Exploring prognosis and therapeutic strategies for HBV-HCC patients based on disulfidptosis-related genes.

BACKGROUND: Hepatocellular carcinoma (HCC) accounts for over 80% of primary liver cancers and is the third leading cause of cancer-related deaths worldwide. Hepatitis B virus (HBV) infection is the primary etiological factor. Disulfidptosis is a newly discovered form of regulated cell death. This study aims to develop a novel HBV-HCC prognostic signature related to disulfidptosis and explore potential therapeutic approaches through risk stratification based on disulfidptosis. METHODS: Transcriptomic data from HBV-HCC patients were analyzed to identify BHDRGs. A prognostic model was established and validated using machine learning, with internal datasets and external datasets for verification. We then performed immune cell infiltration analysis, tumor microenvironment (TME) analysis, and immunotherapy-related analysis based on the prognostic signature. Besides, RT-qPCR and immunohistochemistry were conducted. RESULTS: A prognostic model was constructed using five genes (DLAT, STC2, POF1B, S100A9, and CPS1). A corresponding prognostic nomogram was developed based on riskScores, age, stage. Stratification by median risk score revealed a significant correlation between the prognostic signature and TME, tumor immune cell infiltration, immunotherapy efficacy, and drug sensitivity. The results of the experiments indicate that DLAT expression is higher in tumor tissues compared to adjacent tissues. DLAT expression is higher in HBV-HCC tumor tissues compared to normal tissues. CONCLUSION: This study stratifies HBV-HCC patients into distinct subgroups based on BHDRGs, establishing a prognostic model with significant implications for prognosis assessment, TME remodeling, and personalized therapy in HBV-HCC patients.