臺灣博碩士論文加值系統

乳癌為女性最常見的惡性腫瘤，依據世界衛生組織國際癌症研究機構(IARC)公布2020年全球最新癌症數據報告顯示，新發乳癌案例達226萬，正式取代肺癌，成為全球第一大癌症。有鑑於此，本研究目的係透過篩選驗證模式，彙整本實驗室分析完成之人體內癌變生物指標-雌激素醌類代謝物蛋白質胼合物(Hemoglobin adducts)及缺鹼基核酸位點(AP Sites)之背景值進行二指標相互關係所衍生之風險指標，透過混合判別統計分析模式界定以篩選出乳癌高風險族群並探討雌激素醌類代謝物蛋白質胼合物及缺鹼基核酸位點衍生之風險指標所得之基因突變協同效應。
混合判別分析模式係以本實驗室分析完成之147位乳癌病人與140位健康對照組樣本數據，針對Ln AP sites × Ln E2-3,4-Q-2-S-Hb、Ln AP sites × Ln E2-2,3-Q-4-S-Hb、[Ln AP sites × (Ln E2-3,4-Q-2-S-Hb + Ln E2-2,3-Q-4-S-Hb)]建立混合判別分析模式(混合平均值判別分析模式及混合四分位數判別分析模式)，並觀察樣本數據年齡、BMI等背景資料數據分布關係，並透過另一批次73位乳癌病人進行模式篩選驗證，探討模式整體適用性及基因突變協同效應。
透過模式建立篩選之結果顯示，混合判別分析模式以73位乳癌病人驗證整體靈敏度為93.4%，偽陰性率為6.6%，顯示模式有良好之預測效果，AP Sites及Hemoglobin adducts兩者濃度越高，其相乘所得之協同效應可視為罹患乳癌風險越高。
本次模式應用之AP Sites屬於Total AP Sites，建議未來可透過AP Sites切除酵素分析以進一步使用特定結構AP Sites與Hemoglobin adducts衍生風險指標進行乳癌罹患風險模式建立及篩選，可大幅提高整體預測準確率。

Breast cancer is the most common malignant tumor in women. According to the recent reports by the International Agency for Research on Cancer (IARC), the number of new cases of breast cancer reached 2.26 million in 2020, officially replacing lung cancer as the one leading cancer in the world. In the light of this, the purpose of this study is to apply the background values of estrogen-derived hemoglobin (Hb) adducts, including E2-3,4-Q-2-S-Hb and E2-2,3-Q-2-S-Hb,and apurine/apyrimidinic sites(AP Sites) in breast cancer patients (n=147) and healthy controls (n=140) as biomarkers and establish a prediction model to determine the risk of developing breast cancer.
We adopted the data of these two biomarkers after natural logarithm transformation, and multiplication of the results of these biomarkers, including Ln (AP sites) × Ln (E2-3,4-Q-2-S-Hb), Ln (AP sites) × Ln (E2-2,3-Q-4-S-Hb), Ln (AP sites) × (Ln E2-3,4-Q-2-S-Hb + Ln E2-2,3-Q-4-S-Hb) as indicators to perform and establish mixed discriminant analysis models.As distinguished by age and BMI, the distribution of these indicators were analyzed.Additionally, we verified the validity of these models using data derived from another batch of analysis in breast cancer patients (n=73) before treatment to investigate the overall prediction value of these models.
Results showed that the overall predictive value of the mixed discriminant analysis model was 93.4% with a false negative rate of 6.6%. The AP sites used in this analysis was the total number of AP sites. Further investigation is warranty by using the data derived from structural characterization of AP sites to substitute the total number of AP sites may dramatically improve overall prediction accuracy of these models.

摘要 i
Abstract ii
縮寫表 (List of Abbreviation)及中英對照表 iii
目錄 vi
表目錄 ix
圖目錄 xii
第一章 前言 1
1-1　研究緣起 1
1-2　研究目的 4
第二章　文獻回顧 5
2-1　乳癌 5
2-2　乳癌好發風險因子 7
2-2-1　性別 7
2-2-2　年齡 8
2-2-3　BMI(Body Mass Index) 8
2-2-4　家族遺傳 9
2-2-5　環境污染物 10
2-2-6　飲食及生活習慣 11
2-3　降低乳癌風險因子 16
2-3-1　飲食預防 16
2-3-2　運動預防 17
2-4　雌激素 18
2-4-1　雌激素代謝 20
2-4-2　雌激素與乳癌相關性 22
2-5　核酸損害與修復 24
2-5-1　核酸損害 24
2-5-2　缺鹼基核酸位點 24
2-5-3　核酸修復 25
2-6　生物指標 26
2-6-1　蛋白質胼合物之生物指標 26
2-6-2　缺鹼基核酸位點之生物指標 27
2-6-3　實驗室歷年乳癌預測生物指標 28
第三章　研究設計與流程 33
3-1　研究設計 33
3-2　研究流程 35
第四章　研究樣本與方法 37
4-1　研究樣本 37
4-2　研究分析方法 47
4-2-1　全血血液分離 47
4-2-2　雌激素醌類代謝物血紅蛋白胼合物製備及分析 48
4-2-3　核酸萃取及核酸醛基損害分析方法 50
4-3　統計分析方法 52
4-3-1　風險指標建立 52
4-3-2　混合判別分析模式建立 53
4-3-3　模式適用性指標 54
第五章　研究結果 55
5-1　模式建立結果 56
5-1-1　混合平均值判別分析模式建立結果 56
5-1-2　混合四分位數判別分析模式建立結果 62
5-1-3　模式適用性指標計算(模式建立) 68
5-2　模式驗證結果 70
5-2-1　混合平均值判別分析模式驗證結果 70
5-2-2　混合四分位數判別分析模式驗證結果 75
第六章　討論 80
6-1　混合平均值判別分析及混合四分位數判別分析比研析 80
6-2　風險指標比較 81
6-3　偽陰性探討 82
6-4　混合判別分析與線性判別分析模式比較及探討相互差異 83
6-5　風險指標相關性分析及基因突變率探討 86
6-6　歷年混合判別分析結果與本次實驗結果比較 88
6-7　環境污染物暴露與乳癌罹患風險關係探討 90
第七章　結論 92
第八章　未來工作 93
參考文獻 94

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