Abstract
BACKGROUND: Hepatocellular carcinoma remains the third most common cause of cancer-related deaths worldwide. Although great achievements have been made in resection, chemical therapies and immunotherapies, the pathogenesis and mechanism of HCC initiation and progression still need further exploration. Necroptosis genes have been reported to play an important role in HCC malignant activities, thus it is of great importance to comprehensively explore necroptosis-associated genes in HCC. METHODS: We chose the LIHC cohort from the TCGA, ICGC and GEO databases for this study. ConsensusClusterPlus was adopted to identify the necroptosis genes-based clusters, and LASSO cox regression was applied to construct the prognostic model based on necroptosis signatures. The GSEA and CIBERSORT algorithms were applied to evaluate the immune cell infiltration level. QPCR was also applied in this study to evaluate the expression level of genes in HCC. RESULTS: We identified three clusters, C1, C2 and C3. Compared with C2 and C3, the C1 cluster had the shortest overall survival time and highest immune score. The C1 was samples were significantly enriched in cell cycle pathways, some tumor epithelial-mesenchymal transition related signaling pathways, among others. The DEGs between the 3 clusters showed that C1 was enriched in cell cycle, DNA replication, cellular senescence, and p53 signaling pathways. The LASSO cox regression identified KPNA2, SLC1A5 and RAMP3 as prognostic model hub genes. The high risk-score subgroup had an elevated expression level of immune checkpoint genes and a higher TIDE score, which suggested that the high risk-score subgroup had a lower efficiency of immunotherapies. We also validated that the necroptosis signatures-based risk-score model had powerful prognosis prediction ability. CONCLUSION: Based on necroptosis-related genes, we classified patients into 3 clusters, among which C1 had significantly shorter overall survival times. The proposed necroptosis signatures-based prognosis prediction model provides a novel approach in HCC survival prediction and clinical evaluation.