Breast cancer (BC) is an estrogen-dependent tumor, and the occurrence of BC is closely related to the imbalance of estrogen homeostasis . The accumulation of estrogen and its toxic metabolites in vivo is a significant risk factor for BC development. Different types of estrogens have different physiological and pathological activities and can play an important role in the process of cancer development through different mechanisms. Parent estrogens are postulated to promote tumorigenesis directly through the stimulation of estrogen receptor (ER) . The endogenous conversion of estrogen to genotoxic metabolites has been reported as an alternative, potentially ER-independent mechanism for estrogen-dependent breast tumorigenesis . The catechol estrogens can form adducts with DNA, causing gene mutations, and produce direct genotoxicity . Methoxyestrogens, including 2-methoxyestradiol, have been shown to inhibit carcinogenesis by suppressing cell proliferation and estrogen oxidation due to effects on microtubule stabilization .
In this study, the LC-MS/MS quantitative analysis method was used to determine the serum estrogens in the BC group and NC group. Comparing the levels of serum estrogens in the follicular phase and luteal phase of premenopausal breast cancer patients with healthy female volunteers, we found that the level of parent and hydroxylated estrogen in the BC group was significantly higher than that of NC, which indicated that estrogens metabolism disorder is closely related to the occurrence and development of breast cancer. Using OPLS-DA analysis, we have also noticed that E1, E2, 4-OHE2, 2-OHE2, and 2/4-OHE1 are BC-related disease markers. This result was consistent with the epidemiologic characteristics of patients with BC .
A large number of studies have confirmed that breast cancer existed heritability [26, 27]. However, high-risk genes such as BRCA1 and BRCA2 account for less than 15% of breast cancer cases [28, 29], which suggests that numerous breast cancer-related risk genes have not been discovered, and these gene polymorphisms influence susceptibility to breast cancer.
Estrogen is an important risk factor for breast cancer. However, no research has incorporated estrogens into the breast cancer risk prediction model. The possible main reason is that there is no clinically effective estrogen evaluation method, because the steady-state of estrogen is affected by various physiological and pathological factors such as menstrual cycle fluctuations. However, estrogen homeostasis is regulated by various metabolic enzymes. Therefore, we believe that the estrogen metabolic enzyme gene polymorphisms are closely related to estrogen homeostasis and the occurrence and development of breast cancer. In this study, univariate logistic regression analysis showed that CYP1A1, CYP1B1, and SULT1A1 gene polymorphisms are closely related to the occurrence of breast cancer.
CYP1A1 and CYP1B1 are the major phases I drug metabolism enzymes that catalyze hydroxylation of estrogens. The increasing polarity of estrogens may be related to the risk of breast cancer . Our experiments also verified this view. In this study, we found that the variant allele of CYP1B1 rs1086836 was involved in reducing the risk of breast cancer, and the exact mechanism of the protection of this variant allele was not clear, we assumed that the heterozygote model of CYP1B1 rs1086836 (GC vs.GG: OR = 0.37, 95%CI: 0.21–0.67, P = 0.001) may result in decreased function of the CYP1B1 enzyme, reducing the production of 4-hydroxy estrogen and even catechol estrogen-3,4-quinone (CE-3,4-Q) to form adducts with DNA. At the same time, this study also proved that the variant alleles of CYP1A1 rs1048943 (TC vs.TT: OR = 2.37, 95%CI: 1.27–4.43, P = 0.003) and CYP1B1 rs1056827 (AA vs.CC: OR = 6.90, 95%CI: 1.50-31.76, P = 0.001) are closely related to the risk of breast cancer, which is consistent with the most research [32, 33]. The possible reason is that the mutations promote the activity of CYP1A1 and CYP1B1 enzymes to increase the production of hydroxylated estrogens or promote the individual's susceptibility to estrogen.
SULTs catalyze the sulfate conjugation of a broad range of substrates and play an important role in the metabolism of endogenous and exogenous compounds including thyroid and steroid hormones, neurotransmitters, drugs and procarcinogens . SULTs catalyzes the sulfated metabolism of estrogen (E1 and E2) and its metabolites (such as catechol estrogen) and eliminate the activity of estrogen, by forming the sulfate compounds: sulfated estrogens which can not combine with estrogen receptors (ERs). At the same time, it promotes the rapid excretion of sulfated estrogen from the cells, which can reduce the level of estrogen exposure in the circulation and target tissues. The SULT1A1 rs1042028 is the most widely studied gene polymorphism. Its allelic variation can reduce enzyme activity and thermal stability, resulting in increased estrogen accumulation and increased individual susceptibility to breast cancer . In this study, the heterozygote model of rs1042028 had 2.21 times higher risk of breast cancer than the wild model. It is consistent with the results of multiple studies [36, 37].
Previous studies investigated associations between the PRS of multiple SNPs and breast cancer risk to study the cumulative effect of genes on the disease. Mavaddat et al. constructed a 77-SNP PRS for breast cancer and found a threefold increase in risk when comparing the polygenic scores of the highest 1% and the middle quintiles . Harlid et al. investigated the combined effect of low-penetrant SNPs on breast cancer including ten SNPs and founded that the cumulative effect is strongly correlated with breast cancer . However, most of this research on PRS comes from the Caucasian population sample database. Although Sueta and Chan and others have also conducted similar studies in Asian populations, the evidence is still limited [40, 41]. So far, there has been no relevant report on the establishment of a breast cancer PRS risk prediction model from the perspective of estrogen metabolizing enzymes. Based on this, a multi-gene PRS model including estrogen metabolic enzyme genes SNPs and GWAS-selected SNPs was constructed in this study to evaluate the comprehensive effects of multiple estrogen metabolic enzymes SNPs on breast cancer.
In this study, we evaluated possible relationships between the increased breast cancer risk estrogen metabolic enzyme genes SNPs and GWAS-identified genes SNPs in an Asian population. Among them, the GWAS-identified SNPs were unassociated with breast cancer risk in the per-allele model or dominant model in our study. This finding was inconsistent with previous study . Further, we established PRS model 1 just including GWAS-identified SNPs and PRS model 2 which added estrogen metabolic enzyme genes SNPs on the basis of M1. By calculating the PRS score of each individual under the M1 and M2 PRS models, and performing a t-test analysis on the PRS score of the BC and NC group, we found that the P-value (4.9*10− 5) of the M2 PRS model was far less than M1 (0.17). Meanwhile, the ROC (62.18%) of M2 models was better than the M1 (54.56%). Therefore, the model constructed by adding estrogen metabolic enzyme genes SNPs has a good ability in breast cancer risk prediction, and the accuracy is greatly improved.