Abstract
OBJECTIVE: Recent studies reveal that necroptosis is pivotal in tumorigenesis, cancer metastasis, cancer immunity, and cancer subtypes. Apoptosis or necroptosis of hepatocytes in the liver microenvironment can determine the subtype of liver cancer. However, necroptosis-related genomes have rarely been analyzed in hepatocellular carcinoma (HCC). Therefore, this study aims to construct an HCC risk scoring model based on necroptosis-related genes and to validate its predictive performance in overall survival prediction and immunotherapy efficacy evaluation in HCC, as well as to analyze drug treatment responses. METHODS: This study analyzed clinical information and RNA-seq expression data of liver cancer patients from TCGA public data, identified necroptosis-related genes, and conducted GO and KEGG enrichment analyses. Using Cox regression analysis and LASSO analysis to identify independent prognostic factors, a predictive model was established and validated in clinical subgroups, and correlation analysis with immune cells and ssGSEA differential analysis were conducted. Finally, potential drugs for HCC were screened to explore the drug sensitivity of different subtypes. RESULTS: We identified 19 differentially expressed necroptosis-related genes and constructed a predictive model with 3 independent prognostic factors through stepwise Cox regression. Validation results from clinical subgroups showed that the constructed model performed well in risk prediction, and ssGSEA differential analysis results were significant. We analyzed 55 immunotherapy drugs, and clustered them by distinct IC50 values to guide drug selection for HCC patients. Notable, Bleomycin, Obatoclax. Mesylate, PF.562271, PF.02341066, QS11, X17. AAG, and Bl. D1870 exhibited significantly different sensitivities in different subtypes, providing references for clinical practice in HCC patients.