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A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer

Author(s): Shen Changyu | Huang Yiwen | Liu Yunlong | Wang Guohua | Zhao Yuming | Wang Zhiping | Teng Mingxiang | Wang Yadong | Flockhart David | Skaar Todd | Yan Pearlly | Nephew Kenneth | Huang Tim | Li Lang

Journal: BMC Systems Biology
ISSN 1752-0509

Volume: 5;
Issue: 1;
Start page: 67;
Date: 2011;
Original page

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 (
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