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 ％）。 ;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%.
