Biomarkers expression in benign breast diseases and risk of subsequent breast cancer: a case–control study
نویسندگان
چکیده
Women with benign breast diseases (BBD) have a high risk of breast cancer. However, no biomarkers have been clearly established to predict cancer in these women. Our aim was to explore whether estrogen receptor (ER), progesterone receptor (PR), and Ki67 expression stratify risk of breast cancer in screened women with BBD. We conducted a nested case-control study. Women with breast cancer and prior BBDs (86 cases) were matched to women with prior BBDs who were free from breast cancer (172 controls). The matching factors were age at BBD diagnosis, type of BBD, and follow-up time since BBD diagnosis. ER, PR, and Ki67 expression were obtained from BBDs' specimens. Conditional logistic regression was used to estimate odds ratios (ORs), and 95% confidence intervals (CIs) of breast cancer risk according to ER, PR, and Ki67 expression. Women with >90% of ER expression had a higher risk of breast cancer (OR = 2.63; 95% CI: 1.26-5.51) than women with ≤70% of ER expression. Similarly, women with >80% of PR expression had a higher risk of breast cancer (OR = 2.22; 95% CI: 1.15-4.27) than women with ≤40% of PR expression. Women with proliferative disease and ≥1% of Ki67 expression had a nonsignificantly increased risk of breast cancer (OR = 1.16; 95% CI: 0.46-2.90) than women with <1% of Ki67 expression. A high expression of ER and PR in BBD is associated with an increased risk of subsequent breast cancer. In proliferative disease, high Ki67 expression may also have an increased risk. This information is helpful to better characterize BBD and is one more step toward personalizing the clinical management of these women.
منابع مشابه
Subtypes of Benign Breast Disease as a Risk Factor for Breast Cancer: A Systematic Review and Meta-Analysis Protocol
AbstractBreast cancer is a multifactorial disease. Benign breast disease (BBD) is one of the most important risk factors for breast cancer. The etiology of BBD is unknown. It is divided into nonproliferative and proliferative diseases. The selection of studies will be based on titles, abstract screening, inclusion and exclusion criteria, and quality assessment. Previous studies have shown that ...
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