Marquette University e-Publications@Marquette Mathematics, Statistics and Computer Science, MSCS Faculty Research and Publications Department of 1-1-2014 Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes Serdar Bozdag Marquette University,
[email protected] Aiguo Li National Institutes of Health Mehmet Baysan National Institutes of Health Howard A. Fine National Institutes of Health Published version. Cancer Informatics, Vol. 13, S. 3 (2014): 33-44. DOI. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License. Open Access: Full open access to Cancer this and thousands of other papers at http://www.la-press.com. Informatics Supplementary Issue: Computational Advances in Cancer Informatics (B) Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes Serdar Bozdag1,2, Aiguo Li1, Mehmet Baysan1,3 and Howard A. Fine1,3 1Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA. 2Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, Wisconsin, USA. 3New York University Cancer Institute, New York University Langone Medical Center, New York, New York, USA. ABSTR ACT: Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their tran- scriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype- specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways.