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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 高成炎(Cheng-Yan Kao) | |
dc.contributor.author | Chia-Hung Liu | en |
dc.contributor.author | 劉家宏 | zh_TW |
dc.date.accessioned | 2021-06-08T00:05:33Z | - |
dc.date.copyright | 2013-08-20 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-13 | |
dc.identifier.citation | 1. A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, T. Murray, and M. Thun, Cancer statistics, 2008, vol. 58, p. 71, Am Cancer Soc, 2008.
2. H.-L. Jia, Q.-H. Ye, L.-X. Qin, A. Budhu, M. Forgues, Y. Chen, Y.-K. Liu, H.-C. Sun,L. Wang, H.-Z. Lu, et al., Gene expression profiling reveals potential biomarkers of human hepatocellular carcinoma, Clinical cancer research, vol. 13, no. 4, pp. 1133-1139, 2007. 3. A. Cheng, Y. Kang, Z. Chen, C. Tsao, S. Qin, J. Kim, R. Luo, J. Feng, S. Ye, T. Yang, et al., Efficacy and safety of sorafenib in patients in the asia-pacic region with advanced hepatocellular carcinoma: a phase iii randomised, double-blind, placebo controlled trial, The lancet oncology, vol. 10, no. 1, pp. 2534, 2009. 4. J. Llovet, S. Ricci, V. Mazzaferro, P. Hilgard, E. Gane, J. Blanc, A. de Oliveira, A. Santoro, J. Raoul, A. Forner, et al., Sorafenib in advanced hepatocellular carcinoma, New England Journal of Medicine, vol. 359, no. 4, pp. 378390, 2008. 5. J. Llovet and J. Bruix, Molecular targeted therapies in hepatocellular carcinoma, Hepatology, vol. 48, no. 4, pp. 13121327, 2008. 6. D. Hanahan and R. Weinberg, Hallmarks of cancer: the next generation, Cell, vol. 144, no. 5, pp. 646674, 2011. 7. M. Vidal, M. Cusick, and A. Barabasi, Interactome networks and human disease, Cell, vol. 144, no. 6, pp. 986998, 2011. 8. D. Pe'er and N. Hacohen, Principles and strategies for developing network models in cancer, Cell, vol. 144, no. 6, pp. 864873, 2011. 78 9. X. Guo and X. Wang, Signaling cross-talk between tgf-/bmp and other pathways, Cell research, vol. 19, no. 1, pp. 7188, 2008. 10. A. Barabasi and Z. Oltvai, Network biology: understanding the cell's functional organization, Nature Reviews Genetics, vol. 5, no. 2, pp. 101113, 2004. 11. P. Kreeger and D. Lauenburger, Cancer systems biology: a network modeling perspective, Carcinogenesis, vol. 31, no. 1, p. 2, 2010. 12. U. Stelzl, U.Worm, M. Lalowski, C. Haenig, F. Brembeck, H. Goehler, M. Stroedicke, M. Zenkner, A. Schoenherr, S. Koeppen, et al., A human protein-protein interaction network: a resource for annotating the proteome, Cell, vol. 122, no. 6, pp. 957968, 2005. 13. B. Shoemaker and A. Panchenko, Deciphering protein-protein interactions. part i. experimental techniques and databases, PLoS computational biology, vol. 3, no. 3, p. e42, 2007. 14. J. De Las Rivas and C. Fontanillo, Protein-protein interactions essentials: key concepts to building and analyzing interactome networks, PLoS computational biology, vol. 6, no. 6, p. e1000807, 2010. 15. M. Stumpf, T. Thorne, E. De Silva, R. Stewart, H. An, M. Lappe, and C. Wiuf, Estimating the size of the human interactome, Proceedings of the National Academy of Sciences, vol. 105, no. 19, p. 6959, 2008. 