Interpretable machine learning prioritizes transcriptomic features associated with ferroptosis-mitochondrial permeability transition net activity states in cervical cancer

Background High-risk human papillomavirus (HPV)-associated cervical carcinogenesis involves oxidative stress, mitochondrial dysfunction, and altered non-apoptotic cell-death programs. Ferroptosis and mitochondrial permeability transition (MPT)-driven necrosis may share upstream stress-related transcriptional signals, but their joint pathway-state features in cervical cancer remain incompletely characterized. Methods We constructed a directionality-aware net pathway activity framework using evidence-audited ferroptosis-promoting, ferroptosis-inhibitory, MPT-promoting, and MPT-inhibitory gene sets in 304 TCGA-CESC primary tumors. The score was benchmarked against ssGSEA, GSVA, and PLAGE and evaluated in controlled simulations. For feature prioritization, score-defining genes were excluded from predictors and Elastic Net feature selection, XGBoost tuning, and model fitting were performed strictly within repeated nested cross-validation. Outer-test SHAP, model-sensitivity analysis, and a 200-permutation complete-pipeline test were used for interpretation and robustness assessment. External validation used GSE138080, GSE63514, GSE223804, and GSE151666. Results CombinedNet showed moderate-to-high concordance with ssGSEA, GSVA, and PLAGE (Spearman rho = 0.735, 0.882, and 0.943, respectively), while 1,300 controlled simulations showed strong recovery of the latent promoter-minus-inhibitor balance across scenarios and effect sizes (mean rho = 0.944; mean sign-recovery accuracy = 0.902; mean standardized RMSE = 0.305). The leakage-resistant repeated nested-CV model yielded AUC = 0.780 (95% CI 0.766–0.795); a matched complete-pipeline permutation test gave empirical p = 0.005. Stability-weighted SHAP and perturbation prioritized CKMT1B and KLK13 , whereas GNA13 , PANX2 , and all five sterol genes were not selected in any of 25 outer-fold models. The sterol genes retained modest score correlations but showed no model sensitivity. Across three lesion-progression cohorts, no pathway score remained associated with stage after FDR correction; in the cancer-only GSE151666 RNA-seq cohort, CombinedNet also showed no FIGO-stage trend. Conclusion This study supports directionality-aware transcriptomic scoring and a leakage-resistant feature-prioritization framework, but does not support GNA13 / PANX2 or cholesterol-biosynthesis signals as stable model drivers or the composite score as a validated lesion-stage biomarker. The findings remain hypothesis-generating and require experimental validation.