Comprehensive multi-omics analysis identifies cancer subtypes and prognostic signatures of hepatitis B virus-associated hepatocellular carcinoma.

Hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) is a heterogeneous malignancy with poor prognosis, necessitating refined classification and novel therapeutics. Leveraging multi-omics data, we utilized the MOVICS package to identify two robust cancer subtypes (CSs) from the TCGA-LIHC cohort. CS1 was characterized by a favorable prognosis and a "hot" immune microenvironment, whereas CS2 exhibited aggressive clinicopathological features and a "cold" immune landscape. To predict prognosis, we constructed a machine learning-derived prognostic signature (MLPS) using 92 algorithm combinations. The optimal model, identified as RSF+Enet[alpha = 0.2], demonstrated superior predictive accuracy and outperformed ten existing HBV-HCC models. Furthermore, we implemented a drug repurposing strategy for high-risk patients and identified KX2-391, a dual inhibitor from the CTRP and PRISM databases, as a potent therapeutic agent. In vitro assays confirmed that KX2-391 dose-dependently induces apoptosis in high-MLPS HBV-HCC cell lines, and in vivo xenograft models demonstrated its superior efficacy in inhibiting tumor growth compared to lenvatinib, without significant organ toxicity. Mechanistically, we discovered that F9 acts as a tumor suppressor by interacting with SERPINC1 to inhibit the Wnt/β-catenin signaling pathway. Collectively, our study provides a novel HBV-HCC classification system, a high-performance prognostic tool, and identifies both KX2-391 and F9 as promising avenues for precision therapy.