Meta-Analysis of BRCA1 Polymorphisms and Breast Cancer Susceptibility

Abstract

Background: BRCA1 codes for a tumor suppressor protein involved in DNA repair. Based on the role of single nucleotide polymorphisms (SNPs) in the modification of gene expression and function and the existence of certain SNPs within 3'-untranslated region of BRCA1 with the ability to change bind ing sites for mirRNAs, several association studies have been designed to explore the significance of SNPs within BRCA1 gene in conferr ing breast cancer (BC) risk. This study aims to assess the relationship between BRCA1 SNPs and BC us ing meta-analysis. Aim: To conduct a meta-analysis for retriev ing case-control studies on the associations between the rs11655505, rs1799966, rs3737559, rs1799950, rs799917 and rs16941 BRCA1 polymorphisms and BC. The pooled odds ratios and its 95% confidence intervals were measured us ing fixed and random model to define the association between these polymorphisms and BC risk. Conclusion: No significant association was found for any of these polymorphisms and BC risk in the allelic, homozygote, dominant or recessive models. Over all, our study implies that the mentioned polymorphisms are not associated with BC risk. However, our study did not exclude the possible contribution of other SNPs within this gene in BC nor substantial contribution of multiple variants within this gene in conferr ing BC risk.

Single nucleotide polymorphisms in breast cancer susceptibility gene 1 are associated with susceptibility to lung cancer

“…Sirisena et al (34) found the G allele of rs799917 SNP (β, -1.069; standard error, 0.537; P=0.047) was associated with age of onset, between 50 and 59 years in patients with sporadic breast cancer from Sri Lanka. In previous studies, the BRCA1 rs799917 SNP was not significantly associated with breast cancer risk (35)(36)(37)(38); however, the results have not been consistent. Previous research investigating rs799917 has involved multiple tumors.…”

Interações realizadas por genes líderes do câncer de mama: uma abordagem computacional

“…Os receptores hormonais de estrógeno e progesterona e o receptor de crescimento epidérmico humano-2 (PIZON M, et al, 2018; ILIE SM et al, 2018) são os fatores preditivos mais utilizados para a escolha do tratamento hormonal (WEIGEL e DOWSETT, 2010). A avaliação de mutações e polimorfismos na proteína supressora de tumor BRCA1 tem sido também bastante utilizada como ferramenta de prevenção da doença (SOUDEH GF, et al, 2018). O biomarcador CA 15-3, o qual detecta a forma solúvel da proteína MUC-1, é o marcador sérico mais amplamente usado em pacientes com câncer de mama, especialmente útil no monitoramento de pacientes com doença metastática (DUFFY MJ, et al, 2010).…”