Construction of the cuproptosis-related immune-checkpoint gene signature

To avoid overfitting the model, univariate variables (p < 0.05) were included in the least absolute shrinkage and selection operator (LASSO) analysis, which was used to further select important predictive features. The optimized model was developed using a 10-fold cross-validation strategy. After calculating the risk score, the following regression coefficients were calculated:

Based on the median risk score, the patients in TCGA cohort were separated into low- and high-risk groups, and OS was compared between the groups using the Kaplan-Meier method and log-rank test. Using the "survivalROC" R package, the area under the time-dependent ROC curve (AUC) was calculated to measure the prediction accuracy of the risk score. To validate this prognostic model, the risk score for each patient in the external test cohorts (ICGC and primary ccRCC subgroup of {"type":"entrez-geo","attrs":{"text":"GSE22541","term_id":"22541"}}GSE22541) was generated in the same way to ensure the consistency of the model.