16. P. Braun, M. Tasan, M. Dreze, M. Barrios-Rodiles, I. Lemmens, H. Yu, J. Sahalie, R. Murray, L. Roncari, A. De Smet, et al., An experimentally derived condence score for binary protein-protein interactions, Nature methods, vol. 6, no. 1, pp. 91 97, 2008. 17. S. Whittaker, R. Marais, and A. Zhu, The role of signaling pathways in the development and treatment of hepatocellular carcinoma, Oncogene, vol. 29, no. 36, pp. 49895005, 2010. 18. I. W. Taylor, R. Linding, D. Warde-Farley, Y. Liu, C. Pesquita, D. Faria, S. Bull, T. Pawson, Q. Morris, and J. L. Wrana, Dynamic modularity in protein interaction networks predicts breast cancer outcome, Nature biotechnology, vol. 27, no. 2,pp. 199204, 2009. 19. H.-Y. Chuang, E. Lee, Y.-T. Liu, D. Lee, and T. Ideker, Network-based classification of breast cancer metastasis, Molecular systems biology, vol. 3, no. 1, 2007.20. B. Vogelstein and K. W. Kinzler, Cancer genes and the pathways they control, Nature medicine, vol. 10, no. 8, pp. 789799, 2004. 21. R. Murphy, An active role for machine learning in drug development, Nature Chemical Biology, vol. 7, no. 6, pp. 327330, 2011. 22. M. Jarvius, J. Paulsson, I. Weibrecht, K. Leuchowius, A. Andersson, C. Wahlby, M. Gullberg, J. Botling, T. Sjoblom, B. Markova, et al., In situ detection of phosphorylated platelet-derived growth factor receptor using a generalized proximity ligation method, Molecular & Cellular Proteomics, vol. 6, no. 9, p. 1500, 2007. 23. O. Soderberg, M. Gullberg, M. Jarvius, K. Ridderstrale, K. Leuchowius, J. Jarvius, K. Wester, P. Hydbring, F. Bahram, L. Larsson, et al., Direct observation of individual endogenous protein complexes in situ by proximity ligation, Nature methods, vol. 3, no. 12, pp. 9951000, 2006. 24. S. Fredriksson, Visualizing signal transduction pathways by quantifying protein-protein interactions in native cells and tissue, Nature Methods, vol. 6, no. 4, 2009. 25. S. Ahmad, Y. Alsayed, B. Druker, and L. Platanias, The type i interferon receptor mediates tyrosine phosphorylation of the crkl adaptor protein, Journal of Biological Chemistry, vol. 272, no. 48, p. 29991, 1997. 26. L. Smit, G. van der Horst, and J. Borst, Sos, vav, and c3g participate in b cell receptor-induced signaling pathways and differentially associate with shc-grb2, crk, and crk-l adaptors, Journal of Biological Chemistry, vol. 271, no. 15, p. 8564, 1996. 27. S. Feller, Crk family adaptors-signaling complex formation and biological roles.,Oncogene, vol. 20, no. 44, p. 6348, 2001.80 28. Y. H. Kim, K. A. Kwei, L. Girard, K. Salari, J. Kao, M. Pacyna-Gengelbach, P.Wang, T. Hernandez-Boussard, A. F. Gazdar, I. Petersen, et al., Genomic and functional analysis identifies crkl as an oncogene amplified in lung cancer, Oncogene, vol. 29, no. 10, pp. 14211430, 2009. 29. B. Barleon, S. Sozzani, D. Zhou, H. Weich, A. Mantovani, and D. Marme, Migration of human monocytes in response to vascular endothelial growth factor (vegf) is mediated via the vegf receptor t-1, Blood, vol. 87, no. 8, p. 3336, 1996. 