ArticleHuman mutation2026
Systematic Pathway Screening via Integrated Machine Learning Identifies FOXO-Mediated Transcription Signature for Robust Immunotherapy Response Prediction in Non-Small Cell Lung Cancer.
Article in Human mutation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Non-small cell lung cancer (NSCLC), accounting for 80% of lung cancer cases, remains a leading cause of cancer-related mortality globally. While immune checkpoint inhibitors (ICIs) have improved outcomes, their efficacy is limited to a subset of patients, necessitating robust biomarkers for personalized immunotherapy response prediction. Methods: We integrated transcriptomic data from 584 NSCLC patients across four cohorts treated with ICIs. Using 12,025 pathways from MSigDB, we applied 101 machine learning algorithm combinations (e.g., random survival forest [RSF], least absolute shrinkage and selection operator [Lasso], and Cox proportional hazards model with component-wise likelihood-based boosting [CoxBoost]) to identify prognostic signatures. OAK was used as the training set and Ravi, Jung, and Poplar as the validation set. The optimal pathway and algorithm combination was determined based on the average concordance index ( Results: The FOXO-mediated transcription pathway combined with Lasso-RSF algorithms emerged as the top predictor. The derived FOXO-related signature (FRS) stratified patients into high-risk and low-risk groups, with high-risk patients showing significantly worse progression-free survival (PFS) and overall survival (OS) across all cohorts ( Conclusion: FRS, a machine learning-derived pathway signature, robustly predicts immunotherapy response and survival in NSCLC. Its integration of FOXO-mediated immune regulation offers a clinically translatable tool for precision oncology.
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