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Hierarchical modularity in ER? transcriptional network is associated with distinct functions and implicates clinical outcomes.


ABSTRACT: Recent genome-wide profiling reveals highly complex regulation networks among ER? and its targets. We integrated estrogen (E2)-stimulated time-series ER? ChIP-seq and gene expression data to identify the ER?-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ER? core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ER?-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

SUBMITTER: Tang B 

PROVIDER: S-EPMC3500769 | BioStudies | 2012-01-01

REPOSITORIES: biostudies

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