Identifying glioblastoma gene networks based on hypergeometric test analysis

Vasileios Stathias, Chiara Pastori, Tess Z. Griffin, Ricardo Komotar, Jennifer Clarke, Ming Zhang, Nagi G.Ayad Ayad

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA). To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM). We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers.

Original languageEnglish (US)
Article numbere115842
JournalPloS one
Volume9
Issue number12
DOIs
StatePublished - Dec 31 2014

Fingerprint

Gene Regulatory Networks
Glioblastoma
Genes
neoplasms
Tumors
Neoplasms
testing
Atlases
Genome
RNA Sequence Analysis
Workflow
gene regulatory networks
therapeutics
genome
genes
Bioinformatics
Computational Biology
Brain Neoplasms
bioinformatics
sampling

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Cite this

Stathias, V., Pastori, C., Griffin, T. Z., Komotar, R., Clarke, J., Zhang, M., & Ayad, N. G. A. (2014). Identifying glioblastoma gene networks based on hypergeometric test analysis. PloS one, 9(12), [e115842]. https://doi.org/10.1371/journal.pone.0115842

Identifying glioblastoma gene networks based on hypergeometric test analysis. / Stathias, Vasileios; Pastori, Chiara; Griffin, Tess Z.; Komotar, Ricardo; Clarke, Jennifer; Zhang, Ming; Ayad, Nagi G.Ayad.

In: PloS one, Vol. 9, No. 12, e115842, 31.12.2014.

Research output: Contribution to journalArticle

Stathias, V, Pastori, C, Griffin, TZ, Komotar, R, Clarke, J, Zhang, M & Ayad, NGA 2014, 'Identifying glioblastoma gene networks based on hypergeometric test analysis', PloS one, vol. 9, no. 12, e115842. https://doi.org/10.1371/journal.pone.0115842
Stathias V, Pastori C, Griffin TZ, Komotar R, Clarke J, Zhang M et al. Identifying glioblastoma gene networks based on hypergeometric test analysis. PloS one. 2014 Dec 31;9(12). e115842. https://doi.org/10.1371/journal.pone.0115842
Stathias, Vasileios ; Pastori, Chiara ; Griffin, Tess Z. ; Komotar, Ricardo ; Clarke, Jennifer ; Zhang, Ming ; Ayad, Nagi G.Ayad. / Identifying glioblastoma gene networks based on hypergeometric test analysis. In: PloS one. 2014 ; Vol. 9, No. 12.
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