Machine learning identifies a 10-gene signature predicting hepatocellular carcinoma recurrence and immune–metabolic reprogramming
Menée à l'aide d'un algorithme d'apprentissage automatique et de données transcriptomiques portant sur 344 patients atteints d'un carcinome hépatocellulaire, cette étude met en évidence la performance d'un modèle, basé sur le stade tumoral et l'expression de 10 gènes, pour prédire le risque de récidive et de modification métabolique des cellulles immunitaires
Background : 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.
Methods : 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.
Results : 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.
Conclusions : 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.
European Journal of Surgical Oncology , résumé, 2026