GSLHC － 運用基因組及層次類聚以生物功能群將有生物活性的複合物定性的方法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：5

、訪客IP：3.133.87.156

姓名

金鎮華(CHIN-CHEN HUA) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

GSLHC －運用基因組及層次類聚以生物功能群將有生物活性的複合物定性的方法
(Gene-Set Local Hierarchical Clustering (GSLHC) – A Gene Set-based Approach for Characterizing Bioactive Compounds in terms of Biological Functional Groups)

相關論文

★ 人類陰道滴蟲之Myb2蛋白質動態性質研究	★ 分析原核生物基因體複製起點與終點的反向對偶對稱現象
★ 分析基因體拷貝數變異所使用的兩種方法比較：隱藏馬可夫模型與成對高斯合併法	★ 使用兩種方法偵測基因體拷貝數變異：成對高斯合併法與隱藏馬可夫模型
★ 以整體晶片數據為母體應用於分析基因差異表達的z檢定方法	★ 一個檢定測量微晶片基因表達數據靈敏度的全統計計算法
★ 運用嶄新抗體固著策略發展及驗證新式抗體微晶片平台	★ Drug-resistant colon cancer cells produce high carcinoembryonic antigen and might not be cancer-initiating cells
★ 創傷性關節炎軟骨之退化進程－大鼠模型基因體圖譜研究	★ 基因體功能統合分析在阿茲海默症和大腦老化－近年阿茲海默症研發藥物失敗的理論問題探討
★ 運用時間序列微陣列資料來預測調控基因	★ 以大鼠嗜鉻性瘤細胞株建立神經訊號傳遞之細胞分子生物學模型
★ 一種找尋再利用藥物複合物來系統性治療複雜疾病的架構：大腸直腸腺瘤的應用	★ 以上皮細胞間質化與增生相關功能來描述癌症幹細胞之基因型
★ 從共表達差異基因對導出正常腦老化及因阿茲海默症特定腦區導致在功能性基因途徑與樞紐基因子網絡之變化	★ 以疾病進展趨勢挑選基因法識別正常腦老化與阿爾茨海默氏症在特定腦區引發的關鍵功能路徑與調節路徑之變化

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

自從2003年首次發表後，以基因組為基礎的分析方法(GSA)在基因表達微陣列已經被廣泛地運用來探索基於網路知識之生物功能表現型的相關性。GSA專注在一組相關基因上，且相對於獨立基因分析(IGA)來說有更多優點。其中包括了更好的準確性，強韌性以及生物關聯。但以往的GSA研究並沒有考慮會抑制功能的基因組之間的關係。因此我們提出一個分析方案「以基因組為基礎的局部階層式叢集」(GSLHC)。此方法可以提供從功能，藥物反應等反應的生物見解。我們成功的應用GSLHC從各種基因的分子特徵資料庫(MsigDB)中製作出了C-Map。GSLHC可以消除細胞株本身對於基因表現的影響。從IGA分析的結果證明細胞株的影響明顯優於樣本類型以及藥物標靶。此外，GSLHC根據最顯著的基因集確定了18種功能藥物最相關的分子，其中8種包含公認的抗癌藥物。因此，GSLHC將有助於了解藥物對於生物體的影響，重新定位藥物，常見疾病的基因組診斷，以及功能為基礎的類異構癌症亞型分類模式診斷。

摘要(英)

Gene set-based analysis (GSA) has been widely utilized on gene expression microarray to explore the association of biological features with phenotypes based on a prior pathway knowledge since its first application in 2003. GSA focuses on sets of related genes and has exhibited major advantages over on individual gene analysis (IGA) with respect to greater accuracy, robustness, and biological relevance. However, previous GSA studies have not considered the relationships within gene-sets which may shorten its functionalities and applications. Here, we presented an analytical framework called Gene Set-based Local Hierarchical Clustering (GSLHC) approach which may provide biologically valuable insights on coordinated actions on functionalities and improved classification of heterogeneous subtypes on drug-driven responses. We successfully applied GSLHC on the Connectivity Map (C-Map) dataset with various gene sets from the Molecular Signatures Database (MSigDB). The GSLHC approach eliminated cell type effects that was obviously observed by IGA and showed significantly better performance than IGA on sample clustering and drug-target association. Furthermore, based on sets of significantly enriched gene sets, GSLHC identified 18 unknown compounds which functionally associated with the most correlated drug neighbors, that 8 of them contain putative anti-cancer activities. With extended applicability, GSLHC will facilitate the gaining of the biological insights on unknown drug discovery, drug repositioning, gene-set pattern diagnosis of common disease, and function-based class categorization of heterogeneous cancer subtypes.

