Association of Single Nucleotide Polymorphisms in CYP1B1 and COMT Genes with Breast Cancer Susceptibility in Indian Women

Cytochrome P450 1B1 (CYP1B1) and catechol-$O$-methyltransferase (COMT) enzymes play critical roles in estrogen metabolism. Alterations in the catalytic activity of CYP1B1 and COMT enzymes have been found associated with altered breast cancer risk in postmenopausal women in many populations. The substitution of leucine (Leu) to valine (Val) at codon 432 increases the catalytic activity of CYP1B1, however, substitution of Val to methionine (Met) at codon 158 decreases the catalytic activity of COMT. The present study was performed to evaluate the associations of CYP1B1 Leu432Val and/or COMT Val158Met polymorphisms with total, premenopausal and postmenopausal breast cancer risks in Indian women. COMT and CYP1B1 polymorphisms in controls and breast cancer patients were analyzed employing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) followed by gel electrophoresis. Although CYP1B1 and COMT genotypes did not exhibit statistically significant association with breast cancer risks when analyzed individually, COMT wild type (Val158Val) in combination with CYP1B1 heterozygous variant (Leu432Val) [OR: 0.21; 95% CI (0.05–0.82), p value; 0.021] and COMT heterozygous variant (Val158Met) in combination with CYP1B1 wild type (Leu432Leu) [OR: 0.29; 95% CI (0.08–0.96), p value; 0.042] showed significant protective association with premenopausal breast cancer risk. The results demonstrate that CYP1B1 wild type in combination with COMT heterozygous or their inverse combination offer protection against breast cancer in premenopausal Indian women.

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Influence of Estrogenic Metabolic Pathway Genes Polymorphisms on Postmenopausal Breast Cancer Risk

Pharmaceuticals ◽

10.3390/ph14020094 ◽

2021 ◽

Vol 14 (2) ◽

pp. 94

Author(s):

Micaela Almeida ◽

Mafalda Soares ◽

José Fonseca-Moutinho ◽

Ana Cristina Ramalhinho ◽

Luiza Breitenfeld

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Metabolic Pathway ◽

Postmenopausal Breast Cancer ◽

Mthfr C677t ◽

Estrogen Metabolism ◽

P Value ◽

Breast Cancer Risk Assessment ◽

The Impact

Estrogen metabolism plays an important role in tumor initiation and development. Lifetime exposure to high estrogens levels and deregulation of enzymes involved in estrogen biosynthetic and metabolic pathway are considered risk factors for breast cancer. The present study aimed to evaluate the impact of mutations acquisition during the lifetime in low penetrance genes that codify enzymes responsible for estrogen detoxification. Genotype analysis of GSTM1 and GSTT1 null polymorphisms, CYP1B1 Val432Leu and MTHFR C677T polymorphisms was performed in 157 samples of women with hormone-dependent breast cancer and correlated with the age at diagnosis. The majority of patients with GSTT1 null genotype and with both GSTM1 and GSTT1 null genotypes were 50 years old or more at the diagnosis (p-value = 0.021 and 0.018, respectively). Older women with GSTM1 null genotype were also carriers of the CYP1B1Val allele (p-value = 0.012). As well, GSTT1 null and CYP1B1Val genotypes were correlated with diagnosis at later ages (p-value = 0.022). Similar results were found associating MTHFR C677T and GSTT1 null polymorphism (p-value = 0.034). Our results suggest that estrogen metabolic pathway polymorphisms constitute a factor to be considered simultaneously with models for breast cancer risk assessment.

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

BMC Cancer ◽

10.1186/s12885-021-07896-4 ◽

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.