30. S. Kanno, N. Oda, M. Abe, Y. Terai, M. Ito, K. Shitara, K. Tabayashi, M. Shibuya, and Y. Sato, Roles of two vegf receptors, t-1 and kdr, in the signal transduction of vegf effects in human vascular endothelial cells., Oncogene, vol. 19, no. 17, p. 2138, 2000. 31. C. Hsu, J. Lai, C. Liu, H. Tseng, C. Lin, K. Lin, H. Yeh, T. Sung, W. Hsu, L. Su, et al., Detection of the inferred interaction network in hepatocellular carcinoma from EHCO(encyclopedia of hepatocellular carcinoma genes online), BMC bioinformatics, vol. 8, no. 1, p. 66, 2007. 32. K.-T. Lin, C.-H. Liu, J.-J. Chiou, W.-H. Tseng, K.-L. Lin, and C.-N. Hsu, Gene name service: no-nonsense alias resolution service for homo sapiens genes, in Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops, pp. 185188, IEEE Computer Society, 2007. 33. Q.-H. Ye, L.-X. Qin, M. Forgues, P. He, J. W. Kim, A. C. Peng, R. Simon, Y. Li, A. I. Robles, Y. Chen, et al., Predicting hepatitis b viruspositive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning,Nature medicine, vol. 9, no. 4, pp. 416423, 2003. 34. C.-C. Tsai, Y.-D. Chung, H.-J. Lee, W.-H. Chang, Y. Suzuku, S. Sugano, and J.-Y. Lin, Large-scale sequencing analysis of the full-length cDNA library of human hepatocellular carcinoma, Journal of biomedical science, vol. 10, no. 6, pp. 636643, 2003. 35. C.-C. Tsai, K.-W. Huang, H.-F. Chen, B.-W. Zhan, Y.-H. Lai, F.-h. Lee, C.-Y. Lin, Y.-C. Ho, Y.-W. Chao, Y.-C. Su, et al., Gene expression analysis of human hepatocellular carcinoma by using full-length cdna library, Journal of biomedical science, vol. 13, no. 2, pp. 241249, 2006. 36. W.-H. Su, C.-C. Chao, S.-H. Yeh, D.-S. Chen, P.-J. Chen, and Y.-S. Jou, Oncodb.hcc: an integrated oncogenomic database of hepatocellular carcinoma revealed aberrant cancer target genes and loci, Nucleic acids research, vol. 35, no. suppl 1, pp. D727D731, 2007. 37. H. Nguyen, S. Sankaran, and S. Dandekar, Hepatitis c virus core protein induces expression of genes regulating immune evasion and anti-apoptosis in hepatocytes, Virology, vol. 354, no. 1, pp. 5868, 2006. 38. K. Kato, R. Yamashita, R. Matoba, M. Monden, S. Noguchi, T. Takagi, and K. Nakai, Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues, Nucleic acids research, vol. 33, no. suppl 1, p. D533, 2005. 39. X. Xu, J. Huang, Z. Xu, B. Qian, Z. Zhu, Q. Yan, T. Cai, X. Zhang, H. Xiao, J. Qu, et al., Insight into hepatocellular carcinogenesis at transcriptome level by comparing gene expression profiles of hepatocellular carcinoma with those of corresponding noncancerous liver, Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 26, p. 15089, 2001. 40. O. Delpuech, J. Trabut, F. Carnot, J. Feuillard, C. Brechot, and D. Kremsdorf, Identication, using cDNA macroarray analysis, of distinct gene expression profiles associated with pathological and virological features of hepatocellular carcinoma, Oncogene, vol. 21, no. 18, pp. 29262937, 2002. 