Analysis of Matched Tumor and Normal Profiles Reveals Common Transcriptional and Epigenetic Signals Shared across Cancer Types.

TitleAnalysis of Matched Tumor and Normal Profiles Reveals Common Transcriptional and Epigenetic Signals Shared across Cancer Types.
Publication TypeJournal Article
Year of Publication2015
AuthorsGross AM, Kreisberg JF, Ideker T
JournalPLoS One
Volume10
Issue11
Paginatione0142618
Date Published2015
ISSN1932-6203
Abstract

To identify the transcriptional regulatory changes that are most widespread in solid tumors, we performed a pan-cancer analysis using over 600 pairs of tumors and adjacent normal tissues profiled in The Cancer Genome Atlas (TCGA). Frequency of upregulation was calculated across mRNA expression levels, microRNA expression levels and CpG methylation sites and is provided here as a resource. Frequent tumor-associated alterations were identified using a simple statistical approach. Many of the identified changes were consistent with the increased rate of cell division in cancer, such as the overexpression of cell cycle genes and hypermethylation of PRC2 binding sites. However, we also identified proliferation-independent alterations, which highlight novel pathways essential to tumor formation. Nearly all of the GABA receptors are frequently downregulated, with the gene encoding the delta subunit (GABRD) strongly upregulated as the notable exception. Metabolic genes are also frequently downregulated, particularly alcohol dehydrogenases and others consistent with the decreased role of oxidative phosphorylation in cancerous cells. Alterations in the composition of GABA receptors and metabolism may play a key role in the differentiation of cancer cells, independent of proliferation.

DOI10.1371/journal.pone.0142618
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/26555223?dopt=Abstract
Alternate JournalPLoS ONE
PubMed ID26555223
PubMed Central IDPMC4640835
Grant ListP41 GM103504-04 / GM / NIGMS NIH HHS / United States
P50 GM085764 / GM / NIGMS NIH HHS / United States
P50 GM085764 / GM / NIGMS NIH HHS / United States
Track(s): 
Bioinformatics and Systems Biology