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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

Kaplan–Meier survival plots based on the expression status of four master regulators of GBM subtypes computed by FastMEDUSA. Each regulator name is below the survival plot. Log-rank P < 0.05 for each plot.
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f2-cin-suppl.3-2014-033: Kaplan–Meier survival plots based on the expression status of four master regulators of GBM subtypes computed by FastMEDUSA. Each regulator name is below the survival plot. Log-rank P < 0.05 for each plot.

Mentions: We ran FastMEDUSA five times with a unique random seed each time and obtained five different models. We post-processed these models to compute significant TFs (ie, master regulators) (see Materials and Methods section). The lists of significant upregulated and downregulated master regulators for each subtype are shown in Tables 1 and 2, respectively. Among these TFs, some of them are known previously to have a role in GBM such as CEBPD,21 RUNX1,21 LEF1,22 HES6,23 ASCL1,24 EBF1,25,26 SP100,27 and AEBP1.28 To check the effects of these TFs on survival, we computed survival plots of these TFs based on GBM gene expression data in REMBRANDT database and found out that some of these TFs have significant survival difference based on their expression status (Fig. 2 and Supplementary Fig. 4).


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

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

Kaplan–Meier survival plots based on the expression status of four master regulators of GBM subtypes computed by FastMEDUSA. Each regulator name is below the survival plot. Log-rank P < 0.05 for each plot.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2-cin-suppl.3-2014-033: Kaplan–Meier survival plots based on the expression status of four master regulators of GBM subtypes computed by FastMEDUSA. Each regulator name is below the survival plot. Log-rank P < 0.05 for each plot.
Mentions: We ran FastMEDUSA five times with a unique random seed each time and obtained five different models. We post-processed these models to compute significant TFs (ie, master regulators) (see Materials and Methods section). The lists of significant upregulated and downregulated master regulators for each subtype are shown in Tables 1 and 2, respectively. Among these TFs, some of them are known previously to have a role in GBM such as CEBPD,21 RUNX1,21 LEF1,22 HES6,23 ASCL1,24 EBF1,25,26 SP100,27 and AEBP1.28 To check the effects of these TFs on survival, we computed survival plots of these TFs based on GBM gene expression data in REMBRANDT database and found out that some of these TFs have significant survival difference based on their expression status (Fig. 2 and Supplementary Fig. 4).

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