Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.
Sci Rep
; 2: 875, 2012.
Article
in En
| MEDLINE
| ID: mdl-23166858
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Breast Neoplasms
/
Gene Expression Regulation, Neoplastic
/
Estrogen Receptor alpha
/
Gene Regulatory Networks
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Female
/
Humans
Language:
En
Journal:
Sci Rep
Year:
2012
Type:
Article
Affiliation country:
United States