關鍵字(中)

★ 系統生物
★ 階層式叢集

關鍵字(英)

★ Hierarchical Clustering
★ system biology

論文目次

Chinese Abstract i
English Abstract ii
Acknowledgement iii
Table of Contents iv
List of Figures v
List of Tables v
Chapter 1 Introduction 1
Chapter 2 Materials and Methods 4
2.1 Drug treatment 4
2.2 Gene expression dataset 4
2.3 Data pretreatment 4
2.4 External database 5
2.5 Enrichment score 7
2.6 Significance by permutation 8
2.7 Cluster evaluation 8
2.8 Gene Set-based Local Hierarchical Clustering (GSLHC) 9
2.9 Identification of uncharacterized compounds 10
Chapter 3 Results 11
3.1 Comparison of performance between the local program and the C-Map server 11
3.2 Hierarchical clustering based on genes and functional gene sets 12
3.3 Evaluation on drug response clusters 15
3.4 Identification of uncharacterized active compounds 20
Chapter 4 Discussion 27
References 31
Appendix 35

參考文獻

[1] Perez-Diez, A., A. Morgun, and N. Shulzhenko, Microarrays for cancer diagnosis and classification. Adv Exp Med Biol, 2007. 593: p. 74-85.
[2] Miller, J.A., M.C. Oldham, and D.H. Geschwind, A systems level analysis of transcriptional changes in Alzheimer’’s disease and normal aging. J Neurosci, 2008. 28(6): p. 1410-20.
[3] Cui, X. and G.A. Churchill, Statistical tests for differential expression in cDNA microarray experiments. Genome Biol, 2003. 4(4): p. 210.
[4] Zaravinos, A., et al., Identification of common differentially expressed genes in urinary bladder cancer. PLoS One, 2011. 6(4): p. e18135.
[5] Tusher, V.G., R. Tibshirani, and G. Chu, Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A, 2001. 98(9): p. 5116-21.
[6] Smyth, G.K., Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 2004. 3: p. Article3.
[7] Pavlidis, P., Using ANOVA for gene selection from microarray studies of the nervous system. Methods, 2003. 31(4): p. 282-9.
[8] Ashburner, M., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1): p. 25-9.
[9] Kanehisa, M., et al., KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res, 2012. 40(Database issue): p. D109-14.
[10] Hosack, D.A., et al., Identifying biological themes within lists of genes with EASE. Genome Biol, 2003. 4(10): p. R70.
[11] Nam, D. and S.Y. Kim, Gene-set approach for expression pattern analysis. Brief Bioinform, 2008. 9(3): p. 189-97.
[12] Mootha, V.K., et al., PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet, 2003. 34(3): p. 267-73.
[13] Subramanian, A., et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 2005. 102(43): p. 15545-50.
[14] Efron, B. and R. Tibshirani, On Testing the Significance of Sets of Genes. Annals of Applied Statistics, 2007. 1(1): p. 107-129.
[15] Barry, W.T., A.B. Nobel, and F.A. Wright, Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics, 2005. 21(9): p. 1943-9.
[16] Breslin, T., P. Eden, and M. Krogh, Comparing functional annotation analyses with Catmap. BMC Bioinformatics, 2004. 5: p. 193.
[17] Lee, H.K., et al., ErmineJ: tool for functional analysis of gene expression data sets. BMC Bioinformatics, 2005. 6: p. 269.
[18] Dinu, I., et al., Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics, 2007. 8: p. 242.
[19] Prifti, E., et al., FunNet: an integrative tool for exploring transcriptional interactions. Bioinformatics, 2008. 24(22): p. 2636-8.
[20] Vaske, C.J., et al., Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics, 2010. 26(12): p. i237-45.
[21] Sun, C.H., et al., COFECO: composite function annotation enriched by protein complex data. Nucleic Acids Res, 2009. 37(Web Server issue): p. W350-5.
[22] Wong, D.J., et al., Revealing targeted therapy for human cancer by gene module maps. Cancer Res, 2008. 68(2): p. 369-78.
[23] Lamb, J., et al., The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 2006. 313(5795): p. 1929-35.
[24] Ben-Dor, A., et al., Discovering local structure in gene expression data: the order-preserving submatrix problem. J Comput Biol, 2003. 10(3-4): p. 373-84.
[25] Tanay, A., R. Sharan, and R. Shamir, Discovering statistically significant biclusters in gene expression data. Bioinformatics, 2002. 18 Suppl 1: p. S136-44.
[26] Li, Q.L., et al., PubChem as a public resource for drug discovery. Drug Discovery Today, 2010. 15(23-24): p. 1052-1057.
[27] Zhu, F., et al., Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res, 2012. 40(Database issue): p. D1128-36.
[28] Hollander, M. and D. Wolfe, Nonparametric Statistical Methods 2ed. 1999, New York: Wiley.
