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Genomic evidence based on eQTL data implicates endocrine disruptors as environmental risk factors for estrogen receptor-positive breast cancer

乳腺癌 表达数量性状基因座 雌激素受体 孟德尔随机化 癌症 医学 雌激素受体α 全基因组关联研究 基因 生物 肿瘤科 内科学 癌症研究 生物信息学 遗传学 单核苷酸多态性 基因型 遗传变异
作者
Yanggang Hong,Jiajun Li,Zepeng Du,Nuo Xu,Qianru Yang,Jing-Xuan Zhou,Wanyi Shu
出处
期刊:International Journal of Surgery [Elsevier]
卷期号:111 (8): 5664-5674 被引量:5
标识
DOI:10.1097/js9.0000000000002642
摘要

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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