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研究生:林敬洋
研究生(外文):Ching-Yang Lin
論文名稱:運用混合判定分析探討雌激素醌類代謝物血清白蛋白胼合物於5年存活未復發之乳癌病人之分布趨勢
論文名稱(外文):Using mixed discriminant analysis to explore the distribution of the levels of estrogen quinone-derived albumin adducts in 5-year survivors of breast cancer without recurrence
指導教授:林伯雄林伯雄引用關係
指導教授(外文):Po-Hsiung Lin
口試委員:謝為忠李崇垓
口試日期:2020-07-21
學位類別:碩士
校院名稱:國立中興大學
系所名稱:環境工程學系所
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:103
中文關鍵詞:胼合物蛋白質血清白蛋白雌二醇
外文關鍵詞:AdductProteinAlbumin17β-Estradiol
相關次數:
  • 被引用被引用:1
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本研究目的為運用344位台灣女性,包含169位台灣乳癌婦女、139位正常對照組婦女及36位乳癌術後5年存活未復發之乳癌病人(5年存活者)體內蛋白中雌激素(17β-estradiol, E2)醌類代謝物,為E2-3,4-Q、 E2-2,3-Q所形成血清白蛋白質胼合物(Albumin adducts, Alb adducts),包括4項生物指標,E2-3,4-Q-2-S-Alb、E2-2,3-Q-4-S-Alb、E2-3,4-Q-2-S-Alb + E2-2,3-Q-4-S-Alb及E2-3,4-Q-2-S-Alb / E2-2,3-Q-4-S-Alb之背景值,彙整為一個數據庫後,建立混合平均值判定分析、混合四分位數判定分析及個別平均值判定分析建立篩選乳癌高風險族群模式,進一步探討5年存活者分布趨勢。
混合判定分析求取上述4項生物指標之算數平均值、標準偏差及四分位數,分別觀察各族群特徵,包括年齡及BMI之分布狀況,在混合平均值判定分析,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),相似流程亦運用於個別平均值判定分析,並計算陽性預測值(Positive Predictive Value,PPV)、陰性預測值(Negative Predictive Value,NPV)、假陽性率(False positive rate,Fpr)、假陰性率(False negative rate,Fnr)。
綜合以上研究發現,混合平均值判定分析及個別平均值判定分析在以E2-3,4-Q-2-S-Alb及E2-3,4-Q-2-S-Alb / E2-2,3-Q-4-S-Alb生物指標篩選下,乳癌風險篩選之模式評估指標分別為PPV(100%)、NPV(79%以上)、Fpr(0%)、Fnr(22%以下)、靈敏度(78.1%以上)、特異度(100%)、勝算比(6.41)。進一步分析顯示全部5年存活病人(n=36) 皆分布於低風險族群,此一結果與線性判定分析模式結果相似。
The purpose of this study was to establish a screening model using albumin adducts of 17β-estradiol (E2) metabolites, e.g. E2-3,4-Q and E2-2,3-Q, derived from 344 Taiwanese women, including 169 breast cancer patients, 139 healthy controls, and 36 5-year survivors of breast cancer without recurrence (5-year Survivors). These adducts were used as biomarkers, including, E2-3,4-Q-2-S-Alb, E2-2,3-Q-4-S- Alb, E2-3,4-Q-2-S-Alb plus E2-2,3-Q-4-S-Alb, and ratio of E2-3,4-Q-2-S-Alb to E2-2,3-Q-4-S-Alb are aggregated into a database to afford the establishment of a model, entitled “mixed discriminant analysis”, to screen for high risk of breast cancer, and to explore the distribution of the background levels of these biomarkers in 5-year survivors.
In the “mixed discriminant analysis”, we estimated the values of arithmetic mean, standard deviation, and quartiles of the four biomarkers, and summarized the characteristics of each ethnic group, including age and body mass index. Levels of these biomarkers in subjects with values greater than arithmetic means are classified as high risk group whereas those less than means as low risk group. Similar approach was applied to the s”eparate mean discriminant analysis”. To evaluate the prediction values, we calculated the respective positive predictive value (PPV), negative predictive value (NPV)、False positive rate (Fpr), false negative rate (Fnr), sensitivity, and specificity.

