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.

Background and objectives: Gastric cancer (GC) is one of the most common malignancies, but research on it is still limited. The role of immuntonic cell death (ICD)-associated long non-coding RNA (lncRNA) in GC is still unclear. Therefore, this experiment aimed to investigate the role of ICD-related lncRNA in GC, its prognostic value, and immunotherapeutic potential. Methods: In this study, the relevant data were obtained from The Cancer Genome Atlas database. We used Pearson correlation coefficient analysis to obtain the ICDrelated lncRNA, and randomly divided the data into the test and training groups in a 1:1 ratio. Then, we used Cox regression analysis and Lasso regression analysis to build a prognostic model. The receiver operating characteristic (ROC) curve was applied to analyze its accuracy, and immunocorrelation analysis was also performed for GC. Results: In this study, nine lncRNAs were selected to construct a prognostic feature, comprising AC005332.1, AC116312.1, LINC00705, CEP250-AS1, AC234775.2, LINC01150, FLJ16779, UBL7-AS1, and AC010457.1. The result of the ROC curve proved the reliability of its feature. The concurrently constructed features could be used as independent variables for a variety of clinical conditions. The analysis of the immune-related functions showed that there were differences in some immune functions between the high-risk and low-risk groups. We also found that the high-risk group was more sensitive to immunotherapy. Conclusion: Based on the analysis of the ICD-related lncRNA in GC, our immune-related predictions and model could help predict the outcome of GC and could provide a reference for clinical practice.