Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells

被引：30

作者：

Niida, Atsushi ^{[1
]}

Smith, Andrew D. ^{[2
]}

Imoto, Seiya ^{[3
]}

Tsutsumi, Shuichi ^{[4
]}

Aburatani, Hiroyuki ^{[4
]}

Zhang, Michael Q. ^{[2
]}

Akiyama, Tetsu ^{[1
]}

机构：

[1] Univ Tokyo, Inst Mol & Cellular Biosci, Lab Mol & Genet Informat, Bunkyo Ku, Tokyo 1100032, Japan

[2] Cold Spring Harbor Lab, Cold Spring Harbor, NY 11274 USA

[3] Univ Tokyo, Inst Med Sci, Minato Ku, Tokyo 1088639, Japan

[4] Univ Tokyo, Adv Sci & Technol Res Ctr, Genome Sci Div, Meguro Ku, Tokyo 1538904, Japan

来源：

BMC BIOINFORMATICS | 2008年 / 9卷 / 1期

基金：

日本学术振兴会;

关键词：

D O I：

10.1186/1471-2105-9-404

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：14

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