2026-05-22T05:28:10Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/231482024-11-28T23:36:18Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Ensenyat-Mendez, Miquel author Iñiguez-Muñoz, Sandra author Sese, Borja author Marzese, Diego M author 2021-08-23 Background: Glioblastoma (GBM) is the most aggressive and prevalent primary brain tumor, with a median survival of 15 months. Advancements in multi-omics profiling combined with computational algorithms have unraveled the existence of three GBM molecular subtypes (Classical, Mesenchymal, and Proneural) with clinical relevance. However, due to the costs of high-throughput profiling techniques, GBM molecular subtyping is not currently employed in clinical settings. Methods: Using Random Forest and Nearest Shrunken Centroid algorithms, we constructed transcriptomic, epigenomic, and integrative GBM subtype-specific classifiers. We included gene expression and DNA methylation (DNAm) profiles from 304 GBM patients profiled in the Cancer Genome Atlas (TCGA), the Human Glioblastoma Cell Culture resource (HGCC), and other publicly available databases. Results The integrative Glioblastoma Subtype (iGlioSub) classifier shows better performance (mean AUC = 95.9%) stratifying patients than gene expression (mean AUC = 91.9%) and DNAm-based classifiers (AUC = 93.6%). Also, to expand the understanding of the molecular differences between the GBM subtypes, this study shows that each subtype presents unique DNAm patterns and gene pathway activation. Conclusions: The iGlioSub classifier provides the basis to design cost-effective strategies to stratify GBM patients in routine pathology laboratories for clinical trials, which will significantly accelerate the discovery of more efficient GBM subtype-specific treatment approaches. Ensenyat-Mendez M, iniguez-Munoz S, Sese B, Marzese DM. iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes. Biodata Min. 2021 Aug 23;14(1):42. 10.1186/s13040-021-00273-8 1756-0381 Biodata Mining https://hdl.handle.net/20.500.13003/19630 34425860 L2013512941 2-s2.0-85113724008 https://hdl.handle.net/20.500.12105/23148 687692700001 Glioblastoma Machine learning Molecular subtypes Epigenetics iGlioSub Cancer Integrative classifier DNA methylation Gene expression iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes