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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The\n                      <jats:bold>\n                        <jats:underline>i<\/jats:underline>\n                      <\/jats:bold>\n                      ntegrative\n                      <jats:bold>\n                        <jats:underline>Glio<\/jats:underline>\n                      <\/jats:bold>\n                      blastoma\n                      <jats:bold>\n                        <jats:underline>Sub<\/jats:underline>\n                      <\/jats:bold>\n                      type (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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13040-021-00273-8","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T07:03:14Z","timestamp":1629702194000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes"],"prefix":"10.1186","volume":"14","author":[{"given":"Miquel","family":"Ensenyat-Mendez","sequence":"first","affiliation":[]},{"given":"Sandra","family":"\u00cd\u00f1iguez-Mu\u00f1oz","sequence":"additional","affiliation":[]},{"given":"Borja","family":"Ses\u00e9","sequence":"additional","affiliation":[]},{"given":"Diego M.","family":"Marzese","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"issue":"Suppl 5","key":"273_CR1","first-page":"v1","volume":"14","author":"TA Dolecek","year":"2012","unstructured":"Dolecek TA, Propp JM, Stroup NE, Kruchko C. 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Written consent was obtained from all patients selected. Every sample was deidentified and coded following the Health Insurance Portability and Accountability Act (HIPAA) guidelines.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"42"}}