Estrogen Metabolism in Breast Cancer Cases and Controls.

1995 ◽  
Author(s):  
Giske Ursin
Keyword(s):  
Breast Cancer ◽  
Estrogen Metabolism

scholarly journals Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2370
Author(s):  
JooYong Park ◽  
Ji-Yeob Choi ◽  
Jaesung Choi ◽  
Seokang Chung ◽  
Nan Song ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Environmental Factors ◽  
Minor Allele ◽  
Estrogen Metabolism ◽  
Age At Menarche ◽  
Genome Wide Association Studies ◽  
Estrogen Exposure ◽  
Gene Environment ◽  
Independent Population

In this study we aim to examine gene–environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10−3). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10−4). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.

scholarly journals Discovery of breast cancer risk genes and establishment of a prediction model based on estrogen metabolism regulation

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Feng Zhao ◽  
Zhixiang Hao ◽  
Yanan Zhong ◽  
Yinxue Xu ◽  
Meng Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Healthy Subjects ◽  
Metabolic Enzyme ◽  
Estrogen Metabolism ◽  
Polygenic Risk Score ◽  
Risk Genes ◽  
Polygenic Risk ◽  
Enzyme Gene

Abstract Background Multiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes, which was calculated using a Bayesian approach. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve. Results The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results showed that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than that of M1 (AUC = 54.56%). Conclusion Estrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.

scholarly journals Associations between Dietary Fiber, the Fecal Microbiota and Estrogen Metabolism in Postmenopausal Women with Breast Cancer

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ayse G. Zengul ◽  
Wendy Demark-Wahnefried ◽  
Stephen Barnes ◽  
Casey D. Morrow ◽  
Brenda Bertrand ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Postmenopausal Women ◽  
Dietary Fiber ◽  
Fecal Microbiota ◽  
Estrogen Metabolism

scholarly journals Estrogen Deprivation and Inhibition of Breast Cancer Growth in Vivo through Activation of the Orphan Nuclear Receptor Liver X Receptor

2007 ◽  
Vol 21 (8) ◽  
pp. 1781-1790 ◽  
Author(s):  
Haibiao Gong ◽  
Ping Guo ◽  
Yonggong Zhai ◽  
Jie Zhou ◽  
Hirdesh Uppal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Liver X Receptor ◽  
Cancer Growth ◽  
Estrogen Metabolism ◽  
Endocrine Disorders ◽  
Estrogen Deprivation ◽  
Uterine Epithelial Cell ◽  
Hepatic Expression ◽  
Breast Cancer Growth

Abstract Estrogen plays an important role in normal physiology. It is also a risk factor for breast cancer, and antiestrogen therapies have been shown to be effective in the treatment and prevention of breast cancers. The liver is important for estrogen metabolism, and a compromised liver function has been linked to hyperestrogenism in patients. In this report, we showed that the liver X receptor (LXR) controls estrogen homeostasis by regulating the basal and inducible hepatic expression of estrogen sulfotransferase (Est, or Sult1e1), an enzyme critical for metabolic estrogen deactivation. Genetic or pharmacological activation of LXR resulted in Est induction, which in turn inhibited estrogen-dependent uterine epithelial cell proliferation and gene expression, as well as breast cancer growth in a nude mouse model of tumorigenicity. We further established that Est is a transcriptional target of LXR, and deletion of the Est gene in mice abolished the LXR effect on estrogen deprivation. Interestingly, Est regulation by LXR appeared to be liver specific, further underscoring the role of liver in estrogen metabolism. Activation of LXR failed to induce other major estrogen-metabolizing enzymes, suggesting that the LXR effect on estrogen metabolism is Est specific. In summary, our results have revealed a novel mechanism controlling estrogen homeostasis in vivo and may have implications for drug development in the treatment of breast cancer and other estrogen-related cancerous endocrine disorders.

scholarly journals Association between polymorphisms in estrogen metabolism genes and breast cancer development in Chinese women

Medicine ◽  
2018 ◽  
Vol 97 (47) ◽  
pp. e13337 ◽  
Author(s):  
Juanjuan Qiu ◽  
Zhenggui Du ◽  
Jingping Liu ◽  
Yi Zhou ◽  
Faqing Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Chinese Women ◽  
Cancer Development ◽  
Estrogen Metabolism ◽  
Breast Cancer Development

scholarly journals Epidemiologic studies of estrogen metabolism and breast cancer

Steroids ◽  
2015 ◽  
Vol 99 ◽  
pp. 67-75 ◽  
Author(s):  
Regina G. Ziegler ◽  
Barbara J. Fuhrman ◽  
Steven C. Moore ◽  
Charles E. Matthews
Keyword(s):  
Breast Cancer ◽  
Epidemiologic Studies ◽  
Estrogen Metabolism

scholarly journals Estrogen regulation in human breast cancer cells of new downstream gene targets involved in estrogen metabolism, cell proliferation and cell transformation

2004 ◽  
Vol 32 (2) ◽  
pp. 397-414 ◽  
Author(s):  
JA Vendrell ◽  
F Magnino ◽  
E Danis ◽  
MJ Duchesne ◽  
S Pinloche ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Proliferation ◽  
Time Course ◽  
Cell Transformation ◽  
Expression Profiles ◽  
Estrogen Metabolism ◽  
Breast Carcinoma Cell Line ◽  
Human Breast ◽  
Estrogen Regulation

We explored, by cDNA mini-arrays, gene expression measurements of MVLN, a human breast carcinoma cell line derived from MCF-7, after 4 days of exposure to 17beta-estradiol (E(2)) treatment, in order to extend our understanding of the mechanism of the pharmacological action of estrogens. We focused on 22 genes involved in estrogen metabolism, cell proliferation regulation and cell transformation. The specificity of the E(2) response was reinforced by comparison with 4-hydroxytamoxifen (OH-Tam), ICI 182,780 and E(2)+OH-Tam expression profiles. Real-time quantitative PCR (RTQ-PCR) confirmed the variation of expression of known (TFF1, AREG, IRS1, IGFBP4, PCNA, ERBB2, CTSD, MYC) as well as novel (DLEU2, CCNA2, UGT1A1, ABCC3, ABCC5, TACC1, EFNA1, NOV, CSTA, MMP15, ZNF217) genes. The temporal response of these gene expression regulations was then investigated after 6 and 18 h of E(2) treatment and this allowed the identification of different time-course patterns. Cycloheximide treatment studies indicated first that estrogen affected the transcript levels of ABCC3 and ABCC5 through dissimilar pathways, and secondly that protein synthesis was needed for modulation of the expression of the CCNA2 and TACC1 genes by estrogens. Western blot analysis performed on TFF1, IRS1, IGFBP4, amphiregulin, PCNA, cyclin A2, TACC1 and ABCC5 proteins confirmed the mini-array and RTQ-PCR data, even for genes harboring low variations of mRNA expression. Our findings should enhance the understanding of changes induced by E(2) on the transcriptional program of human E(2)-responsive cells and permit the identification of new potential diagnostic/prognostic tools for the monitoring of estrogen-related disease conditions such as breast cancer.

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