Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98486
標題: 建立雌激素醌類蛋白質胼合物作為乳癌之生物指標預測模式
Establishment of a prediction model of breast cancer using estrogen quinone-derived protein adducts as biomarkers
作者: 廖文碩
Wun-Shuo Liao
關鍵字: 乳癌風險預測
雌性激素
Estrogen Quinone
Breast Cancer
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摘要: 本研究目的為運用本實驗室已分析完成台灣乳癌婦女及正常對照組女性體內蛋白中雌性激素 (17β-estradiol, E2) 醌類代謝物,包括E2-2,3-Q、E2-3,4-Q,所形成紅血球蛋白質胼合物 (Hemoglobin adducts, Hb adducts) 之背景值,建立一最佳乳癌高風險族群篩選模式。其中篩選指標包含3項指標,觀察E2-3,4-Q-2-S-Hb,E2-2,3-Q-4-S-Hb,以及上述二者總和E2-3,4-Q-2-S-Hb +E2-2,3-Q-4-S-Hb之背景值分別求取算數平均值及標準偏差,建立混合模式及個別模式以進行篩選乳癌高風險族群之數學模式。 混合模式利用乳癌病人及健康對照組之混合數據庫,求取E2-3,4-Q-2-S-Hb,E2-2,3-Q-4-S-Hb,以及上述二者總和E2-3,4-Q-2-S-Hb +E2-2,3-Q-4-S-Hb背景值之算術平均值及標準偏差,分別觀察其所屬之族群特徵,包括年齡及BMI之分布狀況,定義大於平均值之族群歸類為高風險族群 (High Risk group),低於背景值平均值歸類為低風險族群 (Low Risk group),並計算預測正確值 (Predictive Value , P.V.),亦即正確預測人數除以所有人數之百分比,以及計算偽陽性 (False Positive rate, F.V),即高風險族群之健康對照組人數除以所有健康對照組人數之百分比及偽陰性 (False Negative rate, F.N),即低風險族群之乳癌病人人數除以所有乳癌病人人數之百分比。 個別模式為分別利用乳癌病人及正常人之個別平均值及標準偏差進行分類,觀察年齡及BMI分布狀況之差異並定義高於乳癌病人平均值之族群歸類為高風險族群 (High Risk group),低於健康對照組平均值之族群歸類為低風險族群 (Low Risk group),介於兩者之間之族群歸類為中風險族群 (Mideum Risk group) 混合模式利用三種生物指標篩選之預測正確值皆大於94.5%以上,偽陽性之值皆低於1.3%,偽陰性之值亦皆低於9.7%,其中混合模式之預測架構具有較高靈敏度 (90.2%以上) 及高特異度 (98.6%以上)。綜合以上之研究結果,此一乳癌高風險族群之篩選模式可作為乳癌預防醫學之應用。
The purpose of this study was to establish a prediction model of breast cancer by using the background values of 17β-estradiol (E2) quinones, including E2-2,3-Q and E2-3,4-Q, generated hemoglobin adducts in breast cancer patients and healthy controls in Taiwanese women. These biomarkers include 3 indicators, i.e., E2-3,4-Q-2-S-Hb, E2-2,3-Q-4-S-Hb, and E2-3,4-Q-2-S-Hb+E2-2,3-Q-4-S-Hb, to screen for high risk individuals of developing breast cancer by two different approaches, including the mixed model and individual model. Mixed model utilizes the background levels of protein adducts derived from the combined pool of breast cancer patients and healthy controls. The mean values of estrogen quinone-derived hemoglobin adducts were calculated and served as indicators of breast cancer risk (high and low risk). Subjects' characteristics for each group of individuals in terms of age and BMI were compared. Additionally, the predictive values, false positive rates, and false negative rates of the screening model were estimated. The mathematical means and standard deviations of the individual model were estimated separately using protein adducts derived from cancer patients and controls and served as indicators of breast cancer risk (high, medium, and low). The corresponding characteristics for each group of individuals were compared as distinguished by age and BMI. The subjects with levels greater than mean values of the cancer patients was classified as a high-risk group, whereas those individuals with levels lower than the mean values of healthy controls was classified as a low-risk group and the rest of the subjects were classified as medium-risk group. Results from our current investigation indicated that predictive value of mixed model using these three biomarkers is greater than 94.5% with less than 1.3% of false positive rates and less than 9.7% of false negative rates. Mixed model also has high sensitivity (greater than 90.2%) and high selectivity (greater than 98.6%). In conclusions, the screening model for individuals with high risk of developing breast cancer can be applied to the field of preventive medicine in breast cancer.
URI: http://hdl.handle.net/11455/98486
文章公開時間: 2018-08-13
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