title: Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp
authors.forename: Atsushi
authors.initials: A
authors.lastname: Niida
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Smith
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Shuichi
authors.initials: S
authors.lastname: Tsutsumi
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Aburatani
authors.forename: Michael Q
authors.initials: MQ
authors.lastname: Zhang
authors.forename: Tetsu
authors.initials: T
authors.lastname: Akiyama
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D023081:CCAAT-Binding Factor / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016384:Consensus Sequence / Q000502:physiology; D018450:Disease Progression; D050704:E2F4 Transcription Factor / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D046228:Microarray Analysis / Q000379:methods; D051266:Nuclear Respiratory Factor 1 / Q000378:metabolism; D009690:Nucleic Acid Conformation; D011379:Prognosis; D050436:Regulatory Elements, Transcriptional / Q000502:physiology; D012045:Regulatory Sequences, Nucleic Acid / Q000502:physiology; D014158:Transcription, Genetic / Q000502:physiology; D050807:ets-Domain Protein Elk-1 / Q000378:metabolism
chemical_list: D023081:CCAAT-Binding Factor; D050704:E2F4 Transcription Factor; D051266:Nuclear Respiratory Factor 1; D050807:ets-Domain Protein Elk-1; C402244:nuclear factor Y