Development and validation of a robust cuproptosis related signature for primary glioma via machine learning aided by loop training and validation

Glioma is the most common malignant tumors in central nervous system with high mortality. Accurately predicting prognosis for patients with glioma still remains a challenge. Accumulated studies have found that cuproptosis-related genes emerged as potential biomarkers for cancer prognosis. However, their prognostic roles in primary glioma are unclear. This study aimed to develop a promising prognostic signature for primary glioma using cuproptosis-related genes. A total of 1248 patients with primary glioma were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. 101 machine learning algorithm combinations together with a loop training and validation procedure were performed to identify the optimal model termed cuproptosis-related prognostic signature (CRPS). The predictive accuracy of CRPS was evaluated through Kaplan-Meier survival curves and receiver-operator characteristic (ROC) analyses. Furthermore, we compared the performance of CRPS with common clinical features and 72 published prognostic signatures. CRPS exhibited robust predictive capability in overall survival (OS) and could serve as an independent prognostic biomarker in different cohorts including TCGA-GBMLGG (HR: 1.987, 95%CI: 1.239–3.189, p < 0.001), CGGA693 (HR: 2.374, 95%CI: 1.505–3.745, p < 0.001) and CGGA325 (HR: 2.248, 95%CI: 1.334–3.787, p = 0.002). Simultaneously, CRPS outperformed 72 published signatures and traditional clinical features. Additionally, a nomogram by the combination of CRPS and tumor grade contributes to more precise prognosis prediction. Our study highlights CRPS as a promising tool in prognosis evaluation, survival risk stratification and personalized clinical management for primary glioma.