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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

Heatmap of GO term enrichment of genes in FastMEDUSA gene regulatory networks. Only the significant P values are colored via log transformation (White: no significance, Blue: low enrichment, Red: high enrichment). GO terms are categorized into more general terms in rows.
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f5-cin-suppl.3-2014-033: Heatmap of GO term enrichment of genes in FastMEDUSA gene regulatory networks. Only the significant P values are colored via log transformation (White: no significance, Blue: low enrichment, Red: high enrichment). GO terms are categorized into more general terms in rows.

Mentions: We checked the functional enrichment of genes in each network and plotted the enrichment P values in a heatmap (Fig. 5). GO terms related to TF activity, regulation of transcription, and metabolic processes were common in all subtypes. Mesenchymal group was uniquely enriched in GO terms related to immune response, response to stimulus, response to hypoxia, signal transduction, and anti-apoptosis. Apoptosis and angiogenesis terms were enriched in both mesenchymal and neural networks. GO terms related to RNA localization were enriched in both classical and proneural−. The classical group network was also uniquely enriched with GO terms related to negative regulation of transcription.


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

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

Heatmap of GO term enrichment of genes in FastMEDUSA gene regulatory networks. Only the significant P values are colored via log transformation (White: no significance, Blue: low enrichment, Red: high enrichment). GO terms are categorized into more general terms in rows.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5-cin-suppl.3-2014-033: Heatmap of GO term enrichment of genes in FastMEDUSA gene regulatory networks. Only the significant P values are colored via log transformation (White: no significance, Blue: low enrichment, Red: high enrichment). GO terms are categorized into more general terms in rows.
Mentions: We checked the functional enrichment of genes in each network and plotted the enrichment P values in a heatmap (Fig. 5). GO terms related to TF activity, regulation of transcription, and metabolic processes were common in all subtypes. Mesenchymal group was uniquely enriched in GO terms related to immune response, response to stimulus, response to hypoxia, signal transduction, and anti-apoptosis. Apoptosis and angiogenesis terms were enriched in both mesenchymal and neural networks. GO terms related to RNA localization were enriched in both classical and proneural−. The classical group network was also uniquely enriched with GO terms related to negative regulation of transcription.

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