41. H. Okabe, S. Satoh, T. Kato, O. Kitahara, R. Yanagawa, Y. Yamaoka, T. Tsunoda, Y. Furukawa, and Y. Nakamura, Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray, Cancer Research, vol. 61, no. 5, p. 2129, 2001. 42. C. Liu, K. Lin, C. Huang, Y. Shann, Y. Lin, C. Kao, and C. Hsu, Genome-wide detection of putative oncofetal genes in human hepatocellular carcinoma by splicing pattern comparison, iConcept Transaction on Computational Intelligence in Bioinformatics (TCIB), vol. 1, no. 1, 2010. 43. M.-H. Chen, W.-L. R. Yang, K.-T. Lin, C.-H. Liu, Y.-W. Liu, K.-W. Huang, P. M.-H.Chang, J.-M. Lai, C.-N. Hsu, K.-M. Chao, et al., Gene expression-based chemical genomics identies potential therapeutic drugs in hepatocellular carcinoma, PloS one, vol. 6, no. 11, p. e27186, 2011. 44. A. Kamburov, U. Stelzl, H. Lehrach, and R. Herwig, The ConsensusPathDB interaction database: 2013 update, Nucleic Acids Research, vol. 41, no. D1, pp. D793D800, 2013. 45. J. Kelso, J. Visagie, G. Theiler, A. Christoels, S. Bardien, D. Smedley, D. Otgaar,G. Greyling, C. Jongeneel, M. McCarthy, et al., eVOC: a controlled vocabulary for unifying gene expression data, 2003. 46. R. Sorek and H. Safer, A novel algorithm for computational identication of contaminated EST libraries, Nucleic Acids Research, vol. 31, no. 3, p. 1067, 2003. 47. N. Thacker, F. Aherne, and P. Rockett, The Bhattacharyya metric as an absolute similarity measure for frequency coded data, Kybernetika, vol. 34, no. 4, pp. 363368, 1997. 48. A. Bateman, E. Birney, L. Cerruti, R. Durbin, L. Etwiller, S. Eddy, S. Griths-Jones, K. Howe, M. Marshall, and E. Sonnhammer, The Pfam protein families database, Nucleic acids research, vol. 30, no. 1, p. 276, 2002. 49. K. Lin, C. Liu, J. Chiou, W. Tseng, K. Lin, and C. Hsu, Gene Name Service: No-Nonsense Alias Resolution Service for Homo Sapiens Genes, Web Intelligence and Intelligent Agent Technology Workshops, 2007 IEEE/WIC/ACM International Conferences on:, pp. 185188, 2007. 50. S. Lee, C. Chan, T. Chen, C. Yang, K. Huang, C. Tsai, J. Lai, F. Wang, C. Kao, and C. Huang, POINeT: protein interactome with sub-network analysis and hub prioritization, BMC bioinformatics, vol. 10, no. 1, p. 114, 2009. 83 51. M. McDowall, M. Scott, and G. Barton, Pips: human protein-protein interaction prediction database, Nucleic Acids Research, vol. 37, no. suppl 1, p. D651, 2009. 52. J. Greenberg, D. Shields, S. Barillas, L. Acevedo, E. Murphy, J. Huang, L. Scheppke, C. Stockmann, R. Johnson, N. Angle, et al., A role for VEGF as a negative regulator of pericyte function and vessel maturation, Nature, vol. 456, no. 7223, pp. 809813, 2008. 53. C. Chen, Generalized association plots for information visualization: the applications of the convergence of iteratively formed correlation matrices, Statistica Sinica, vol. 12, pp. 123, 2002. 54. Y. Tien, Y. Lee, H. Wu, and C. Chen, Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles, BMC bioinformatics, vol. 9, no. 1, p. 155, 2008. 55. P. F. Jonsson and P. A. Bates, Global topological features of cancer proteins in the human interactome, Bioinformatics, vol. 22, no. 18, pp. 22912297, 2006. 56. K.