Targeting p53 Dysfunction in Hepatocellular Carcinoma: Translational Barriers and Opportunities for AI-Driven Precision Medicine

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with TP53 dysfunction representing a central molecular alteration in approximately 30% of cases [1,2]. Despite robust preclinical evidence supporting p53 as a therapeutic target, clinical translation has been severely limited by delivery challenges, tumor heterogeneity, the cirrhotic microenvironment, and inadequate patient stratification. This critical narrative review examines the paradox of p53's biological centrality contrasted with its clinical underperformance in HCC. We analyze translational barriers and emerging therapeutic strategies while proposing that artificial intelligence (AI)-driven frameworks may, in the future, reposition TP53 as a useful stratification biomarker. At present, however, AI applications remain largely investigational retrospective, single center, and lacking prospective validation and any clinical applications should be considered adjunctive and hypothesis-generating rather than ready for routine use. The review also highlights the under explored potential of hotspot specific biology as a direction for future research, while emphasizing the need for rigorous prospective studies before impacting trial design or therapy planning.