Developing risk stratification strategies and biomarkers for recurrent hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with high rates of post-resection recurrence posing significant clinical challenges. Early recurrence is largely driven by aggressive tumor biology, while late recurrence reflects de novo carcinogenesis in a cirrhotic liver. Traditional clinical and pathological predictors are insufficient for accurately identifying high-risk patients. Emerging translational advances including genomic, transcriptomic, proteomic, and metabolomic biomarkers; liquid biopsy techniques; artificial intelligence (AI)-driven histological and radiomic analyses offer new avenues to refine recurrence risk stratification and guide perioperative therapy. Simultaneously, the shifting etiological landscape from viral hepatitis to metabolic dysfunction-associated steatohepatitis (MASH) and alcohol-related liver disease underscores the need for tailored surveillance and preventive strategies. Advanced technologies such as single-cell and spatial transcriptomics provide unprecedented insights into fibrosis progression and tumor evolution. Integrating these approaches may enable personalized surveillance protocols and therapeutic interventions, optimizing outcomes for HCC patients and reducing unnecessary resource utilization.