-I. Goh, M. E. Cusick, D. Valle, B. Childs, M. Vidal, and A.-L. Barabasi, The human disease network, Proceedings of the National Academy of Sciences, vol. 104, no. 21, pp. 86858690, 2007. 57. T. Ideker and N. J. Krogan, Differential network biology, Molecular systems biology, vol. 8, no. 1, 2012. 58. S. Bandyopadhyay, M. Mehta, D. Kuo, M.-K. Sung, R. Chuang, E. J. Jaehnig, B. Bodenmiller, K. Licon, W. Copeland, M. Shales, et al., Rewiring of genetic networks in response to DNA damage, Science Signalling, vol. 330, no. 6009, p. 1385, 2010. 59. G. Abelev, Alpha-fetoprotein in ontogenesis and its association with malignant tumors, Adv Cancer Res, vol. 14, no. 295-358, p. 14, 1971. 60. N. Lee, K. Leung, N. Cheung, B. Lam, M. Xu, P. Sham, G. Lau, R. Poon, S. Fan, and J. Luk, Comparative proteomic analysis of mouse livers from embryo to adult reveals an association with progression of hepatocellular carcinoma, Proteomics, vol. 8, no. 10, 2008. 61. M. Roy, Q. Xu, and C. Lee, Evidence that public database records for many cancer associated genes reflect a splice form found in tumors and lack normal splice forms, Nucleic acids research, vol. 33, no. 16, p. 5026, 2005. 62. B. Modrek and C. Lee, A genomic view of alternative splicing, Nature genetics, vol. 30, pp. 1319, 2002. 63. C. Sugnet, K. Srinivasan, T. Clark, G. O'Brien, M. Cline, H. Wang, A. Williams, D. Kulp, J. Blume, D. Haussler, et al., Unusual intron conservation near tissue regulated exons found by splicing microarrays, PLoS Comput Biol, vol. 2, no. 1,p. e4, 2006. 64. E. Wang, R. Sandberg, S. Luo, I. Khrebtukova, L. Zhang, C. Mayr, S. Kingsmore, G. Schroth, and C. Burge, Alternative isoform regulation in human tissue transcriptomes, Nature, vol. 456, no. 7221, pp. 470476, 2008. 65. K. Breuhahn, T. Longerich, and P. Schirmacher, Dysregulation of growth factor signalling in human hepatocellular carcinoma, Oncogene, vol. 25, no. 27, pp. 3787 3800, 2006. 66. R. Sandberg, J. Neilson, A. Sarma, P. Sharp, and C. Burge, Proliferating cells express mRNAs with shortened 3'untranslated regions and fewer microRNA target sites, Science, vol. 320, no. 5883, p. 1643, 2008. 67. R. N. Kaplan, R. D. Riba, S. Zacharoulis, A. H. Bramley, L. Vincent, C. Costa, D. D. MacDonald, D. K. Jin, K. Shido, S. A. Kerns, et al., Vegfr1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche, Nature, vol. 438, no. 7069, pp. 820827, 2005. 68. G. Sriram and R. B. Birge, Emerging roles for crk in human cancer, Genes & cancer, vol. 1, no. 11, pp. 11321139, 2010. 69. C. M. Johannessen, J. S. Boehm, S. Y. Kim, S. R. Thomas, L. Wardwell, L. A. Johnson, C. M. Emery, N. Stransky, A. P. Cogdill, J. Barretina, et al., Cot drives resistance to raf inhibition through map kinase pathway reactivation, Nature, vol. 468, no. 7326, pp. 968972, 2010. 70. H. W. Cheung, J. Du, J. S. Boehm, F. He, B. A. Weir, X. Wang, M. Butaney, L. V. Sequist, B. Luo, J. A. Engelman, et al., Amplication of crkl induces transformation and epidermal growth factor receptor inhibitor resistance in human non small cell lung cancers, Cancer Discovery, vol. 1, no. 7, pp. 608625, 2011. 