Limits...
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

Venn diagram for (A) upregulated and (B) downregulated DEGs for each subtype with respect to normal samples. proneural GPOS: proneural+, proneural GNEG: proneural−.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4214595&req=5

f1-cin-suppl.3-2014-033: Venn diagram for (A) upregulated and (B) downregulated DEGs for each subtype with respect to normal samples. proneural GPOS: proneural+, proneural GNEG: proneural−.

Mentions: We computed the source of variation in the expression data by establishing a three-way ANOVA model, where subtype, institution ID, and batch ID were the covariates. We found out that 4.94% of the variation was due to the batch ID, and the source of variation due to institution ID was negligible (Supplementary Fig. 3). We removed batch effect that was due to the batch ID by using the batch effect removal module in Partek Genomics Suite. We applied one-way ANOVA to compute DEGs for each subtype with respect to normal samples using Partek (FDR ≤0.05 and fold change = 1.5). Figure 1 shows the Venn diagram of upregulated and downregulated genes for each subtype.


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

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

Venn diagram for (A) upregulated and (B) downregulated DEGs for each subtype with respect to normal samples. proneural GPOS: proneural+, proneural GNEG: proneural−.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-suppl.3-2014-033: Venn diagram for (A) upregulated and (B) downregulated DEGs for each subtype with respect to normal samples. proneural GPOS: proneural+, proneural GNEG: proneural−.
Mentions: We computed the source of variation in the expression data by establishing a three-way ANOVA model, where subtype, institution ID, and batch ID were the covariates. We found out that 4.94% of the variation was due to the batch ID, and the source of variation due to institution ID was negligible (Supplementary Fig. 3). We removed batch effect that was due to the batch ID by using the batch effect removal module in Partek Genomics Suite. We applied one-way ANOVA to compute DEGs for each subtype with respect to normal samples using Partek (FDR ≤0.05 and fold change = 1.5). Figure 1 shows the Venn diagram of upregulated and downregulated genes for each subtype.

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