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Master regulators, regulatory networks, and pathways of glioblastoma subtypes.

Bozdag S, Li A, Baysan M, Fine HA - Cancer Inform (2014)

Bottom Line: 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.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.Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

View Article: PubMed Central - PubMed

Affiliation: Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA. ; Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, Wisconsin, USA.

ABSTRACT
Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic 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. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

No MeSH data available.


Related in: MedlinePlus

IPA network built from master regulators of GBM subtypes computed by FastMEDUSA. Colored nodes are the master regulators (Red: upregulated, Green: downregulated).
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Related In: Results  -  Collection


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f3-cin-suppl.3-2014-033: IPA network built from master regulators of GBM subtypes computed by FastMEDUSA. Colored nodes are the master regulators (Red: upregulated, Green: downregulated).

Mentions: To examine biological relationship among these TFs based on literature, we uploaded the master regulators into IPA and ran core analysis to build networks from these regulators. The top-scoring network was associated with functions of gene expression, cellular function and maintenance, and cellular growth and maintenance (Fig. 3).


Master regulators, regulatory networks, and pathways of glioblastoma subtypes.

Bozdag S, Li A, Baysan M, Fine HA - Cancer Inform (2014)

IPA network built from master regulators of GBM subtypes computed by FastMEDUSA. Colored nodes are the master regulators (Red: upregulated, Green: downregulated).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4214595&req=5

f3-cin-suppl.3-2014-033: IPA network built from master regulators of GBM subtypes computed by FastMEDUSA. Colored nodes are the master regulators (Red: upregulated, Green: downregulated).
Mentions: To examine biological relationship among these TFs based on literature, we uploaded the master regulators into IPA and ran core analysis to build networks from these regulators. The top-scoring network was associated with functions of gene expression, cellular function and maintenance, and cellular growth and maintenance (Fig. 3).

Bottom Line: 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.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.Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

View Article: PubMed Central - PubMed

Affiliation: Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA. ; Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, Wisconsin, USA.

ABSTRACT
Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic 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. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

No MeSH data available.


Related in: MedlinePlus