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

Abstract

BackgroundEstrogens 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.ResultsWe 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.ConclusionsWe 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.

Network-Based Inference Framework for Identifying Cancer Genes from Gene Expression Data

“…As previous investigations have shown [17, 20], many hubs in differential interaction network serve as key components of cancer-associated pathways and can be used to discover cancer-induced genes. Our experimental results on breast cancer data also demonstrated that two such differential network hubs, DCK and BBC3 , link to MAPK signaling pathway and metastasis, as reported in [21, 22].…”

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME

“…In addition, a catalogue of direct binding targets of PPARα with functional PPREs in their promoter was used (Mandard et al 2004; Heinaniemi et al 2007; Rakhshandehroo et al 2010) to assign differentially expressed genes to three groups based on their mechanism of regulation (Shen et al 2011):Direct genomic binding (DGB), where PPARα induces gene transcription by binding to PPREs in the promoters of target genes, corresponding to the “canonical” mode of action.Indirect genomic binding (IDGB), whereby PPARα is bound to promoter regions of target genes, but not to PPREs—presumably through protein–protein interactions with other transcription factors that directly bind DNA. This “tethering” mechanism has been observed with other NRs like the GR and ER, where it may account for the regulation of 25–30 % of target genes (George et al 2011; Heldring et al 2007; So et al 2007; Stender et al 2010).…”

Information-dependent enrichment analysis reveals time-dependent transcriptional regulation of the estrogen pathway of toxicity

“…Predicting the transcription factors responsible for a cellular response would significantly contribute to PoT identification (Essaghir et al 2010; Shen et al 2011; Maertens et al 2015). However, traditional approaches for identifying transcription factors from gene expression patterns use data from a small subset of the genome.…”