ArticleFrontiers in bioinformatics2026
Multi-cohort machine learning identifies a ferroptosis-linked prognostic signature in lung adenocarcinoma.
Article in Frontiers in bioinformatics, 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
Introduction: Lung adenocarcinoma (LUAD) shows marked outcome heterogeneity within clinicopathological stage groups. This study developed and externally validated a ferroptosis-linked transcriptomic risk model using a leakage-controlled multi- cohort survival-learning framework and characterized its immune context. Methods: Candidate genes were defined by combining FerrDb V3-annotated genes within a ferroptosis-associated weighted gene co-expression network analysis (WGCNA) module with a filtered WGCNA discovery branch derived from the independent GSE81089 cohort. Model development used TCGA-LUAD, GSE31210, GSE72094, and GSE136961 (1,149 patients; 344 overall-survival events) with bagged cross-cohort Cox screening, leave-one-cohort-out (LOCO) stability locking, and benchmarking of 150 survival-learning configurations. External evaluation used the GSE50081, GSE68465, and GSE30219 cohorts (912 patients; 507 events). Results: The development-selected extra survival trees configuration reached a mean leave-one-cohort-out Uno's C-index of 0.723. The highest cohort-specific C-indices among the prespecified candidate configurations were 0.611, 0.691, and 0.699 and arose from different model configurations. Applying the same development-selected configuration to all three external cohorts yielded Harrell's C-indices of 0.605, 0.675, and 0.682 and 5-year Uno's C-indices of 0.605, 0.678, and 0.696. In TCGA-LUAD, the stored out-of-fold molecular score remained prognostic after TNM stage adjustment (hazard ratio per standard deviation 2.32, 95% confidence interval: 1.39-3.87; p = 0.0013). Discussion: At 3 years and 5 years, the combined TNM-plus-score models showed close calibration and modest gains in discrimination, while decision-curve analysis identified positive incremental net benefit only over restricted threshold ranges. Low-risk tumors were enriched for interferon, complement, and immune-cell programs. These computational findings require further prospective assay-level and experimental validation before clinical implementation.
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