Abstract
Background: Hepatocellular carcinoma (HCC) remains a growing threat to global health. Necroptosis is a newly discovered regulated cell necrosis that plays a vital role in cancer development. Thus, we conducted this study to develop a predictive signature based on necroptosis-related genes.Methods: The tumor samples in The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) cohort were subtyped using the consensus clustering algorithm. Univariate Cox regression and LASSO-Cox analysis were performed to construct a gene signature model from differentially expressed genes between tumor clusters. Then we integrated TNM stage and the prognostic model to build a nomogram. The gene signature and the nomogram were externally validated in the GSE14520 cohort from the gene expression omnibus (GEO) and LIRP-JP cohort from the International Cancer Genome Consortium (ICGC). Predictive performance evaluation was conducted using Kaplan-Meier plot, time-dependent receiver operating characteristic curve, principal components analysis, concordance index, and decision curve analysis. The tumor microenvironment was estimated using seven published methods. Finally, we also predicted the drug responses to immunotherapy, conventional chemotherapy and molecular-targeted therapy using two algorithms and two datasets. Results: We identified two necroptosis-related clusters and a ten-gene signature (MTMR2, CDCA8, S100A9, ANXA10, G6PD, SLC1A5, SLC2A1, SPP1, PLOD2, and MMP1). The gene signature and the nomogram had good predictive ability in TCGA, ICGC, and GEO cohorts. The risk score was positively associated with the degree of necroptosis and immune infiltration (especially immunosuppressive cells). The high-risk group could benefit more from immunotherapy. Chemotherapy and molecular-targeted therapy should be adapted to the molecular profiles of each patient.Conclusion: The necroptosis-related gene signature provides reliable evidence for prognosis prediction, comprehensive treatment, and new therapeutic targets for HCC patients. The nomogram can further improve predictive accuracy.