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Updated: Sep 17, 2025

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
Published on: March 17, 2016
Genomic evidence based on eQTL data implicates endocrine disruptors as environmental risk factors for estrogen
Yanggang Hong1,2,3, Jiajun Li1,2,3, Zhuoyi Du3
1The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Background:
Estrogen receptor-positive (ER+) breast cancer is the most common molecular subtype of breast cancer and is strongly influenced by hormonal and environmental factors. Endocrine-disrupting chemicals (EDCs), which interfere with hormone signaling, have been suggested to contribute to ER+ breast cancer risk, but causal mechanisms remain unclear.
Methods:
We integrated chemical-gene interaction data from the TEDX and CTD databases with large-scale genomic datasets to investigate the relationship between EDC-regulated gene expression and ER+ breast cancer. A total of 5797 EDC-related genes were identified and filtered using cis-expression quantitative trait loci (cis-eQTL) data from eQTLGen. Mendelian randomization (MR) and colocalization analyses were performed using ER+ breast cancer GWAS summary statistics to assess causal associations and shared genetic signals. Interacting EDCs were mapped to colocalized genes.
Results:
Among 4207 genes with available cis-eQTLs, 50 showed statistically significant associations (FDR < 0.05) with ER+ breast cancer. Of these, 24 genes, including CIRBP, JMJD1C, and TET2, demonstrated strong evidence of colocalization. Key EDCs, such as bisphenol A and phthalates, were identified to interact with multiple high-risk genes, suggesting potential environmental drivers of ER+ breast cancer.
Conclusion:
This study provides genetic evidence supporting the causal role of EDC-regulated gene expression in ER+ breast cancer. The integration of MR, colocalization, and chemical-gene networks offers a novel framework for identifying environmentally relevant risk factors and contributes to understanding the gene-environment mechanisms underlying hormone-dependent cancers.
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