[29] Lamb, J., et al., A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell, 2003. 114(3): p. 323-34.
[30] Manning, C.D., P. Raghavan, and H. Schutze, Introduction to Information Retrieval: Cambridge University Press.
[31] Li, Q., et al., PubChem as a public resource for drug discovery. Drug Discov Today, 2010. 15(23-24): p. 1052-7.
[32] Richon, V.M., Cancer biology: mechanism of antitumour action of vorinostat (suberoylanilide hydroxamic acid), a novel histone deacetylase inhibitor. Br J Cancer, 2006. 95(S1): p. S2-S6.
[33] Gottlicher, M., et al., Valproic acid defines a novel class of HDAC inhibitors inducing differentiation of transformed cells. EMBO J, 2001. 20(24): p. 6969-78.
[34] Kemp, M.G., et al., The histone deacetylase inhibitor trichostatin A alters the pattern of DNA replication origin activity in human cells. Nucleic Acids Res, 2005. 33(1): p. 325-36.
[35] Keen, J.C., et al., A novel histone deacetylase inhibitor, scriptaid, enhances expression of functional estrogen receptor alpha (ER) in ER negative human breast cancer cells in combination with 5-aza 2’’-deoxycytidine. Breast Cancer Res Treat, 2003. 81(3): p. 177-86.
[36] Balakin, K.V., et al., Histone deacetylase inhibitors in cancer therapy: latest developments, trends and medicinal chemistry perspective. Anticancer Agents Med Chem, 2007. 7(5): p. 576-92.
[37] Wood, E.R., et al., Discovery and in vitro evaluation of potent TrkA kinase inhibitors: oxindole and aza-oxindoles. Bioorg Med Chem Lett, 2004. 14(4): p. 953-7.
[38] Lahusen, T., et al., Alsterpaullone, a novel cyclin-dependent kinase inhibitor, induces apoptosis by activation of caspase-9 due to perturbation in mitochondrial membrane potential. Mol Carcinog, 2003. 36(4): p. 183-94.
[39] Keller, H.U., A. Zimmermann, and V. Niggli, Diacylglycerols and the protein kinase inhibitor H-7 suppress cell polarity and locomotion of Walker 256 carcinosarcoma cells. Int J Cancer, 1989. 44(5): p. 934-9.
[40] Tan, C., et al., Daunomycin, an antitumor antibiotic, in the treatment of neoplastic disease. Clinical evaluation with special reference to childhood leukemia. Cancer, 1967. 20(3): p. 333-53.
[41] Rose, M.G., Hematology: Azacitidine improves survival in myelodysplastic syndromes. Nat Rev Clin Oncol, 2009. 6(9): p. 502-3.
[42] Ko, M.W., et al., Acute promyelocytic leukemic involvement of the optic nerves following mitoxantrone treatment for multiple sclerosis. J Neurol Sci, 2008. 273(1-2): p. 144-7.
[43] Kim, J.Y., et al., Ellipticine induces apoptosis in human endometrial cancer cells: the potential involvement of reactive oxygen species and mitogen-activated protein kinases. Toxicology, 2011. 289(2-3): p. 91-102.
[44] Ulukan, H. and P.W. Swaan, Camptothecins: a review of their chemotherapeutic potential. Drugs, 2002. 62(14): p. 2039-57.
[45] Rubin, B.K. and J. Tamaoki, Antibiotics as anti-inflammatory and immunomodulatory agents. Pir. 2005, Basel ; Boston: Birkhauser. xiii, 273 p.
[46] Sanders, W.E., Jr., Antibiotics during anesthesia and surgery. Int Anesthesiol Clin, 1968. 6(1): p. 211-8.
[47] Smith, T.J., S.A. Blackman, and S.J. Foster, Autolysins of Bacillus subtilis: multiple enzymes with multiple functions. Microbiology, 2000. 146 ( Pt 2): p. 249-62.
[48] Holtje, J.V., From growth to autolysis: the murein hydrolases in Escherichia coli. Arch Microbiol, 1995. 164(4): p. 243-54.
[49] Garcia, P., et al., LytB, a novel pneumococcal murein hydrolase essential for cell separation. Mol Microbiol, 1999. 31(4): p. 1275-81.
[50] Garman, K.S., et al., A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities. Proc Natl Acad Sci U S A, 2008. 105(49): p. 19432-7.
[51] Huang, L., et al., An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors. PLoS One, 2011. 6(7): p. e22274.
[52] Wang, G., et al., Expression-based in silico screening of candidate therapeutic compounds for lung adenocarcinoma. PLoS One, 2011. 6(1): p. e14573.
[53] Segal, M.R., et al., Querying genomic databases: refining the connectivity map. Stat Appl Genet Mol Biol, 2012. 11(2).
[54] Flynn, C., et al., Connectivity Map Analysis of NMD+ BMPR2 Related HPAH Provides Insights into Disease Penetrance. Am J Respir Cell Mol Biol, 2012.
[55] Damian, D. and M. Gorfine, Statistical concerns about the GSEA procedure. Nature Genetics, 2004. 36(7): p. 663-663.Mootha, V.K., et al., Statistical concerns about the GSEA procedure - Reply. Nature Genetics, 2004. 36(7): p. 663-663.

指導教授

李弘謙(Hoong-Chien Lee)

審核日期

2012-7-27

推文