TY  - JOUR
A1  - Garbulowski, Mateusz
A1  - Smolinska, Karolina
A1  - Çabuk, Uğur
A1  - Yones, Sara A.
A1  - Celli, Ludovica
A1  - Yaz, Esma Nur
A1  - Barrenas, Fredrik
A1  - Diamanti, Klev
A1  - Wadelius, Claes
A1  - Komorowski, Jan
T1  - Machine learning-based analysis of glioma grades reveals co-enrichment
T2  - Cancers
N2  - Simple Summary Gliomas are heterogenous types of cancer, therefore the therapy should be personalized and targeted toward specific pathways. We developed a methodology that corrected strong batch effects from The Cancer Genome Atlas datasets and estimated glioma grade-specific co-enrichment mechanisms using machine learning. Our findings created hypotheses for annotations, e.g., pathways, that should be considered as therapeutic targets. Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.
KW  - glioma
KW  - machine learning
KW  - batch effect
KW  - TCGA
KW  - co-enrichment
KW  - rough sets
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64619
SN  - 2072-6694
VL  - 14
IS  - 4
PB  - MDPI
CY  - Basel
ER  -