Cuproptosis-Related 4-Gene Risk Model for Predicting Immunotherapy Drug Response and Prognosis of Kidney Renal Clear Cell Carcinoma

Abstract

Background

Kidney renal clear cell carcinoma (KIRC) is one of the most common renal malignancies with a high mortality rate. Cuproptosis, a novel form of cell death, is strongly linked to mitochondrial metabolism and is mediated by protein lipoylation, leading to a proteotoxic stress response and cell death. To date, few studies have ellucidated the holistic role of cuproptosis-related genes (CRGs) in the pathogenesis of KIRC.

Methods

We comprehensively and completely analyzed the RNA sequencing data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We screened for differentially expressed CRGs and constructed a prognostic risk model using univariate and multivariate Cox proportional regression analyses. Kaplan-Meier analysis was performed and receiver operating characteristic (ROC) curves were plotted to predict the prognosis of KIRC patients. Functional enrichment analysis was utilized to explore the internal mechanisms. Immune-related functions were analyzed using single-sample gene set enrichment analysis (ssGSEA), tumour immune dysfunction and exclusion (TIDE) scores, and drug sensitivity analysis.

Results

We established a concise prognostic risk model consisting of four CRGs (DBT, DLAT, LIAS and PDHB) to predict the overall survival (OS) in KIRC patients. The results of the survival analysis indicated a significantly lower OS in the high-risk group as compared to the patients in the low-risk group. The area under the time-dependent ROC curve (AUC) at 1, 3, and 5 year was 0.691, 0.618, and 0.614 in KIRC. Functional enrichment analysis demonstrated that CRGs were significantly enriched in tricarboxylic acid (TCA) cycle-related processes and metabolism-related pathways. Sorafenib, doxorubicin, embelin, and vinorelbine were more sensitive in the high-risk group.

Conclusions

We constructed a concise CRGs risk model to evaluate the prognosis of KIRC patients and this may be a new direction for the diagnosis and treatment of KIRC.