Background: Copper metabolism plays an important role in the tumor microenvironment, and cuproptosis is the last discovered programmed cell death process. However, the potential mechanism of cuproptosis in regulating the immune microenvironment of HCC remains unclear.Methods: A total of 716 HCC patients with complete mRNA expression and survival information were collected from three public HCC cohorts (TCGA-LIHC cohort, n = 370; GSE76427 cohort, n = 115; ICGC-LIRI cohort, n = 231). The unsupervised clustering analysis (NMF) was performed to identify three different cuproptosis-related subtypes. The univariate-Cox, lasso-Cox and multivariate-Cox regression analyses were performed to screen the cuproptosis related and construct the cuproptosis-related prognosis signature (Cu-PS). The immune cell infiltration was estimated by both CIBERSORT and MCPcounter algorithms.Results: This study identified three distinct cuproptosis-related metabolic patterns, which presented different pathway enrichment and immune cell infiltration. The Cu-PS, a 5-genes (C7, MAGEA6, HK2, CYP26B1 and EPO) signature, was significantly associated with TNM stage, tumor mutational burden (TMB), drugs sensitivity, and immunotherapies response.Conclusion: This study performed a multi-genetic analysis of cuproptosis-related genes and further explored the regulatory mechanism of cuproptosis in HCC. The Cu-PS might be a useful biomarker for predicting immunotherapy response and enhancing the diagnosis and treatment of HCC.

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Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and play fundamental roles in immune regulation. Growing evidence suggests that immunerelated genes and lncRNAs can serve as markers to predict the prognosis of patients with cancers, including hepatocellular carcinoma (HCC). This study aimed to contract an immunerelated lncRNA (IR-lncRNA) signature for prospective assessment to predict early recurrence of HCC. A total of 319 HCC samples under radical resection were randomly divided into a training cohort (161 samples) and a testing cohort (158 samples). In the training dataset, univariate, lasso, and multivariate Cox regression analyses identified a 9-IR-lncRNA signature closely related to disease-free survival. Kaplan-Meier analysis, principal component analysis, gene set enrichment analysis, and nomogram were used to evaluate the risk model. The results were further confirmed in the testing cohort. Furthermore, we constructed a competitive endogenous RNA regulatory network. The results of the present study indicated that this 9-IR-lncRNA signature has important clinical implications for improving predictive outcomes and guiding individualized treatment in HCC patients. These IR-lncRNAs and regulated genes may be potential biomarkers associated with the prognosis of HCC.