Abstract
PURPOSE: We performed a case-control study to evaluate the association of genetic polymorphisms of estrogen-metabolizing enzyme genes and estrogen receptor genes with breast cancer risk according to age group and subtypes in Korean women. METHODS: Breast cancer patients (n = 830) and the hospital healthy controls (n = 390) with both clinical information and SNP data were included in the study. Age was divided into three groups: premenopausal under 35 years (n = 64), premenopausal over 35 years (n = 456), and postmenopausal women (n = 310), respectively. Tumor subtype was classified into four subtypes: luminal A, luminal B, HER2-overexpressing, and triple-negative, respectively. Genotyping of the selected SNPs in ESR1, ESR2, CYP1A1, CYP1B1, and COMT was conducted using the VeraCode Golden Gate Genotyping Assay Technology. Multiple logistic regression models (dominant, recessive, and additive) were applied to determine the odds ratio, 95% confidence interval, and p value. RESULTS: ESR1, rs2881766, rs2077647, rs926778, and rs2273206 polymorphisms increased breast cancer risk, and rs3798377 decreased the risk in overall patients. The association between SNP genotype and breast cancer risk was varied according to age groups and tumor subtypes. For age subgroups, rs2881766 increased breast cancer risk in the all three age groups, and rs926778 increased the risk in premenopausal over 35 years women and in postmenopausal women. For the tumor subtypes, rs2881766 increased breast cancer risk manly in luminal A, HER2-overexpressing, and triple-negative subtypes except for luminal B subtype, and rs926778 increased the risk in luminal A and triple-negative subtypes. Rs3798577 decreased the risk in luminal B and triple-negative subtypes. CONCLUSION: The results showed that ESR1 rs2881766 polymorphism increased breast cancer risk and rs3798377 decreased the risk in Korean women. Because of wide variation of the association between SNP genotype and breast cancer risk according to age group and tumor subtypes, further studies such as a large-scale cohort study need for validation and test of biologic significance.