Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Cuproptosis is a new type of programmed cell death that is mediated by protein lipid acylation and is closely linked to mitochondrial metabolism. The Cancer Genome Atlas (TCGA) database provided us with RNA-Seq data together with the related clinical and prognostic information. Using univariate Cox, 135 prognostic lncRNAs associated with cuproptosis were identified for use in prognostic model construction. Multivariate Cox analysis was subsequently used to further integrate these lncRNAs. We used subject operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis to assess the model’s prognostic ability. In order to predict immune escape and potential therapeutic drugs in the two groups, we also looked at the differences in immunological and tumor mutational burden between the high- and low-risk groups. The quantitative polymerase chain reaction (Q-PCR) was then used to validate the risk model. The lncRNA profile linked to cuproptosis is used to divide patients into two risk categories, with the lower risk group having a better prognosis. The tumor mutational burden was larger, immune escape was more likely to occur, and immunotherapy results were generally worse in the high-risk group. The traditional chemotherapeutic medications sunitinib, AKT inhibitor VIII, rapamycin, and lapatinib were more effective in low-risk individuals. Six prognostic lncRNAs were ultimately confirmed in human cell lines, including HK-2, ACHN, 769-P, and CAKI-1. A risk model based on six cuproptosis-related lncRNAs has a high predictive value for ccRCC and might be a medical target for cuproptosis.
He et al. (Fri,) studied this question.