A multi‐omics‐based investigation of the immunological and prognostic impact of necroptosis‐related genes in patients with hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common histological subtype of liver cancer and the third leading cause of death from cancer globally. Recent studies suggested cell death is also a key regulator of tumour progression. The purpose of this study was to generate a new predictive signature for HCC patients based on a complete analysis of necroptosis‐associated genes. METHODS: We extracted the mRNA expression profiles of HCC patients from the TCGA and ICGC databases and their clinical data. In addition, we used the IMvigor210 cohort to validate our model molecule's ability to predict the effect of immunotherapy. In the TCGA cohort, a seven‐gene risk‐prognostic model was constructed using univariate cox‐Lasoo regression. External validation was conducted using the ICGC cohort. The ssGSEA algorithm is used to determine the degree of immune function response. The CMAP databases are used for chemotherapy drug analysis and screening for drugs that reduce the expression of high‐risk genes. The cbioportal database was used to explore mutations in model genes. RESULTS: Survival analysis shows shorter survival for high‐risk patients. Immune function analysis revealed significant differences in the activity of immune pathways between risk subgroups. Varied risk scores result in dramatically diverse immune infiltration and tumour growth, as well as significantly different chemotherapeutic sensitivity. In addition, Apigenin and LY‐294002 reduced the expression of high‐risk genes, while Arecoline had the opposite effect. In the immunotherapy IMvigor210 cohort, risk scores were significantly different between the objective responder and non‐responder groups. By comparing the models constructed with published literature, it is suggested that our model has better predictive power. CONCLUSIONS: We created a new prognostic signature of necroptosis‐related genes that can be used as potential prognostic biomarkers to guide effective personalized therapy for hepatocellular carcinoma patients.