Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells

Niida, A., Smith, A. D., Imoto, S., Tsutsumi, S., Aburatani, H., Zhang, M. Q., Akiyama, T. (September 2008) Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells. BMC Bioinformatics, 9. p. 404.

[img]
Preview
PDF (Paper)
Zhang BMC Bioinformatics 2008b.pdf - Published Version

Download (778Kb) | Preview
URL: http://www.ncbi.nlm.nih.gov/pubmed/18823535
DOI: 10.1186/1471-2105-9-404

Abstract

Microarray technology has unveiled transcriptomic differences among tumors of various phenotypes, and, especially, brought great progress in molecular understanding of phenotypic diversity of breast tumors. However, compared with the massive knowledge about the transcriptome, we have surprisingly little knowledge about regulatory mechanisms underling transcriptomic diversity. RESULTS: To gain insights into the transcriptional programs that drive tumor progression, we integrated regulatory sequence data and expression profiles of breast cancer into a Bayesian Network, and searched for cis-regulatory motifs statistically associated with given histological grades and prognosis. Our analysis found that motifs bound by ELK1, E2F, NRF1 and NFY are potential regulatory motifs that positively correlate with malignant progression of breast cancer. CONCLUSION: The results suggest that these 4 motifs are principal regulatory motifs driving malignant progression of breast cancer. Our method offers a more concise description about transcriptome diversity among breast tumors with different clinical phenotypes.

Item Type: Paper
Subjects: bioinformatics
diseases & disorders > cancer
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > transcription
diseases & disorders
bioinformatics > genomics and proteomics > genetics & nucleic acid processing
bioinformatics > genomics and proteomics
diseases & disorders > cancer > cancer types > breast cancer
diseases & disorders > cancer > cancer types
CSHL Authors:
Communities: CSHL labs > Zhang lab
Depositing User: Matt Covey
Date: 29 September 2008
Date Deposited: 26 Feb 2013 17:39
Last Modified: 26 Feb 2013 17:39
PMCID: PMC2572072
Related URLs:
URI: http://repository.cshl.edu/id/eprint/27552

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving