Meta-analysis of the association between three microRNA polymorphisms and breast cancer susceptibility
Metrics: PDF 1259 views | HTML 2107 views | ?
Kun Mu1, Zi-Zheng Wu2, Jin-Pu Yu3, Wei Guo4, Nan Wu1, Li-Juan Wei1, Huan Zhang1, Jing Zhao1 and Jun-Tian Liu1
1The Second Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, P.R. China
2Department of Breast Surgery, The First Hospital of Qinhuangdao, Qinhuangdao 066000, P.R. China
3Cancer Molecular Diagnostic Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, P.R. China
4Department of Orthopaedics, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China
Jun-Tian Liu, email: [email protected]
Keywords: breast cancer, meta-analysis, miRNA, polymorphisms
Received: September 22, 2016 Accepted: June 02, 2017 Published: June 16, 2017
Single nucleotide polymorphisms (SNPs) in three microRNAs (miRNAs), rs2910164 in miR-146a, rs11614913 in miR-196a2, and rs3746444 in miR-499, have been associated with breast cancer (BC) susceptibility, but the evidence is conflicting. To obtain a more robust assessment of the association between these miRNA variants and BC risk, we carried out a meta-analysis through systematic literature retrieval from the PubMed and Embase databases. A total of 9 case-control studies on rs2910164, 12 on rs11614913, and 7 on rs3746444 were included. Pooled odds ratios and 95% confidence intervals were used to evaluate associations with BC risk. Overall analysis showed that rs2910164 was not associated with BC susceptibility in any genetic model, whereas rs11614913 was associated with a decreased risk in both the allelic contrast and recessive models, and rs3746444 imparted an increased risk in all genetic models. Stratified analyses showed that rs11614913 may decrease the risk of BC in the heterozygote model in Asians, and in all genetic models, except the heterozygote model, when the sample size is ≥ 500. Subgroup analysis indicated that rs3746444 was associated with increased risk of BC in Asians, but not Caucasians, at all sample sizes. This meta-analysis suggests that rs11614913 in miR-196a2 may decrease the risk of BC, while rs3746444 in miR-499 may increase it, especially in Asians when the sample size is large. We propose that rs11614913(C > T) and rs3746444 (A > G) may be useful biomarkers predictive of BC risk.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.