We are analyzing https://link.springer.com/article/10.1007/s10549-011-1879-2.

Title:
Prediction of menopausal status from estrogen-related gene expression in benign breast tissue | Breast Cancer Research and Treatment
Description:
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.
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article, breast, pubmed, google, scholar, menopause, cancer, cas, age, expression, research, gene, menopausal, women, set, privacy, cookies, status, tissue, lee, genes, risk, factors, epidemiol, content, data, publish, search, prediction, benign, november, chatterton, khan, access, res, natural, study, edn, elsevier, philadelphia, medicine, usa, analysis, information, log, journal, treatment, estrogenrelated, report, helenowski,

Topics {✒️}

estrogen-related gene expression month download article/chapter healthy high-risk participants cancer prevention n01-cn-35157 hormone replacement therapy serum hormone profiles risk-related research full article pdf privacy choices/manage cookies benign breast tissue archived breast samples behavior genetics research article lee protein risk biomarkers henderson-jackson eb women aged 35–49 years benign breast lesions gail risk model national cancer institute biological noise related �breast tissue age real-time pcr receiver-operator characteristic early potential precursors nipple aspirate fluid multi-targeted therapy false discovery rate pike mc proposed classification system 28 estrogen-responsive genes estrogen-responsive genes conditions privacy policy improve menopausal stratification improve menopausal classification �hormonal’ risk factors european economic area electronic supplementary material population-based study true menopausal status menopause transition based accepting optional cookies breast cancer sex steroid hormones article log multiethnic cohort study expression usage analysis check access instant access journal finder publish

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         description: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.
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