Departments of Surgery, Northwestern University Feinberg School of Medicine, 303 E Superior St. Lurie 4-111, Chicago, IL 60611, USA.
Breast Cancer Res Treat. 2012 Feb;131(3):1067-76. doi: 10.1007/s10549-011-1879-2. Epub 2011 Nov 19.
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.
存档石蜡包埋乳腺组织常用于与风险相关的研究，但通常受到绝经状态数据缺失的限制。我们检验了这样一个假设，即乳腺组织基因表达模式可改善绝经分层。临床试验中的健康高危参与者接受了乳房随机细针穿刺（rFNA）；从 rFNA 样本中提取 100ngRNA，逆转录；通过实时 PCR 评估 28 个雌激素反应基因的表达。通过测量血浆激素和年龄确定真实绝经状态（TMS）。通过逻辑回归分析差异表达基因和年龄。使用接收器操作特性（ROC）分析评估绝经预测的准确性，并在第二组 44 名女性独立样本中进行验证。在测试组中，绝经后女性的五种雌激素反应基因（GREB1、PGR、TFF1、PRLR 和 CCND1）表达显著降低（所有基因调整后的 P 值均<0.03）。在验证组中，这些基因中的三个在绝经后女性中的表达水平较低（GREB1、PGR、TFF1）（所有基因调整后的 P 值均<0.06）。在测试组中，基于年龄和三个基因的模型曲线下面积（AUC）高于仅年龄>50 （AUC 96.1% vs. 87.2%，P=0.002），在验证组中，该模型仍优于仅年龄（AUC 99.0% vs. 95.5%，P=0.16）。乳腺标本中雌激素相关基因的表达可用于改善绝经分类，减少与绝经相关的生物学噪声，从而有助于在存档的乳腺样本中识别 RNA 或蛋白质风险生物标志物的研究。
