ArticleiScience2022
A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine.Epigenetics & chromatin · 2025Review
- Cancer-Associated Fibroblasts Together with a Decline in CD8+ T Cells Predict a Worse Prognosis for Breast Cancer Patients.Annals of surgical oncology · 2024Article
- [Screening of immune related gene and survival prediction of lung adenocarcinoma patients based on LightGBM model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.Journal of clinical medicine · 2023Review
- Gradient tree boosting and network propagation for the identification of pan-cancer survival networks.STAR protocols · 2022Article
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Authors and funding
2 authors.
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Abstract
Predicting cancer survival from molecular data is an important aspect of biomedical research because it allows quantifying patient risks and thus individualizing therapy. We introduce XGBoost tree ensemble learning to predict survival from transcriptome data of 8,024 patients from 25 different cancer types and show highly competitive performance with state-of-the-art methods. To further improve plausibility of the machine learning approach we conducted two additional steps. In the first step, we applied pan-cancer training and showed that it substantially improves prognosis compared with cancer subtype-specific training. In the second step, we applied network propagation and inferred a pan-cancer survival network consisting of 103 genes. This network highlights cross-cohort features and is predictive for the tumor microenvironment and immune status of the patients. Our work demonstrates that pan-cancer learning combined with network propagation generalizes over multiple cancer types and identifies biologically plausible features that can serve as biomarkers for monitoring cancer survival.
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Registered trials
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