Research article

Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells

Atsushi Niida¹ , Andrew D Smith² , Seiya Imoto³ , Shuichi Tsutsumi⁴ , Hiroyuki Aburatani⁴ , Michael Q Zhang² and Tetsu Akiyama¹

¹  Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 110-0032, Japan

²  Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11274, USA

³  The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

⁴  Genome Science Division, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, 153-8904, Japan

author email corresponding author email

BMC Bioinformatics 2008, 9:404doi:10.1186/1471-2105-9-404

Published:	29 September 2008

Abstract

Background

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.