Fig. 5

Identification and prognostic evaluation of SLC25A13-related risk score in gliomas. A Combination of multiple machine learning methods to screen SLC25A13-related core genes. B Intersection genes for the top 15 machine learning method combinations with the highest C-index. C Association of SLC25A13-related risk score with clinical parameters such as gender, age, IDH mutation status, 1p19q co-deletion status, MGMT promoter methylation status, glioma stage, overall survival, and survival status. D Kaplan–Meier survival curve analysis of high- and low-risk scoring groups in the glioma cohorts from TCGA and CGGA databases