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Updated: May 17, 2026
Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
Published on: March 17, 2016
A modulator based regulatory network for ERα signaling pathway
Heng-Yi Wu1, Pengyue Zheng, Guanglong Jiang
1Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, IN, USA. hengwu@umail.iu.edu
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. ERα 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 ERα on breast cancer.
Methods:
To investigate the regulatory network of ERα and discover novel modulators of ERα 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 ERα binding. Network formed from targets genes with ERα binding was called ERα genomic regulatory network; while network formed from targets genes without ERα binding was called ERα non-genomic regulatory network. Considering the active or repressive function of ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ERα, the ERα/modulator/target relationships were categorized into 27 classes.
Results:
Using the gene expression data and ERα Chip-seq data from the MCF-7 cell line, the ERα genomic/non-genomic regulatory networks were built by merging ERα/ modulator/target triplets (TF, M, T), where TF refers to the ERα, M refers to the modulator, and T refers to the target. Comparing these two networks, ERα 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 ERα genomic regulatory network, but 4% overlap for the non-genomic regulatory network.
Conclusions:
We proposed a novel approach to infer the ERα/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.
Insights
We developed a new method to map estrogen receptor alpha (ERα) regulatory networks. The non-genomic ERα network proved more reliable than the genomic network in breast cancer cells.
Area of Science:
- Molecular Biology
- Genomics
- Cancer Research
Background:
- Estrogen receptor alpha (ERα) critically regulates gene expression in hormone-responsive breast cancer.
- ERα mediates its effects through complex genomic and non-genomic mechanisms involving co-regulators.
- Understanding ERα's regulatory network is vital for studying breast cancer progression.
Purpose of the Study:
- To investigate the ERα regulatory network and identify novel modulators of ERα function.
- To develop and apply an analytical method for constructing ERα genomic and non-genomic regulatory networks.
- To categorize ERα/modulator/target relationships based on various functional parameters.
Main Methods:
- Proposed a linear regression model to identify translational modulators and their network relationships.
- Constructed ERα genomic and non-genomic regulatory networks using gene expression and ERα Chip-seq data from MCF-7 cells.
- Validated the networks by analyzing gene expression data from ZR-75.1 cells and comparing network overlaps.
Main Results:
- Successfully built ERα genomic and non-genomic regulatory networks by analyzing ERα/modulator/target triplets.
- The ERα non-genomic network exhibited a lower False Discovery Rate (FDR) compared to the genomic network.
- Network overlap analysis revealed a higher consistency (4%) for the non-genomic network versus the genomic network (1%) between MCF-7 and ZR-75.1 cell lines.
Conclusions:
- Introduced a novel approach to infer ERα/modulator/target relationships and construct regulatory networks.
- Demonstrated that the non-genomic regulatory network is more reliable and consistent across different breast cancer cell lines.
- The findings provide a foundation for systematic studies on ERα's role in breast cancer.
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