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以基因、微型核醣核酸表現分佈為基礎探討人類冠狀心臟疾病

The Study of Coronary Artery Disease Based on Gene and MicroRNA Expression Profiles

作者:林恩如
畢業學校:慈濟大學
出版單位:慈濟大學
核准日期:2012-10-22
類型:Electronic Thesis or Dissertation
權限:Copyright information available at source archive--Tzu Chi University....

中文摘要

根據世界衛生組織統計資料顯示全世界約有1/3人口死於心血管疾病。衛生署最新公布100年國人十大死因,心臟血管疾病排名第二,心臟血管疾病死亡率一直高居不下,冠狀動脈心臟病(冠心病)是其中最重要原因之一。目前對於冠心病的臨床診斷,非侵入性方式(如:心電圖、血液檢測等)診斷準確率相對較低。如何發展出一非侵入性更具效力之臨床輔助診斷方式值得探討。
微型核醣核酸(microRNA),為近年來科學家發現之短片段之非編碼(non-coding)的RNA,藉由促進目標mRNA降解或抑制其轉錄,對目標基因表現具有調節作用,進而促進或抑制疾病的發生。因為其容易量測及非侵入性等特色,目前被認為極具作為臨床疾病診斷治療之生物標誌(biomarker)。
本研究主要目標,包括探討與冠心病相關具潛力之微型核醣核酸及基因生物標誌、相關聯網路分析及冠心病之疾病診斷分類。研究結合了微型核醣核酸表現及基因表現分佈資料,藉由統計方法(T檢定、q-value)、功能相關性、目標預測(PITA、miRTarBase)、整合性分析及基因演算法-支持相機(gene algorithm – support vector machine, GASVM)達成研究目標。
其中探討與冠心病相關具潛力之微型核醣核酸部分,使用miRTarbase目標預測方法所選出之5個顯著表現之微型核醣核酸(hsa-mir-21、hsa-mir-663、has-mir-122、has-mir-20a、has-mir-34b)中,目前已有3個(hsa-mir-21、hsa-mir-663、has-mir-122)具生物實驗證實與冠心病相關。探討與冠心病相關具潛力之基因部分,使用PITA目標預測及miRTarbase目標預測方法,所篩選出之基因其生物功能與前人研究所得結論相似,然而仍有許多基因(PNPLA2、FKBP8、SIRT5、DISC1、PBX2、SERPINB8、ADM2、H6PD、ITPK1、MGLL、SLC2A1、JUND、NFAT2IP、RUNX1)目前雖然尚未有直接生物證據指出其與冠心病之關聯,然而根據其基因功能,推測出其可能潛在之關聯性,提供未來能進一步探索的方向。網路關聯性部分,hsa-mir-663與JUND及hsa-mir-122與NFATC2IP之網路或許能提供未來更進一步了解冠心病相關調節機制及治療方針的參考基礎。在冠心病診斷分類上,由PITA目標預測所選出具潛力微型核醣核酸於GASVM分類效能與以往傳統非侵入式診斷分類效能相比確實有相當的提升,達到分類準確率89.75%。

英文摘要

Introduction: According to the statistics record from World Health Organization, one third global population dies from heart disease; coronary heart disease (CAD) is one of the main causes. In clinical assessment of heart disease, biomarkers become key criteria. MicroRNAs, ~22 nucleotide non-coding RNAs, regulate negatively to their target genes via degradation or translational inhibition. Recent studies reveal that microRNAs play as important regulators of development and stress responsiveness of heart. Discovering microRNA biomarkers of heart disease is an increasingly aware issue.
Methods: In this study, statistic methods, functional similarity scores, and target prediction systems are used to discover the potential microRNA/gene CAD biomarkers and build up the potential CAD related microRNAs-targets visualization network. Moreover, gene algorithm  support vector machine (GASVM) is implemented for the classification of CAD.
Results: In this study, 3 out of 5 microRNAs selected via miRTarBase network are verified with the previous biological studies of CAD. In addition to genes involved in cell growth and immunity, genes with function of apoptosis as well as cellular metabolism regulator may also be potential CAD related biomarkers. The selected genes: PNPLA2, FKBP8, SIRT5, DISC1, PBX2, SERPINB8, ADM2, H6PD, ITPK1, MGLL, SLC2A1, JUND, NFAT2IP, and RUNX1, existing the potential connections with CAD need further verification. The visualization network provides the potential microRNA-target pairs, hsa-mir-663-JUND as well as hsa-mir-122-NFATC2IP, for further study. The accuracy of CAD classification with GASVM is up to 89.75% certainly improving the classification performance of the traditional non-invasive diagnosis methods.


召集人 - 洪旭偉

口試委員 - 黃少偉

指導教授 - 陳信志


 

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