A modulator based regulatory network for ERa signaling pathway.

Background: Estrogens control multiple functions of hormone-responsive breast cancer cells. They 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. ERa requires distinct co-regulator or modulators for efficient transcriptional regulation, and they form a regulatory network. Knowing this regulatory network will enable systematic study of the effect of ERa on breast cancer. Methods: To investigate the regulatory network of ERa and discover novel modulators of ERa functions, we proposed an analytical method based on a linear regression model to identify translational modulators and their network relationships. In the network analysis, a group of specific modulator and target genes were selected according to the functionality of modulator and the ERa binding. Network formed from targets genes with ERa binding was called ERa genomic regulatory network; while network formed from targets genes without ERa binding was called ERa non-genomic regulatory network. Considering the active or repressive function of ERa, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ERa, the ERa/modulator/target relationships were categorized into 27 classes. Results: Using the gene expression data and ERa Chip-seq data from the MCF-7 cell line, the ERa genomic/nongenomic regulatory networks were built by merging ERa/ modulator/target triplets (TF, M, T), where TF refers to the ERa, M refers to the modulator, and T refers to the target. Comparing these two networks, ERa non-genomic network has lower FDR than the genomic network. In order to validate these two networks, the same network analysis was performed in the gene expression data from the ZR-75.1 cell. The network overlap analysis between two cancer cells showed 1% overlap for the ERa genomic regulatory network, but 4% overlap for the non-genomic regulatory network. Conclusions: We proposed a novel approach to infer the ERa/modulator/target relationships, and construct the genomic/non-genomic regulatory networks in two cancer cells. We found that the non-genomic regulatory network is more reliable than the genomic regulatory network. [ABSTRACT FROM AUTHOR]