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Association of Homozygous Wild-Type Glutathione S-Transferase M1 Genotype with Increased Breast Cancer Risk

Cancer Research ◽

10.1158/0008-5472.can-03-2861 ◽

2004 ◽

Vol 64 (4) ◽

pp. 1233-1236 ◽

Cited By ~ 44

Author(s):

Nady Roodi ◽

William D. Dupont ◽

Jason H. Moore ◽

Fritz F. Parl

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Wild Type ◽

Glutathione S Transferase

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Estrogen metabolism and breast cancer risk among postmenopausal women: a case–cohort study within B~FIT

Carcinogenesis ◽

10.1093/carcin/bgt367 ◽

2013 ◽

Vol 35 (2) ◽

pp. 346-355 ◽

Cited By ~ 42

Author(s):

Cher M. Dallal ◽

Jeffrey A. Tice ◽

Diana S.M. Buist ◽

Douglas C. Bauer ◽

James V. Lacey ◽

...

Keyword(s):

Breast Cancer ◽

Cohort Study ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Postmenopausal Women ◽

Estrogen Metabolism

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Does the Use of Tamoxifen during Controlled Ovarian Hyperstimulation Alter Estrogen Metabolism Favoring a Protective Effect On Breast Cancer Risk?

Fertility and Sterility ◽

10.1016/j.fertnstert.2013.01.068 ◽

2013 ◽

Vol 99 (3) ◽

pp. S30-S31

Author(s):

M. Gualtieri ◽

F. Lee ◽

F. Stanczyk ◽

S. Patel ◽

K. Bendikson ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Protective Effect ◽

Controlled Ovarian Hyperstimulation ◽

Estrogen Metabolism ◽

Ovarian Hyperstimulation

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Estrogen Metabolism and Breast Cancer Risk: A Review

African Journal of Reproductive Health ◽

10.2307/30032440 ◽

2006 ◽

Vol 10 (1) ◽

pp. 13 ◽

Cited By ~ 10

Author(s):

M. N. Okobia ◽

C. H. Bunker

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Estrogen Metabolism

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The Discovery of Breast Cancer Risk Gene and Establishment of Prediction Model Based on Estrogen Metabolism Regulation

10.21203/rs.3.rs-50139/v1 ◽

2020 ◽

Author(s):

Feng Zhao ◽

Zhixiang Hao ◽

Yanan Zhong ◽

Yinxue Xu ◽

Meng Guo ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Risk Score ◽

Healthy Subjects ◽

Estrogen Metabolism ◽

Polygenic Risk Score ◽

Polygenic Risk

Abstract Background In this study, we aim to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes with breast cancer (BC) and establish a risk prediction model based on polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of the estrogen levels and the single nucleotide polymorphisms (SNPs) of 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. The independent sample t test was used to evaluate the difference between PRS scores of BC and healthy subjects. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (ROC). 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 accumulated in the serum of BC patients. Then,we established PRS model to evaluate the role of multiple genes SNPs. The PRS model 1 (M1) was established from 6 GWAS-identified high risk genes SNPs. On the basis of M1, we added 7 estrogen metabolism enzyme genes SNPs to establish PRS model 2 (M2). The independent sample t test results show that there is no difference between BC and healthy subjects in M1 (P = 0.17), however, there is significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results also show that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than M1 (AUC = 54.56%). Conclusion Estrogens and the related metabolic enzymes gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme genes SNPs has a good ability in breast cancer risk prediction, and the accuracy is greatly improved comparing PRS model only includes GWAS-identified genes SNPs.

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Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models

Cancers ◽

10.3390/cancers13205194 ◽

2021 ◽

Vol 13 (20) ◽

pp. 5194

Author(s):

Sherly X. Li ◽

Roger L. Milne ◽

Tú Nguyen-Dumont ◽

Dallas R. English ◽

Graham G. Giles ◽

...

Keyword(s):

Breast Cancer ◽

Family History ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Age Groups ◽

Risk Scores ◽

P Value ◽

Risk Models ◽

Discriminatory Accuracy ◽

Incident Cases

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50–65 years and unaffected at commencement of follow-up two (conducted in 2003–2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50–54, 55–59, 60–65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56–0.62) and IBIS (0.57, 95% CI 0.54–0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.

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