Results indicated that using E2-3,4-Q-2-S-Alb and ratio of E2-3,4-Q-2-S-Alb to E2-2,3-Q-4-S-Alb as biomarkers, the prediction values were estimated as follow: PPV (100%), NPV (more than 79%), Fpr (0%), Fnr (less than 22%), sensitivity (78.1%), specificity (100%), and odds ratio (6.41). We concluded that most of the values of these biomarkers in 5-year survivors were classified as low-risk groups, which is comparable with those findings obtained using the linear discriminant analysis model.
摘要 i
英文摘要(Abstract) ii
中英對照縮寫表 iii
目錄 v
圖目錄 viii
表目錄 x
第一章 前言 1
1-1 研究緣起 1
1-2 研究目的 3
第二章 文獻回顧 4
2-1 全球癌症現況 4
2-2 乳癌 7
2-2-1 乳房醫學檢查 7
2-2-2 乳癌分期及存活率 8
2-2-3 乳癌病理報告 10
2-3 雌激素 12
2-4 乳癌之風險因子 15
2-4-1 性別 15
2-4-2 年齡 15
2-4-3 肥胖與身體質量指標 (Body Mass Index,BMI) 15
2-4-4 內外部性原因 16
2-4-5 家族病史 17
2-4-6 生活方式 18
2-5 乳癌治療方式及機制 19
2-5-1 手術 20
2-5-2 放射療法 22
2-5-3 全身療法 22
2-5-4 化學療法(Chemotherapy) 23
2-5-5 靶向治療 23
2-5-6 荷爾蒙療法 24
2-6 基因多型性(Genetic Polymorphism) 27
2-7乳癌預防醫學生物指標 29
2-7-1 蛋白質胼合物 29
2-7-2 以蛋白質胼合物研究血清中萘醌的累積組織劑量 33
2-7-3 以多環芳香族碳氫化合物與雌激素的生物活化 34
2-7-4 血紅蛋白胼合物雌激素醌與乳癌的危險因素 35
2-7-5 乳癌患者白蛋白胼合物雌激素-3,4-醌體內負荷調查 36
2-7-6 以雌激素的Hb和Alb作為生物標誌物的早期發現乳癌 37
2-7-7 使用蛋白質胼合物之篩選模式 38
第三章 實驗架構與研究假設 40
3-1 Mixed discriminant analysis (混合判定分析)之族群分布 41
3-1-1混合平均值判定分析族群分布 42
3-1-2混合四分位數判定分析之族群分布 43
3-2 Separate mean discriminant analysis (個別平均值判定分析)之族群分布 44
第四章 實驗材料與方法 45
4-1 實驗材料 45
4-2 彰化基督教醫院乳癌病人之基本資料(血清白蛋白質) 46
4-3 苗栗大千醫院正常婦女對照組基本資料(血清白蛋白質) 49
4-4 彰化基督教醫院術後5年未復發之乳癌病人基本資料 52
4-5 統計分析 53
4-5-1 混合平均值判定分析 55
4-5-2 混合四分位數判定分析 56
4-5-3 個別平均值判定分析 57
第五章 實驗結果 58
5-1 混合判定分析及個別平均值判定分析之族群分佈 59
5-1-1 混合平均值判定分析之族群分佈 59
5-1-2 混合四分位數判定分析之族群分佈 68
5-1-3 個別平均值判定分析之族群分佈 73
5-1-4 小結 82
5-2 靈敏度(Sensitivity)、特異度(Specificity)與勝算比(Odds Ratio) 83
第六章 討論 85
6-1 混合判定分析平均值法、四分位數法之預測值比較 86
6-2 混合判定分析與個別平均值判定分析之間比較 88
6-3 混合判定分析與線性判別分析模式(linear discriminant analysis)之間的差異 90
6-4 模式分析後之5年存活者分布趨勢 92
6-5 乳癌診斷基因型ER、PR及HER2表現及治療藥物之對蛋白質胼合物影響 94
第七章 結論 96
第八章 未來工作 97
第九章 參考文獻 98
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