive activation of proliferative signaling, metabolic dysregulation, and immune evasion pathways.

The integrated 10-gene-plus-stage classifier is a robust and generalizable predictor of HCC recurrence, providing mechanistic insights into immune-metabolic reprogramming and highlighting potential implications for risk-informed patient stratification in future studies.