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Study on the Association of some Single Nucleotide Polymorphisms of CYP19A1 Gene with Breast Cancer in Vietnamese Women

2018· article· en· W2905722121 on OpenAlexaboutno aff
Huyen T. M. Pham, Tran Thi Thuy Anh, Nguyễn Thị Hồng Vân

Bibliographic record

VenueVNU Journal of Science Natural Sciences and Technology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsnot available
Fundersnot available
KeywordsSNPGenotypeBreast cancerSingle-nucleotide polymorphismAromataseBiologyGeneticsInternal medicineGeneEndocrinologyCancerOncologyMedicine

Abstract

fetched live from OpenAlex

The CYP19A1 gene encodes for aromatase P450, which is a key enzyme in estrogen metabolism, catalyzes the conversation of testosterone to estradiol and androstenedione to estrone. It is generally believed that polymorphisms in genes coding for key enzymes involved in these pathways could effect to the activity of enzymes, which can change the level of endogenous hormones. Therefore genetic polymorphisms in hormone-related genes could increase the breast cancer susceptibility. In this study, 60 blood samples of breast cancer women and 50 control populations were analyzed to identify the genotype frequency at SNP loci rs10046 C>T and rs2236722 Trp39Arg (T>C) on CYP19A1 using PCR-RFLP and PCR-CTPP respectively. The data were analyzed to determine the association between these polymorphism loci and susceptibility to breast cancer. The result showed that, the genotype frequencies at SNP rs10046 in the control as: CC (14%), CT (48%), TT (38%), in case group are CC (18.33%) , CT (58.33%) and TT (23.34%); at SNP rs2236722 in the control group: TT (94%), TC (6%), in case group TT (90%), TC (10%). The OR analyses for the gene carrying the CC and TC genotypes compared with TT genotype at both loci (OR=2.01; 95% CI=0.87–4.67 with rs10046 and OR = 1.74; 95% CI= 0.40 – 7.42 with rs2236722) indicated that these SNP loci in CYP19A1 have no effect on breast cancer susceptibility.
 Keywords
 Breast cancer, SNP, rs10046, rs2236722, CYP19A1 gene
 References
 1. Bora M. T., Tülin Ö., Halil I. K., Sennur I., Calay Z., Oğuz Ö., Turgay I. (2010), “CYP17 (T-34C) and CYP19 (Trp39Arg) Polymorphisms and their Cooperative Effects on Breast Cancer Susceptibility”, In vivo, 24, pp.71–74.2. Chen C., Sakoda L. C., Doherty J. A., Loomis M. M., Fish S., Ray R. M. (2008), “Genetic variation in CYP19A1 and risk of breast cancer and brocystic breast conditions among women in Shanghai, China”, Cancer Epidemiology Biomarkers Prevention, 17(12), pp.3457–3466.3. Dunning A. M., Dowsett M., Healey C. S., Tee L., Luben R. N., Folkerd E., Novik K. L., Kelemen L., Ogata S., Pharoah P. D., Easton D. F., Day N. E., Ponder B. A. (2004), “Polymorphisms associated with circulating sex hormone levels in postmenopausal women”, J. Natl. Cancer Inst., 96(12), pp.936–945.4. Farzaneh F., Noghabaei G., Barouti E., Pouresmaili F., Jamshidi J., Fazeli A. (2016), “Analysis of CYP17, CYP19 and CYP1A1 gene polymorphisms in Iranian women with breast cancer”, Asian Pacific Journal of Cancer Prevention, 17, pp.23–26.5. Ghisari M., Eiberg H., Long M. (2014), “Polymorphisms in phase I and phase II genes and breast cancer risk and relations to persistent organic pollutant exposure: A case-control study in Inuit women”, Environmental Health, 13(1), pp.19.6. Henderson B. E., Ross R., Bernstein L. (1988), “Estrogens as a cause of human cancer: The Richard and Hinda Rosenthal Foundation award lecture”, Cancer Research, 48, pp.246–253.7. Hirose K., Matsuo K., Toyama T. (2004), “The CYP19 gene codon 39 Trp/Arg polymorphism increases breast cancer risk in subsets of premenopausal Japanese”, Cancer Epidemiol BiomarkPrev, 13, pp.1407–1411.8. Kristensen V. N., Harada N., Yoshimura N., Haraldsen E., Lonning P. E. (2000), “Genetic variants of CYP19 (aromatase) and breast cancer risk”, Oncogene, 19, pp.1329–1333.9. Lunardi G., Piccioli P., Bruzzi P., Notaro R., Lastraioli S., Serra M. (2013), “Plasma estrone sulfate concentrations and genetic variation at the CYP19A1 locus in postmenopausal women with early breast cancer treated with letrozole”, Breast Cancer Research and Treatment, 137(1), pp.167–174.10. Miyoshi Y., Iwao K., Ikeda N., Egawa C., Noguchi S., (2000), “Breast cancer risk associated with polymorphism in CYP19 in Japanese women”, Int J Cancer, 89, pp.325–328. 11. Pineda B., García-Pérez M.Á., Cano A., Lluch A., Eroles P. (2013), “Associations between Aromatase CYP19 rs10046 Polymorphism and Breast Cancer Risk: From a Case–Control to a Meta–Analysis of 20.098 Subjects”, PLos One, 8(1), pp.1–9.12. Ralph D. A., Zhao L. P., Aston C. E., Manjeshwar S., Pugh T. W. (2007), “Age-specific association of steroid hormone pathway gene polymorphisms with breast cancer risk”, Cancer, 109, pp.1940–1948.13. Samson M., Rama R., Swaminathan R., Sridevi V., Nancy K. N., Rajkumar T., (2009), “CYP17 (T-34C), CYP19 (Trp39Arg), and FGFR2 (C-906T) polymorphisms and the risk of breast cancer in South Indian women”, Asian Pacific J Cancer Prev, 10, pp.111–116.14. Yang L., Wang X. Y., Li Y. T., Wang H. L., Wu T., Wang B. (2015), “CYP19 gene polymorphisms and the susceptibility to breast cancer in Xinjiang Uigur women”, Genetics and Molecular Research, 14(3), pp.8473–8482.15. Yoshimoto N., Nishiyama T., Toyama T., Takahashi S., Shiraki N., Sugiura H., (2011), “Genetic and environmental predictors, endogenous hormones and growth factors, and risk of estrogen receptor positive breast cancer in Japanese women”, Cancer Science, 102(11), pp.2065–2072.16. Zins K., Mogg M., Schneeberger C., Abraham D., (2014), “Analysis of the rs10046 polymorphism of aromatase (CYP19) in premenopausal onset of human breast cancer”, International Journal of Molecular Sciences, 15(1), pp.712–724.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.240
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2018
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