Identifying Prostate Cancer-related Networks from Microarray Data Based on Genotype-Phenotype Networks Using Markov Blanket Search

基於基因與表現型態和馬可夫搜尋法從生物晶片中辨識前列腺癌相關網路

劉怡瑜(Yi-Yu Liu)

指導教授 ： 蘇豐文

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2010年)

https://doi.org/10.6843/NTHU.2010.00479

摘要

癌症是一種基因突變所造成且複雜的疾病，而且目前只有少數的人類基因被提出來可能為致癌基因。在分子基因學領域當中，有許多研究已經驗證不同癌症的一些重要基因以及辨識與基因表現資料符合的有意義的生物網路去了解細胞的生物機制是一個重要的議題。我們整合表現型態網路、蛋白質網路和應用貪心式馬可夫搜尋法有效地利用基因表現資料和蛋白質交互作用網路去辨識人類疾病有意義的網路。我們利用前列腺癌資料作為我們的研究範圍。在與統計的方法比較當中，如t-test檢定和wilcoxon檢定，我們的方法比目前已有的資料庫和文獻辨識出更多與前列腺癌相關的基因。我們辨識的疾病相關基因有更高的準確率以及至少高於1.5倍的F-measure。功能的模組包括前列腺癌是一個過量表現的Interleukin-type、insulin-like生長因子和RAS相關致癌基因都由我們的方法辨識出來。細胞訊息傳遞、免疫防禦、細胞週期和細胞激素交互作用標準路徑也被發現跟前列腺癌相關。我們提出來的方法有效地利用基因表現資料、表現型態和蛋白質網路去辨識想要探討的疾病的子網路和基因。那些有意義的基因和結合的網路可能是了解前列腺癌機制的對象。我們的方法將可以更有效且正確去整合生物晶片資料和表現型態網路去辨識疾病相關的網路。
關鍵字:前列腺癌、生物晶片資料、蛋白質交互作用網路、馬可夫搜尋法、表現型態網路

關鍵字

前列腺癌 ； 生物晶片資料 ； 蛋白質交互作用網路 ； 馬可夫搜尋法 ； 表現型態網路

並列摘要

Many researches in molecular genetics area have identified a number of important genes of various types of cancers and the identification of significant biological networks corresponding to gene expression data has been an important issue in understanding underlying biological mechanisms of cells. We integrate phenotype networks, protein networks and apply a greedy Markov blanket search method that efficiently utilizes both gene expression data and protein-protein interaction networks to identify significant networks as well genes for a human disease. We use prostate cancer data as our test domain. In comparison with such statistical methods as t-test and wilcoxon test, our method identifies more prostate cancer-related genes than those reported in published database and literature. We identify disease-related genes with higher precision and at least 1.5 fold higher F-measure. The functional modules involved in the prostate cancer is over-expressed Interleukin-type, insulin-like growth factors and well-known RAS related oncogenes are identified by our method. Cell signaling, immune response, cell cycle and cytokine interactions canonical pathways are also found to be significantly related to prostate cancer. Our proposed methods efficiently utilize gene expression, phenotype and protein networks in identifying the sub-networks and genes that might be related to the disease under interest. Those significant genes and the associated networks may be the subjects to understand the mechanism of prostate cancer. Our method would be more powerful and accurate to integrate the microarray data and the phenotype network for identifying the disease-related genes and networks.
Keywords: Prostate Cancer, Microarray data, Protein-protein interaction networks, Markov Blanket search, Phenotype networks

參考文獻

[1] Vogelstein B, Kinzler KW:Cancer genes and the pathways they control. Nature Med 2004,10(8):789–799.

[4] Strausberg RL, Simpson AJ, Wooster R: Sequence-based cancer genomics: progress,lessons and opportunities. Nature Rev Genet 2003,4(6):409–418.

[5] Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, Ding W, et al.: A strong candidate for the breast and ovarian ancer susceptibility gene BRCA1. Science 1994,266(5182):66–71.

[6] Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, Collins N, Gregory S, Gumbs C, Micklem G: Identification of the breast cancer susceptibility gene BRCA2. Nature 1995,378(6559):789-792.

[7] Oldenburg RA, Meijers-Heijboer H, Cornelisse CJ, Devilee P:Genetic susceptibility for breast cancer: how many more genes to be found? Crit Rev Oncol Hematol 2007, 63(2):125-149.