Gene set-based module discovery in the breast cancer transcriptome

Niida, A., Smith, A. D., Imoto, S., Aburatani, H., Zhang, M. Q., Akiyama, T. (February 2009) Gene set-based module discovery in the breast cancer transcriptome. BMC Bioinformatics, 10. p. 71. ISSN 1471-2105

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URL: http://www.ncbi.nlm.nih.gov/pubmed/19243633
DOI: 10.1186/1471-2105-10-71

Abstract

Background: Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data. Results: In order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on cis-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2) is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells. Conclusion: These results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.

Item Type: Paper
Uncontrolled Keywords: NF-KAPPA-B EXPRESSION PROFILES MICROARRAY DATA INTERACTION NETWORKS REGULATORY NETWORKS DNA MICROARRAYS BINDING-SITES HUMAN GENOME PROTEIN-DNA STEM-CELLS
Subjects: bioinformatics > genomics and proteomics > analysis and processing
bioinformatics
diseases & disorders > cancer
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification
diseases & disorders
bioinformatics > genomics and proteomics > genetics & nucleic acid processing
bioinformatics > genomics and proteomics
bioinformatics > genomics and proteomics > analysis and processing > microarray gene expression processing
diseases & disorders > cancer > cancer types > breast cancer
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function
diseases & disorders > cancer > cancer types
CSHL Authors:
Communities: CSHL labs > Zhang lab
CSHL Cancer Center Shared Resources > Bioinformatics Service
Depositing User: Matt Covey
Date: 26 February 2009
Date Deposited: 21 Feb 2013 14:29
Last Modified: 30 Dec 2014 15:13
PMCID: PMC2674431
Related URLs:
URI: http://repository.cshl.edu/id/eprint/27384

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