Literature DB >> 24023349

Genetic profile and determinants of homocysteine levels in Kazakhstan patients with breast cancer.

Ainur Akilzhanova1, Zhannur Nurkina, Kuvat Momynaliev, Erlan Ramanculov, Zhaxibai Zhumadilov, Zhaxybai Zhumadilov, Tolebay Rakhypbekov, Naomi Hayashida, Masahiro Nakashima, Noboru Takamura.   

Abstract

AIM: To analyze associations between homocysteine level, MTHFR and FTO rs1477196 polymorphisms and folate status in patients with breast cancer (BC) in order to clarify determinants of hyperhomocysteinemia. PATIENTS AND METHODS: The study included 315 BC cases and 604 controls.
RESULTS: The MTHFRC677T genotype was associated with an increased incidence of BC [Odds ratio (OR)=1.71; 95% Confidential interval (CI)=1.21-2.43]. The MTHFR A1298C genotype was associated with a decreased risk of BC [OR=0.68; 95% CI: 0.49-0.95]. The homocysteine level was not associated with either MTHFR C677T or A1298C, nor with FTO rs1477196, but was inversely correlated with folate status in cancer cases (p<0.0001) and tended to be higher in patients with the MTHFR 677TT genotype. The folate level (p<0.0005) was an independent predictor of hyper-homocysteinemia in patients with BC.
CONCLUSION: These results suggest an important role of homocysteine in breast tumorigenesis. Further studies are warranted to investigate how combined MTHFR genotypes exert their effects on cancer susceptibility.

Entities:  

Keywords:  Breast cancer; FTO; Kazakhstan; MTHFR; folate; homocysteine; polymorphism

Mesh:

Substances:

Year:  2013        PMID: 24023349

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


  14 in total

Review 1.  RNA epigenetics.

Authors:  Nian Liu; Tao Pan
Journal:  Transl Res       Date:  2014-04-08       Impact factor: 7.012

2.  Association of C677T (rs1081133) and A1298C (rs1801131) Methylenetetrahydrofolate Reductase Variants with Breast Cancer Susceptibility Among Asians: A Systematic Review and Meta-Analysis.

Authors:  Maryam Rezaee; Hamed Akbari; Mohammad Amin Momeni-Moghaddam; Fatemeh Moazzen; Sarvenaz Salahi; Reza Jahankhah; Sedigheh Tahmasebi
Journal:  Biochem Genet       Date:  2021-01-02       Impact factor: 1.890

Review 3.  Post-transcriptional gene regulation by mRNA modifications.

Authors:  Boxuan Simen Zhao; Ian A Roundtree; Chuan He
Journal:  Nat Rev Mol Cell Biol       Date:  2016-11-03       Impact factor: 94.444

4.  Extremely-randomized-tree-based Prediction of N6-Methyladenosine Sites in Saccharomyces cerevisiae.

Authors:  Rajiv G Govindaraj; Sathiyamoorthy Subramaniyam; Balachandran Manavalan
Journal:  Curr Genomics       Date:  2020-01       Impact factor: 2.236

5.  MTHFR 677C>T polymorphism and the risk of breast cancer: evidence from an original study and pooled data for 28031 cases and 31880 controls.

Authors:  Singh Pooja; Justin Carlus; Deepa Sekhar; Amirtharaj Francis; Nishi Gupta; Rituraj Konwar; Sandeep Kumar; Surender Kumar; Kumarasamy Thangaraj; Singh Rajender
Journal:  PLoS One       Date:  2015-03-24       Impact factor: 3.240

6.  ASSOCIATION OF MTHFR A1298C POLYMORPHISM WITH BREAST CANCER AND/OR OVARIAN CANCER RISK: AN UPDATED META-ANALYSIS.

Authors:  Wei Liu; Yi Li; Rui Li; Xiao Han; Ying Ma; Bin Liu; Xiangzhen Kong
Journal:  Afr J Tradit Complement Altern Med       Date:  2016-08-12

7.  MTHFR C677T polymorphism and breast, ovarian cancer risk: a meta-analysis of 19,260 patients and 26,364 controls.

Authors:  Lilin He; Yongxiang Shen
Journal:  Onco Targets Ther       Date:  2017-01-06       Impact factor: 4.147

8.  Is FTO gene variant related to cancer risk independently of adiposity? An updated meta-analysis of 129,467 cases and 290,633 controls.

Authors:  Yu Kang; Fang Liu; Yao Liu
Journal:  Oncotarget       Date:  2017-03-22

9.  Plasma Levels of Homocysteine and the Occurrence and Progression of Rectal Cancer.

Authors:  Zhi Liu; Chunhui Cui; Xiaoyang Wang; Alejandro Fernandez-Escobar; Qunzheng Wu; Kai Xu; Jiajia Mao; Minxin Jin; Kexin Wang
Journal:  Med Sci Monit       Date:  2018-03-27

10.  iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm.

Authors:  Omid Mahmoudi; Abdul Wahab; Kil To Chong
Journal:  Genes (Basel)       Date:  2020-05-09       Impact factor: 4.096

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.