The machine learning-driven GBM models consistently demonstrated superior discriminative accuracy (AUC: 0.752-0.915) compared to conventional methods in predicting both OS and BRFS, primarily attributed to their ability to resolve nonlinear interactions between TET2 mutations and traditional clinicopathological parameters. The gene discussed is TET2; the disease is glioblastoma.