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研究生:吳泯祐
研究生(外文):Min-You Wu
論文名稱:利用生物資訊方法預測治療肺癌之藥物標靶
論文名稱(外文):In Silico Prediction of Therapeutic Drug Targets for Lung Cancer
指導教授:黃建宏黃建宏引用關係
指導教授(外文):CHIEN-HUNG HUANG
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:59
中文關鍵詞: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
外文關鍵詞:lung cancermicroarray data analysisdifferentially expressed geneprotein-protein interactionsprotein complexesgene enrichment analysisdrug target
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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。


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.


摘要.......................i
Abstract.......................ii
致謝.......................iv
表目錄.......................vii
圖目錄.......................viii
第一章 背景及目的.......................1
1.1 肺癌介紹.......................1
1.2 生物資訊.......................1
1.3 蛋白質體學.......................1
1.4 DNA微陣列.......................2
1.5 蛋白質交互作用研究.......................3
1.6 預測與分析蛋白質交互作用.......................3
第二章 背景知識.......................4
2.1 基因表達數據庫.......................4
2.2 生物路徑資料庫.......................6
2.3 蛋白質交互作用.......................7
2.4 數據分析.......................9
2.4.1 微陣列之表達量分析(Robust Multi-array Average).......................10
2.4.2 經驗貝氏定理(Empirical Bayes).......................12
2.5區域近似法(Clique Percolation Method).......................12
第三章 研究方法.......................15
3.1 研究方法流程.......................15
3.2 基因表達量處理.......................16
3.3 組合蛋白質交互作用.......................19
3.4 蛋白質交互作用篩選.......................21
3.5 與實驗交互作用資料比對.......................24
3.6 蛋白質路徑分析.......................27
3.7 篩選藥物之標靶基因.......................31
3.7.1 標靶藥物之篩選.......................32
3.7.2 標記蛋白質交互作用.......................33
第四章 研究結果.......................35
4.1 生物功能資料庫比對.......................35
4.1.1 生物路徑的交集率.......................35
4.1.2 生物路徑的分類.......................36
4.2 標靶基因與交互作用網路的關係.......................40
4.3 標靶基因與相鄰基因的關係.......................41
4.4 標靶藥物的調控.......................45
第五章 結論.......................47
參考文獻.......................49


參考文獻
[1]Arthyr M. Lesk, 2008, Introduction to Bioinformatics, 3rd, Long-Yuan Li, 2nd, 生物資訊, Jeou Chou Book, Taipei.
[2]David P. Clark, Lonnie D. Russell et al., 2004, Molecular Biology made simple and fun 2/e, 2nd, Yu-Wei Chu, 1st, 分子生物學-輕鬆學分生, Yihsient, Taipei.
[3]Huang da Wei, Brad T. Sherman and Richard A., 2009, “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Research, Volume 37, pp. 1-13.
[4]Kamburov Atanas, 2011, “ConsensusPathDB: toward a more complete picture of cellbiology,” Nucleic Acids Research, Volume 39, pp. 712-717.
[5]Federica Censi, Giovanni Calcagnini and Pierto Bartolini et al., 2010, “Principal component analysis of gene expression data: the case of atrial fibrillation,” ISABEL, pp. 7-10.
[6]Chris Stark, Breitkreutz Bobby-Joe and Teresa Reguly et al., 2006, “BioGRID: a general repository for interaction datasets,” Nucleic Acids Research, pp. 535-539.
[7]Senol Isci et al., 2012, “Detecting gene interactions within a Bayesian Network framework using external knowledge,” HIBIT, pp. 82-87.
[8]Ching-Hsiang Chen, 2010, Applied Statistics Using R, Tung Hua, Taipei.
[9]Setia Pramana, 2010, “IsoGene: An R Package for Analyzing Dose-response Studies in Microarray Experiments,” R Journal Volume.2, pp. 5-12.
[10]Xiao Sun, 2006, “R language and Bioconductor in genome analysis applied,” Science Press, Beijing.
[11]Irizarry, RA. Hobbs et al., 2003, “Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data,” Biostatistics, Volume 4, pp. 249-264.
[12]B. M. Bolstad, R. A. Irizarry, M. Astrand, T. P. Speed, 2003, “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias,” Volume 19, pp. 185-193.
[13]Efron, B. 2001, “Empirical Bayes Analysis of a Microarray Experiment,” American Statistical Association, pp. 1151-1160.
[14]Gordon K. Smyth, 2005, Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer, Springer New York, pp.397-420.
[15]Jianxin Wang, Binbin Liu, Min Li and Yi Pan, 2009, “Identifying protein complexes from interaction networks based on clique percolation and distance restriction,” BMC Genomics 2010, 11(Suppl 2):S10.
[16]Melissa S Cline, Michael Smoot, Ethan Cerami et al., 2007, “Integration of biological networks and gene expression data using Cytoscape,” Nature Protocols 2, pp. 2366–2382.
[17]Palla et al., 2007, “Quantifying social group evolution,” Nature, pp. 664-667.
[18]Zikai Wu, Yong Wang, 2012, “A new method to identify repositioned drugs for prostate cancer,” IEEE 6th, pp. 208-284.
[19]Benjamini Y., Yekutieli D., 2001, “The control of the false discovery rate in multiple testing under dependency,” Annals of Statistics 29, pp. 1165–1188.
[20]Sonya Vengrova, 2011, DNA Repair and Human Health, Intech, pp. 73-92.
[21]Jin-Mei Piao, Hee Nam Kim, Hye-Rim Song et al., 2011, “p53 codon 72 polymorphism and the risk of lung cancer in a Korean population,” ELSEVIER, pp. 264-267.
[22]Victoria Bolo’s, Joaquı’n Grego-Bessa, and Jose’ Luis de la Pompa, 2006, Notch Signaling in Development and Cancer,” The Endocrine Society, pp.339–363.
[23]Ko-Chun Yang, Tsung-Jui Chen, Sheng-An Lee et al., 2009, “Putative Candidate Drugs Based on Disease-related Microarray Genes to Assess Applicability Potential by Protein-protein Interaction Network and to Infer Appropriate New Indications by Connectivity Map,” JCMIT.2009.
[24]Ming-Ying Lan, Chi-Ying Huang et al., 2010, “From NPC Therapeutic Target Identification to Potential Treatment Strategy,” Mol Cancer Ther 2010, pp.2511-2523.
[25]Lina Chen, Yan Zhao, Liangcai, et al., 2009, “CTFMining: A Method to Predict Candidate Disease Genes Based on the Combined Network Topological Features Mining,” ICBBE, 3rd.
[26]Yu-Liang Lee, Jie-Wei Weng, Wen–Chin Chiang et al., 2011, “Investigating Cancer-related Proteins Specific Domain Interactions and Differential Protein Interactions Caused by Alternative Splicing,” IEEE 11th , BIBE, pp. 33-38.


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