Prediction of Prognosis and Immunological Features in Hepatocellular Carcinoma Based on Non-Apoptotic Regulatory Cell Death Genes.

Background: Hepatocellular carcinoma (HCC) is the most common liver cancer. Exploring non-apoptotic regulated cell death (RCD) offers a strategy to overcome drug resistance. This study investigates a risk model based on non-apoptotic RCD-related genes to predict clinical outcomes and guide immunotherapy.

Methods: We identified genes associated with non-apoptotic RCD in HCC through weighted gene co-expression network analysis (WGCNA) and differential analysis. We then employed non-negative matrix factorization (NMF) clustering to categorize HCC into molecular subtypes related to non-apoptotic RCD and identified differentially expressed genes (DEGs) among these subtypes. We developed a prognostic model utilizing Cox regression and LASSO analysis, stratifying patients into specific risk groups and validating the model's prognostic significance. We subsequently analyzed immune functions and tumor mutation burden (TMB). Finally, we identified potential drugs and evaluated drug sensitivity specific to HCC.

Results: We identified four non-apoptotic RCD genes and classified patients into three subtypes. We observed significant differences in immune characteristics and prognostic outcomes among these groups. Six DEGs emerged as key indicators for risk assessment, leading to a prognostic model. High-risk patients face poorer survival rates and increased mortality. Independent prognostic analyses confirm that these models can effectively predict patient outcomes. Notably, in high-risk patients, immune-related functions appear suppressed, facilitating tumor immune evasion.

Conclusion: We developed a risk model focused on non-apoptotic RCD genes. This model accurately predicts the prognosis for HCC patients. It may also offer new insights for clinical decisions and immunotherapy.