Cuproptosis is a form of cell death caused by intracellular copper excess, which plays an important regulatory role in the development and progression of cancers, including hepatocellular carcinoma (HCC), a prevalent malignancy with high morbidity and mortality. This study aimed to create a cuproptosis associated long non-coding RNAs (CAlncRNAs)signature to predict HCC patient survival and immunotherapy response. Firstly, we identified 509 CAlncRNAs using Pearson correlation analysis in The Cancer Genome Atlas (TCGA) datasets, before the three CAlncRNAs (MKLN1-AS, FOXD2-AS1, LINC02870) with the most prognostic value were further screened. Then, we constructed a prognostic risk model for HCCwas using univariate and LASSO Cox regression analyses. Multivariate Cox regression analyses illustrated that this model was an independent prognostic factor for overall survival (OS) prediction, outperforming traditional clinicopathological factors. And the risk score not only could be prognostic factors independent of other factors but also suited for patients with diverse ages, stages, and grades. The 1-, 3-, and 5- years areas under the curves (AUC) values of the model were 0.759, 0.668 and 0.674 respectively. Pathway analyses showed that the high-risk groupenriched in immune-related pathways. Importantly, patients with higher risk scores exhibited higher mutation frequency, higher TMB scores, and lower TIDE scores. Besides, we screened for two chemical drugs (A-443654 and Pyrimethamine) with the greatest value for high-risk HCC patients. Finally, the abnormal high expression of the three CAlncRNAs were confirmed in HCC tissues and cells by Real Time Quantitative PCR (RT-qPCR). And proliferative, migratory and invasion abilities of HCC cell were restrained via silencing CAlncRNAs expression in vitro. In summary, we built a CAlncRNAs-based risk score model, which can be a candidate for HCC patients prognostic prediction and offer some useful information for immunotherapies.
Keywords: CAlncRNAs; Cuproptosis; Hepatocellular carcinoma; Prognostic model; TCGA.