A novel prognostic model based on immunogenic cell death-related genes for improved risk stratification in hepatocellular carcinoma patients.

Purpose: Hepatocellular carcinoma (HCC) is a prevalent primary malignant tumor with increasing incidence and mortality rates in recent years. The treatment options for advanced HCC are very limited. Immunogenic cell death (ICD) plays an important role in cancer, in particular immunotherapy. However, the specific ICD genes and their prognostic values in HCC remain to be investigated.

Methods: The TCGA-LIHC datasets were obtained from TCGA database, LIRI-JP datasets were obtained from ICGC database, and immunogenic cell death (ICD) genes datasets were obtained from previous literature. WGCNA analysis identifies ICD-related genes. Functional analysis was used to investigate the biological characteristics of ICD-related genes. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select prognostic ICD-related genes and construct a prognostic risk score. Prognostic independence of ICD risk scores was determined by univariate and multivariate Cox regression analyses. A nomogram was then constructed and the diagnostic value was assessed using decision curve analysis. Immune infiltration analysis and drug sensitivity analysis were used to investigate immune cell enrichment and drug response in HCC patients classified as low or high risk based on their risk score.

Results: Most of the ICD genes were differentially expressed in normal and HCC patients, and some ICD genes were differentially expressed in different clinical groups. A total of 185 ICD-related genes were identified by WGCNA. Prognostic ICD-related genes were selected using a univariate Cox analysis. A model comprising nine prognosis ICD-related gene biomarkers was developed. Patients was divided into high-risk and low-risk groups, and patients in high-risk groups had poorer outcomes. Meanwhile, the reliability of the model was verified by external independent data. The Independent prognostic value of the risk score in HCC was investigated by univariate and multivariate Cox analyses. Diagnostic nomogram was constructed to predict prognosis. Through immune infiltration analysis, we found that some innate and adaptive immune cells were significantly different between low- and high-risk groups.

Conclusion: We developed and validated a novel prognostic predictive classification system for HCC based on nine ICD-related genes. In addition, immune-related predictions and model could help predict the outcomes of HCC and could provide a reference for clinical practice.