臺灣博碩士論文加值系統

本研究目的為運用本實驗室已分析完成之生物指標，亦即台灣女性乳癌病人及健康對照組體內蛋白中雌性激素（17β-estradiol, E2）醌類代謝物，包括17β-estradiol-3,4-quinone（E2-3,4-Q）、17β-estradiol-2,3-quinone（E2-2,3-Q），所形成紅血球蛋白質胼合物（hemoglobin adduct, Hb adduct）之背景值，建立一最佳乳癌高風險族群之篩選模式。以生物指標E2-3,4-Q-2-S-Hb、E2-2,3-Q-4-S-Hb之背景值，套用統計軟體之線性判別分析方法（linear discriminant analysis, LDA），求得判別函數（discriminant function），作為篩選乳癌高風險族群之模式。同時利用實驗室過去分析之懷孕女性體內紅血球蛋白質胼合物背景值，作為線性判別分析模式驗證之數據，代入判別函數，探討此一乳癌篩選模式之預測準確度。
線性判別分析模式利用144位乳癌病人及147位健康對照組之數據庫，求取E2-3,4-Q-2-S-Hb與E2-2,3-Q-4-S-Hb背景值之判別函數，定義函數值接近乳癌病人重心之族群為乳癌病人，近健康對照組重心則歸類為健康對照組，並計算預測正確值（predictive value, P.V.）、偽陽性率（false positive rate, F.P.）及偽陰性率（false negative rate, F.N.）。同時探討乳癌風險判別錯誤之個案特徵，包括年齡及身體質量指數（body mass index, BMI），找出此篩選模式不適用之族群。
研究結果顯示，線性判別分析模式篩選之預測正確值為97.3%，偽陽性率 4.8%、偽陰性率0.7%。以懷孕女性樣本代入驗證的結果，預測正確值99.5%，偽陽性率0.5%，無偽陰性。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。並建議未來以盲樣分析之方式測試此線性判別分析模式之準確度。

The purpose of this study was to establish a model for prediction of breast cancer using the background levels of hemoglobin (Hb) adducts of 17β-estradiol (E2)–derived quinones, including 17β-estradiol-3,4-quinone (E2-3,4-Q) and 17β-estradiol-2,3-quinone (E2-2,3-Q), in breast cancer patients and healthy controls in Taiwanese women. These biomarkers including 2 indicators, i.e., E2-3,4-Q-2-S-Hb, and E2-2,3-Q-4-S-Hb, were used to screen for high risk individuals of developing breast cancer by the linear discriminant analysis (LDA) model.
The discriminant function of the LDA model was calculated and served as an indicator of breast cancer risk. Subjects with discriminant scores near the centroid of patients were classified as patients, whereas those individuals with scores near the centroid of controls were classified as controls. In parallel, the predictive values, false positive rates, and false negative rates were estimated. As distinguished by age and BMI, the corresponding characteristics of individuals in the categories of false positives and false negatives were compared. Finally, the discriminant function of the LDA model was verified using the background levels of hemoglobin adducts in Taiwanese pregnant women.
Results indicated that predictive value of the LDA model using these biomarkers is 97.3% with 4.8% of false positive rate and 0.7% of false negative rate. In conclusions, the LDA model we established in this study may be applied to the field of preventive medicine in breast cancer.

摘要 i
Abstract ii
縮寫表（List of Abbreviation） iii
目錄 iv
圖目錄 vii
表目錄 viii
第一章 前言 1
1-1 研究緣起 1
1-2 研究目的 5
第二章 文獻回顧 6
2-1 乳癌 6
2-2 乳癌之風險因子 10
2-2-1 性別 10
2-2-2 年齡 11
2-2-3 肥胖與體重過輕 11
2-2-4 家族病史 13
2-2-5 種族及經濟因素 14
2-2-6 生活習慣 15
2-2-7 環境暴露因素 16
2-3 雌性激素 20
2-3-1 雌性激素之代謝 22
2-3-2 雌性激素與乳癌 24
2-4 乳癌診斷及受體 25
2-4-1 雌性激素受體 25
2-4-2 黃體激素受體 27
2-4-3 人類表皮生長因子受體2 27
2-5 乳癌之預防 29
2-6 蛋白質胼合物 30
2-6-1 蛋白質胼合物分類 30
2-7 使用蛋白質胼合物之篩選模式 33
第三章 研究架構 35
3-1 乳癌高風險族群之篩選模式－線性判別分析模式之建立 36
3-2 乳癌高風險族群之篩選模式－線性判別分析模式之驗證 37
第四章 研究材料與方法 38
4-1 研究樣本 38
4-1-1 乳癌病人之基本資料 39
4-1-2 健康對照組之基本資料 42
4-1-3 懷孕女性之基本資料 45
4-2 樣本背景值分析方法 52
4-3 統計分析方法 53
4-3-1 線性判別分析 53
4-3-2 線性判別分析與乳癌篩選 54
4-3-3 統計軟體之判別分析 55
4-4 模式結果表示方法 56
第五章 研究結果 57
5-1 線性判別分析之假設檢定 57
5-2 線性判別分析模式之建立 59
5-3 線性判別分析模式之驗證 62
第六章 討論 65
6-1 線性判別分析模式之誤判樣本 66
6-1-1 線性判別分析模式之偽陽性 66
6-1-2 線性判別分析模式之偽陰性 68
6-2 線性判別分析模式與混合模式之比較 69
6-2-1 線性判別分析模式與混合模式平均數法之比較 70
6-2-2 線性判別分析模式與混合模式四分位數法之比較 72
6-3 線性判別分析模式與個別模式之比較 74
6-4 線性判別分析模式與多層感知器模式之比較 77
第七章 結論 79
第八章 未來工作 80
第九章 參考文獻 81

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