發展以血中微型 RNA 作為冠心症(CAD)的非侵入性疾病指標

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姓名

賴柏丞(Bo-Cheng Lai) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

發展以血中微型 RNA 作為冠心症(CAD)的非侵入性疾病指標
(Exploration of microRNAs as non-invasive biomarkers for coronary artery disease (CAD))

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至系統瀏覽論文 (2026-8-19以後開放)

摘要(中)

MicroRNA(miRNA)是近年來一種被廣為研究的項目之一，且被認為是轉錄後基因的重要調節關鍵，更與一些癌症疾病的產生有關，可以用於相關的治療。
冠心症(Coronary artery diease, CAD)是一種心血管疾病，成因通常為供給心臟氧氣的粥狀動脈變得狹窄或冠狀動脈被斑塊(plaque)所阻塞造成心肌細胞缺氧而發生的。由於CAD的致死率高，且目前準確的早期診斷方法較缺乏，若是可以找出一可行的生物標記去做準確的早期預測，應該可以改善CAD致死率。
而miRNAs可以由周邊體液採集並分離而得，容易取得且穩定性也高，我們希望可以發展一由miRNAs組成的非侵入性診斷標記。根據研究指出，miRNAs和一些代謝和內分泌是相關的，而這些代謝或內分泌可能影響了CAD的發展，故我們認為miRNAs是有潛力發展成可行的CAD預測標靶。
本實驗採用世代研究法(cohort study)，運用了一群原先健康的群體資料，追蹤其從健康到發病間的變化，藉由聯新國際醫院生物資料庫中取得四組同一受試者在未罹患冠心症及已罹患冠心症之血液樣本，並利用高通量qPCR和microRNA PCR array (為定量microRNA的實驗方法)及miRNA 次世代定序的方式去分析四組受試者血液樣本之間的miRNAs表現量差異，結果發現有些miRNAs是有明顯的區別，因此挑選出15個有潛力的miRNAs。之後又收144位受試者的血清利用Real time PCR 定量其miRNA，篩選出來15個候選miRNAs再經過144 例臨床個案之樣本作為訓練組後計算，建立出的組合公式，可明顯區分出ACS病患與3年內皆未得ACS。模型公式所顯示出最佳曲線下面積（AUC）為0.96並且敏感度（91 ％）和特異度（87.8 ％）。

摘要(英)

MicroRNA (miRNA) is one of the most widely studied topics in recent years, and is considered to play an important role to the regulation of post-transcriptional genes. It is also related to the occurrence of some cancer diseases and can be used for related treatments.
Coronary artery disease (CAD) is a cardiovascular disease that usually occurs when the atherosclerotic arteries that carries oxygen to the heart becomes narrowed or the coronary arteries are blocked by plaques, causing myocardial cell hypoxia. Owing to the high mortality rate of CAD and the lack of accurate early diagnosis methods, if a suitable biomarker can be found for early prediction, it should improve the mortality rate of CAD.
And miRNAs can be collected and separated from surrounding body fluids, which are easy to get and have high stability. We hope to develop a non-invasive diagnostic marker composed of miRNAs. According to previous study, miRNAs are related to some metabolisms and endocrines, and these metabolisms or endocrines may affect the development of CAD. Therefore, we believe that miRNAs have the potential to develop into reliable CAD biomarker.
This screen subjects were selected from a cohort study, using a selected group data to track its changes from health to onset. We collected blood samples from the same patient before and after ACS occurrence from Landseed cohort database. High-throughput quantification real-time PCR arrays and next generation sequencing (NGS) of miRNA were used to analyses the expression difference before and after ACS occurrence among 4 patients. The results indicate there are 15 potential miRNAs displayed significant differences The candidate miRNAs were then verifed by single qRT-PCR assays from 144 serum samples. The miRNA classifier for ancillary ACS detection was developed by multiple logistic regression analyses. The ROC analysis of the miRNA and TG classifier showed discrimination between ACS and non ACS patients and found that the area under curve (AUC) was 0.96. The classifier had a sensitivity of 91% and a specificity of 87.8%.

關鍵字(中)

★ 冠心症
★ 微型RNA

關鍵字(英)

★ Coronary artery diease
★ microRNA

論文目次

中文摘要.…………………………………………………………………………….v
Abstract ……………………………………………………………………………vii
誌謝…………………………………………………………………………………ix
表目錄………………………………………………………………………………xii
圖目錄 ……………………………………………………………………………xiii
一、緒論 1
1.冠狀動脈疾病(coronary artery diease) 1
1-1.冠狀動脈疾病的起因 1
1-2.冠狀動脈疾病的診斷 2
1-3.冠狀動脈疾病的治療 2
1-4.何為微型RNA 3
1-5.miRNA的作用機制 3
1-6. miRNA 作為CAD 的生物標記之重要性及優勢 4
1-7. 研究目的 4
二、實驗材料和方法 5
2-1. 實驗材料: 5
2-1-1 病患檢體 5
2-1-2 即時定量聚合酶鍊鎖反應與微型RNA的偵測: 5
2-1-3 萃取RNA所用化學藥劑: 5
2-1-4 數據分析軟體: 6
2-2 實驗方法: 6
2-2-1 實驗步驟: 6
2-2-2 分離血液檢體中的血清 6
2-2-3 微型RNA的萃取 6
2-2-4 微型RNA的RT-qPCR 實驗 7
2-2-5 資料分析 7
2-2-5-1 盒狀圖分析 7
2-2-5-2 接收者操作特征曲線分析 8
2-2-5-3 邏輯式回歸與數據的正規化 8
三、實驗結果 9
3-1.查篩組之微型RNA篩選 9
3-2.訓練組之微型RNA模型的建立 9
3-2-1 訓練組病患背景資料 9
3-2-2 訓練組之微型RNA實驗 9
3-2-3 訓練組之生化指標分析 10
3-2-4 訓練組之多元邏輯回歸分析 11
3-2-5 運用多元邏輯回歸分析建立出的miRNAs及TG組合公式 11
四、結果討論 12
五、參考文獻 14
六、圖 19
七、表
Table 1. Most possible miRNAs which are related to CAD after screening set. 27
Table 2. Distribution of the clinical status of patients in training group. 28
Table 3. Candidate miRNAs profile among training set samples between ACS and non ACS, the ROC analysis of the ratio of miRNA expression. 29
Table 4. The multiple logistic regression of of the ratio of miRNA expressions from training set. 30
八、附錄 31

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指導教授

馬念涵(Nian-Han Ma)

審核日期

2020-8-19

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