摘要:微小核醣核酸(microRNA) 由17-25個核苷酸組成, 主要功能是藉由干擾特定mRNA 的轉譯(Translation) 成蛋白質的功能而達到調節基因表現。近年來對miRNA的研究也增加對生物體功能的了解: 包括疾病發生機轉細胞功能及細胞和細胞的通訊和交互作用。這同時血液中循環的miRNA(Circulating miRNA) 也被研究做為生物標記物:血液中的miRNA 具有非侵襲性, 高敏感性和特異性;可以早期偵測疾病發生,隨病程中演變而變化,在檢體內有穩定性及長半衰期,而且可以經濟有效快速的在實驗室完成檢驗。對心臟血管系統疾病的miRNA的研究也在近年來蓬勃發展。許多研究顯示血液中的miRNA可以做為冠心症,心肌梗塞,心肌病變,心臟衰竭等疾病診斷及預後的生物標記物。 例如 miR-423-5p, miR-133a, miR-499 , miR-519e*, miR-520d, miR-1231, miR-200b*, miR-622, and miR-1228* 等miRNA在心臟衰竭患者血液中的濃度會上升.葉克膜extracorporeal membrane oxygenation(ECMO)可以對嚴重心臟衰竭患者提供暫時性支持及恢復的機會; 然而目前成功率仍然跼限於30-50%。 更精緻的適應症病患選擇, 預測成果及治療中的監測,甚至何時該中止治療仍然在積極研究中。血液中的miRNA可以用定量聚合酶鏈反應(Q-PCR)在實驗室中迅速(<一天)測量出結果, 因此有實際在臨床應用的可行性。這研究的目的是找出用血液中的miRNA做為葉克膜病患的預後因子:我們預計利用三年時間做到尋找預後因子及 驗證兩個階段。根據收案進度第一年及第二年以尋找預後因子為目標。預計收到心衰竭使用葉克膜的病患, 以20個存活者和20個非存活者做比較。在病患使用葉克膜之前及裝置葉克膜的第2天,收取血漿檢體, 抽取RNA 後用次世代定序(Next Generation Sequencing, NGS)技術做基因高通量分析 (high-throughput analysis) 來分析miRNA 表現量。再對照miRNA資料庫進行生物統計分析找出預測存活的miRNA 表現; 做成預測模型(prediction model)。預計第三年進行miRNA預測模型驗證階段:這階段病患預計收案到40個心衰竭使用葉克膜的病患,根據找到跟預後有關係的miRNA 做定量聚合酶鏈反應(Q-PCR)。 根據預測模型和實際的結果來驗證模型的敏感性sensitivity,特異性specificity和面積下曲線Area-under-curve (AUC)。
Abstract: MicroRNAs (miRNAs)are short (17–25 nucleotides) non-coding RNAs, whose main function is to regulate gene expression by hindering the translation of specific mRNAs at the post transcriptional level. The recent studies on microRNAs resulted in better understanding of the biological mechanisms, including the development of cardiovascular diseases, cellular function and cell-to-cell communication.At the same time, the recent demonstration that cell-derived circulating miRs can be measured in the blood opens up their use as powerful biomarkers. Circulating miRNAs may fulfill most of the essential characteristics of a good biomarker: noninvasive measurability; a high degree of sensitivity and specificity, allowing early detection of pathological states; time-related changes during the course of disease; a long half-life within the sample; and rapid and cost-effective laboratory detection.The research on miRs as biomarker of cardiovascular disease showed promising results. Severalgroups have proposed circulating miRNAs as biomarkers for diagnosis and prognosis of cardiovascular pathologies ranging from heart failure, acute MI, and cardiomyopathies. For example,miR-423-5p, miR-133a, miR-499 , miR-519e*, miR-520d, miR-1231, miR-200b*, miR-622, and miR-1228* were found increased in patients with heart failure. However, the miRs expression in patients receiving extracorporeal membrane oxygenation(ECMO) support was never reported.ECMO support could provide chance of survival for patients with refractory cardiac failure, and resuscitate from cardiopulmonary collapse. Despite recent advance, the precise indication, prognosis predication, timing of initiation and withdrawn, were still under active investigation. The circulation miRNA could be measured by quantitative PCR within one day, so the data will have possibility for clinical use as a novel biomarker for diagnosis and prognosis.The goal of this study is to identify miRNA based marker to predict ECMO outcome.The 3-year study include the experimental cohort and validation cohort.In the first two years, we will enroll patients with ECMO support for cardiac origin, the specific aim of the experimental cohort is identification of miRNA based marker in ECMO for survivor vs non-survivor.The blood sample will be collected before ECMO initiation, and the next say day after ECMO initiation. The plasma miRNA will be analysed by Next-Generation Sequencing(NGS). NGS short reads are aligned to a known reference sequence database. Then the data was compared between the survivors and non-survivors in order to identify survival associated miRNAs. The model of prediction will be generated.In the third year, as the data is available, we will collect another cohort of patients. The blood sample was collected, and specific miRNAs will be measured by quantitative PCR (qPCR). The patients' outcome and the prediction data will validate the sensitivity, specificity, and Area-under-curve (AUC) of the predication model.