臺灣博碩士論文加值系統

鼻咽癌是個獨特的癌症，主要發生在南亞地區，起因於基因、病毒、食物及環境因子的複雜交互作用。許多不同表現的基因被發現與鼻咽癌有關；然而，如何從未整理的基因名單中排序出重要的基因仍然未知。此外，雖然同步化學放射治療是大多數鼻咽癌患者的主要治療方式，但屬後期的患者其預後不佳仍是未解決的問題。為了排序出重要的鼻咽癌基因及發現具潛力藥物，我們從已發表的微陣列分析資料及文獻收集了鼻咽癌558個高表現及993個低表現的基因。我們進一步假設將基因特徵轉化成蛋白質交互作用網路來分析網路拓樸學結構可對鼻咽癌致病因中的重要調控因子有深入的了解。較特別引起注意的是 “團”(clique)的出現，它是在推論的鼻咽癌網路中自己完全相連接的子圖。這些以團為基礎的集線點，與3個以上的查詢點相連，比鼻咽癌蛋白質交互作用網路中其他的點排序較前，被進一步以路徑分析得到24個高表現及6個低表現的瓶頸基因用來預測鼻咽癌致癌過程。此外，額外的致癌基因、抑癌基因、與蛋白質複合物相關的基因、及以功能性分析得到的基因與瓶頸基因綜合在一起得到38個高表現及10個低表現的最終基因特徵。我們分別利用初始及最終基因特徵來搜尋「連接網絡資料庫」 (Connectivity Map,簡稱CMap)，發現經由我们的方式來歸納簡化標的可以有效的發現具潛力的藥物。我們於3年後更新鼻咽癌基因特徵並重建鼻咽癌蛋白質交互作用網路。我們得到一個更緻密的蛋白質交互作用網路。「支持向量機」(Support Vector Machines,簡稱SVM) 被進一步用來分類對鼻咽癌有效的測試藥，利用藥物在連接網絡資料庫的基因表現資料來辨識對藥物敏感性基因，並預測出87個具潛力治療鼻咽癌的藥物。排序前兩名藥物，thioridazine及vorinostat，進一步證實可有效抑制鼻咽癌細胞。此外，根據文獻回顧，將近半數的預測藥物具抗癌作用。我們架設一個整合網站(http://140.109.23.188:8080/NPC)以利於未來鼻咽癌的研究。此經由電腦模擬從標的排序到辨識具潛力藥物的方法，可能對其他癌症研究也是一種有效方式。

Nasopharyngeal carcinoma (NPC) is a unique cancer occurring primarily in south Asia and is caused by a complex interaction of genetic, viral, dietary, and environmental factors. Many differentially expressed genes have been linked to NPC; however, how to prioritize therapeutic targets from un-sorted gene lists remains largely unknown. Besides, although concurrent chemoradiotherapy is the mainstay treatment for most NPC cases, the poor outcomes of patients at advanced stages present an unsolved problem that needs to be addressed. In order to prioritize the important genes and discover potential drugs for NPC, we first collected 558 up- and 993 down-regulated NPC genes from published microarray data and the primary literatures. We then postulated that conversion of gene signatures into the protein-protein interaction (PPI) network and analyzing the network topologically could provide insight into key regulators involved in tumorgenesis of NPC. Of particular interests were the presences of cliques, referred to as fully connected sub-graphs, in the inferred NPC networks. These clique-based hubs, connecting with more than three queries and ranked higher than other nodes in the NPC PPI network, were further narrowed down by pathway analysis to retrieve 24 up- and 6 down-regulated bottleneck genes for predicting NPC carcinogenesis. Moreover, additional oncogenes, tumor suppressor genes, genes involved in protein complexes and genes obtained after functional profiling were merged with the bottleneck genes to form the final gene signatures of 38 up- and 10 down-regulated genes. We used the initial and final NPC gene signatures to query the Connectivity Map (CMap) respectively and found that target reduction via our pipeline could efficiently uncover potential drugs. We then updated the NPC gene signatures and reconstructed the NPC PPI network 3 years later. A more highly interconnected network was obtained. Support Vector Machines (SVM) were further utilized to classify the effectiveness of tested drugs against NPC using their gene expression from CMap, to identify several chemically sensitive genes, and to predict 87 drugs with potential for treating NPC. The two top-ranked drugs, thioridazine and vorinostat, were further demonstrated to be effective in inhibiting NPC cells. Moreover, according the literature reviews, nearly half of the predicted drugs have anticancer effects. An integrative website (http://140.109.23.188:8080/NPC) was established to facilitate future NPC research. This in silico approach, from target prioritization to potential drugs identification, might be an effective method for various cancer researches.

English abstract …………………………………………………… 5-6
Chinese abstract………………………………………………………7
List of abbreviation…………………………………………………8
Introduction…………………………………………………………… 9-12
Materials and methods………………………………………………13-21
Results…………………………………………………………………… 22-30
Discussion …………………………………………………………… 31-42
Conclusion……………………………………………………………… 43
Perspectives…………………………………………………………… 43
References……………………………………………………………… 44-54
Tables………………………………………………………………………55-115
Figures……………………………………………………………………116-133
Publication list………………………………………………………134-135

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