ArticleFrontiers in genetics2026
Interpretable machine learning prioritizes transcriptomic features associated with ferroptosis-mitochondrial permeability transition net activity states in cervical cancer.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 Conclusion: This study supports directionality-aware transcriptomic scoring and a leakage-resistant feature-prioritization framework, but does not support
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