The utility of archived paraffin-embedded breast tissue for risk-related research is often limited by missing menopausal status data. We tested the hypothesis that breast tissue gene expression patterns can improve menopausal stratification. Healthy high-risk participants in a clinical trial underwent breast random fine-needle aspiration (rFNA); 100 ng of RNA extracted from rFNA samples was reverse-transcribed; the expression of 28 estrogen-responsive genes was evaluated by real-time PCR. True menopausal status (TMS) was determined by measurement of plasma hormones and age. Differentially expressed genes and age were analyzed by logistic regression. The accuracy of the menopause prediction was assessed using receiver-operator characteristic (ROC) analysis, and validated in a second independent set of 44 women. In the test set, postmenopausal women demonstrated significantly lower expression of five estrogen-responsive genes: GREB1, PGR, TFF1, PRLR, and CCND1 (adjusted P < 0.03 for all). In the validation set, three of these genes were expressed at lower levels in postmenopausal women (GREB1, PGR, TFF1) (adjusted P < 0.06 for all). In the test set, the modeled area under the curve (AUC) for age and three genes was higher than for age >50 alone (AUC 96.1% vs. 87.2%, P = 0.002), and remained better than for age alone in the validation set (99.0% vs. 95.5%, P = 0.16). Estrogen-related gene expression in breast specimens can be used to improve menopausal classification, reducing the biological noise related to menopause in studies that seek to identify RNA or protein risk biomarkers in archived breast samples.