Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes

Binhua Tang, Hang Kai Hsu, Pei Yin Hsu, Russell Bonneville, Su Shing Chen, Tim H.M. Huang, Victor X. Jin

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Abstract

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-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 ERα 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 ERα-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.

Original languageEnglish (US)
Article number875
JournalScientific reports
Volume2
DOIs
StatePublished - Dec 14 2012

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