Tumor subtype-specific transcriptional signatures predict recurrence risk in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) displays significant molecular heterogeneity that clinical staging alone does not fully account for when assessing the risk of recurrence following curative treatment. To address this, we developed a multi-cohort survival modeling framework that translates bulk RNA-seq profiles into biologically meaningful tumor lineage scores. These scores are then used to stratify disease-free survival (DFS). Our study included 1,059 patients from four independent cohorts, where tumor immunogenic and proliferative lineage scores satisfied the proportional hazards assumptions and were integrated into a cohort-stratified Cox model. Each patient received a linear predictor (LP), and predefined cutpoints were established to categorize them into actionable risk groups. These groups exhibited consistent DFS separation in both the training (70%) and independent test (30%) sets. In ROC analyses, the LP demonstrated moderate overall discrimination (AUC 0.629, 95% CI 0.595-0.662). It showed higher discrimination for early recurrence (＜1 year; AUC 0.653, 95% CI 0.591-0.715) and lower discrimination for later recurrence (3-5 years; AUC 0.576, 95% CI 0.473-0.678). This approach establishes a biologically informed framework for stratifying recurrence risk based on RNA-seq data, potentially enhancing riskadapted surveillance and postoperative management for HCC.