title: A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer.
abstract: BACKGROUND
  Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood.
  
  
  RESULTS
  We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF) regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct (<5% overlap of ERα target genes between the 4 and 24 h time points), all nine hubs were significantly represented in both networks. In MCF7 cells with acquired resistance to tamoxifen, the ERα regulatory network was unresponsive to 17β-estradiol stimulation. The significant loss of hormone responsiveness was associated with marked epigenomic changes, including hyper- or hypo-methylation of promoter CpG islands and repressive histone methylations.
  
  
  CONCLUSIONS
  We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.
authors.affiliation: Center for Computational Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
authors.forename: Changyu
authors.initials: C
authors.lastname: Shen
authors.forename: Yiwen
authors.initials: Y
authors.lastname: Huang
authors.forename: Yunlong
authors.initials: Y
authors.lastname: Liu
authors.forename: Guohua
authors.initials: G
authors.lastname: Wang
authors.forename: Yuming
authors.initials: Y
authors.lastname: Zhao
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Wang
authors.forename: Mingxiang
authors.initials: M
authors.lastname: Teng
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
authors.forename: David A
authors.initials: DA
authors.lastname: Flockhart
authors.forename: Todd C
authors.initials: TC
authors.lastname: Skaar
authors.forename: Pearlly
authors.initials: P
authors.lastname: Yan
authors.forename: Kenneth P
authors.initials: KP
authors.lastname: Nephew
authors.forename: Tim Hm
authors.initials: TH
authors.lastname: Huang
authors.forename: Lang
authors.initials: L
authors.lastname: Li
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism*; D045744:Cell Line, Tumor; D019008:Drug Resistance, Neoplasm / Q000187:drug effects; D044127:Epigenesis, Genetic / Q000187:drug effects; D047628:Estrogen Receptor alpha / Q000819:agonists / Q000037:antagonists & inhibitors / Q000378:metabolism*; D053263:Gene Regulatory Networks / Q000187:drug effects; D023281:Genomics; D006657:Histones / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D008239:Lysine / Q000378:metabolism; D008745:Methylation / Q000187:drug effects; D008957:Models, Genetic; D012319:RNA Polymerase II / Q000378:metabolism; D015203:Reproducibility of Results; D013629:Tamoxifen / Q000031:analogs & derivatives / Q000378:metabolism / Q000494:pharmacology; D013997:Time Factors
chemical_list: D047628:Estrogen Receptor alpha; D006657:Histones; D013629:Tamoxifen; C016601:afimoxifene; C055492:4-hydroxy-N-desmethyltamoxifen; D012319:RNA Polymerase II; D008239:Lysine