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  A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment

Thedinga, K., & Herwig, R. (2021). A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment. iScience, 25(1): 103617. doi:10.1016/j.isci.2021.103617.

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Thedinga_2021.pdf (Verlagsversion), 3MB
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© 2021 The Authors

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 Urheber:
Thedinga, Kristina1, Autor           
Herwig, Ralf1, Autor           
Affiliations:
1Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385701              

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 Zusammenfassung: 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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Sprache(n): eng - English
 Datum: 2021-12-10
 Publikationsstatus: Online veröffentlicht
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 Identifikatoren: DOI: 10.1016/j.isci.2021.103617
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Titel: iScience
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis : Elsevier
Seiten: - Band / Heft: 25 (1) Artikelnummer: 103617 Start- / Endseite: - Identifikator: ISSN: 2589-0042
CoNE: https://pure.mpg.de/cone/journals/resource/2589-0042