A dual gene-signature framework for glioma survival prediction with multi-cohort validation

Despite the proliferation of prognostic gene signatures for glioma, clinical translation remains stalled by poor reproducibility and overfitting. In this study, we address this stability crisis by developing a robust "Dual-Signature Framework" using stability selection-a rigorous resampling method-rather than standard regression. Analyzing RNA-seq data from 1351 patients across the TCGA (n = 694) and CGGA (n = 657) cohorts, we constructed two distinct models. The primary 20-gene "Data-Driven" signature achieved superior predictive accuracy (C-index: 0.7392), significantly outperforming 14 published benchmark models and the current best single-gene predictor (HOXA5). In parallel, we derived a 7-gene "Biology-Driven" signature (including HOXA5, CHI3L1, MMP14) that retained 98% of the predictive power (C-index: 0.7252) while prioritizing mechanistic interpretability. Both models successfully stratified patients into distinct risk groups with high statistical significance (Log-rank p < 0.001) in external validation. Comprehensive subgroup analyses across 19 clinical and molecular subgroups demonstrated robust performance (C-index range: 0.59-0.85), with extended calibration analysis confirming excellent probability estimation (Brier score 0.20 for 5-year predictions). By integrating stability-driven feature selection with biological pathway constraints, this study provides a reproducible, high-performance alternative to unstable "black box" models, offering a translation-ready tool for personalized glioma risk assessment.