利用生物資訊方法預測治療肺癌之藥物標靶

In Silico Prediction of Therapeutic Drug Targets for Lung Cancer

吳泯祐(Min-You Wu)

指導教授 ： 黃建宏

共同指導教授 ： 吳家樂(Ka-Lok Ng)

國立虎尾科技大學/電機資訊學院/資訊工程研究所/碩士(2013年)

https://doi.org/10.6827/NFU.2013.00008

摘要

與癌症相關的蛋白質是研究治療癌症的一個潛在目標，但是如何在蛋白質中找出與癌症相關的藥物，是相當困難的，近年來，隨著微陣列晶片的技術發展，蛋白質與蛋白質之間的交互作用反應(PPI)的數據，和研究人體細胞與藥物之間的藥物反應之數據的增加，透過研究我們也可能更快速的找出藥物，在本論文中，我們試著透過確認癌症相關的差異性表達基因(DEGs)和相關的蛋白質交互作用預測可能的標靶藥物。
透過eBayes分析我們從ArrayExpress下載的基因表達量數據，從中得到與癌症有關的差異性表達基因，並建立了與其相應的兩組上調與下調的蛋白質交互作用網路的基因組，接著我們透過分析基因的特性，找出生物反應或者路徑，如細胞週期，細胞分裂等，最後我們將上調與下調基因透過Connectivity Map(CMAP)，去找到潛在的藥物與其標靶基因。
本論文的研究方法整合了差異性表達基因，蛋白質交互作用，網路分析與CMAP分析，得到生物路徑，預測標靶藥物與標靶基因，以及透過本論文的方法可以應用在其他癌症與其未來的研究方面。

關鍵字

肺癌 ； 微陣列資料分析 ； 差異性表達基因 ； 蛋白質交互作用 ； 基因集分析 ； 藥物標靶

並列摘要

Cancer-related proteins are potential targets for drug therapy. However, it is rather difficult to make connections from cancer through gene or protein to drug discovery. With the advancement of the microarray technology, accumulation of protein-protein interaction (PPI), and human cells treated with many chemicals data; it is now possible that the drug discovery process can be accelerated. This thesis attempts to identify cancer-related differentially expressed genes(DEGs)and their related protein interactions to predict possible drug targets.
By using eBayes to analyze gene expression data retrieved from ArrayExpress database, cancer-specific DEGs were obtained. We built the corresponding PPI network for both of the up- and down-regulated gene sets. Then, we performed enrichment analysis to find enriched biological processes and pathways; such as cell cycle or cell division. Finally, these sets of up- and down- regulated genes were submitted to the Connectivity Map(CMAP)web resource to identify potential drugs and their target genes.
The method carry out in this study was set up by integrating DEGs, PPI, pseudo-clique analysis and CMAP analysis to derive biological pathways, predict drugs and identify drug targets. The method proposed in this thesis could be applied to other cancers research in the future.

並列關鍵字

lung cancer ； microarray data analysis ； differentially expressed gene ； protein-protein interactions ； protein complexes ； gene enrichment analysis ； drug target

參考文獻

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