71. S.-T. Chiu, K.-J. Chang, C.-H. Ting, H.-C. Shen, H. Li, and F.-J. Hsieh, Overexpression of ephb3 enhances cell-cell contacts and suppresses tumor growth in ht-29 human colon cancer cells, Carcinogenesis, vol. 30, no. 9, pp. 14751486, 2009. 72. Y. Kobashigawa and F. Inagaki, Structural biology: Crkl is not crk-like, Nature Chemical Biology, vol. 8, no. 6, pp. 504505, 2012. 73. B. Hemmeryckx, A. van Wijk, A. Reichert, V. Kaartinen, R. de Jong, P. K. Pattengale,I. Gonzalez-Gomez, J. Groen, and N. Heisterkamp, Crkl enhances leukemogenesis in bcr/abl p190 transgenic mice, Cancer research, vol. 61, no. 4, pp. 13981405, 2001. 74. F. Fan, J. S. Wey, M. F. McCarty, A. Belcheva, W. Liu, T. W. Bauer, R. J. Somcio, Y. Wu, A. Hooper, D. J. Hicklin, et al., Expression and function of vascular endothelial growth factor receptor-1 on human colorectal cancer cells, Oncogene, vol. 24, no. 16, pp. 26472653, 2005. 75. J. S.Wey, F. Fan, M. J. Gray, T. W. Bauer, M. F. McCarty, R. Somcio, W. Liu, D. B. Evans, Y. Wu, D. J. Hicklin, et al., Vascular endothelial growth factor receptor-1 promotes migration and invasion in pancreatic carcinoma cell lines, Cancer, vol. 104, no. 2, pp. 427438, 2005. 76. T. Li, Y. Zhu, W. Ren, S. Xu, Z. Yang, A. Fang, and C. Qin, High co-expression of vascular endothelial growth factor receptor-1 and snail is associated with poor prognosis after curative resection of hepatocellular carcinoma, Medical Oncology, pp. 112, 2012. 77. A. Villanueva, Y. Hoshida, S. Toanin, A. Lachenmayer, C. Alsinet, R. Savic, H. Cornella, and J. M. Llovet, New strategies in hepatocellular carcinoma: genomic prognostic markers, Clinical Cancer Research, vol. 16, no. 19, pp. 46884694, 2010. 78. M. B. Thomas, J. S. Morris, R. Chadha, M. Iwasaki, H. Kaur, E. Lin, A. Kaseb, K. Glover, M. Davila, and J. Abbruzzese, Phase ii trial of the combination of bevacizumab and erlotinib in patients who have advanced hepatocellular carcinoma, Journal of Clinical Oncology, vol. 27, no. 6, pp. 843850, 2009. 79. R. Guiet, E. Van Goethem, C. Cougoule, S. Balor, A. Valette, T. Al Saati, C. A. Lowell, V. Le Cabec, and I. Maridonneau-Parini, The process of macrophage migration promotes matrix metalloproteinase-independent invasion by tumor cells, The Journal of Immunology, vol. 187, no. 7, pp. 38063814, 2011. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17297 | - |
dc.description.abstract | 肝癌是目前最惡性的癌症類型之一。肝癌生物標記的發現仍然緩慢,仍然需要開發更好的方法。最近的研究顯示建立在生物意義下的系統性的方法能夠較好的引導生物標記的發性。
在細胞的層面使用蛋白質交互作用來解析癌症反應路徑網路是一個具潛力但仍不完善的方法。此研究結合計算生物學的模型建構和抗體原位雜交技術的生物驗證,成功地在兩個肝癌細胞株中測量出67筆內生性的蛋白質交互作用,這67筆蛋白質交互作用連接了21個癌症反應路徑,兩個肝癌細胞株分別是Huh7 (轉移能力最低的肝癌細胞株)、Mahlavu (轉移能力最高的肝癌細胞株)。 因為這兩個細胞株的轉移能力有很大的差別,此蛋白質交互作用網路採用差異化網路生物學進行分析,此分析方法發現了一筆嶄新的交互作用現象, CRKL-FLT1,此交互作用在網路的拓樸結構最為重要,更多的生物功能性驗證顯示此交互作用的表現量不僅在肝癌細胞也和其他種類癌症的細胞轉移能力呈現高度正相關。 經由實驗也證明將上述兩個蛋白質的基因從肝癌細胞中移除會大幅降低肝癌細胞的轉移能力,透過實驗也證明了CRKL或FLT1會參與被視為癌細胞具備侵襲和轉移能力之重要過程上皮-間質細胞轉換過程。 為了確認此交互作用被用於預後標記的能力,192個肝癌樣本被使用於免疫組織化學法的實驗,實驗結果的統計分析顯示CRKL或CRKL-FLT1的表現與無疾病存活期和整體存活期有高度的正相關,顯示此交互作用可作為一個新的肝癌預後指標。此研究顯示整合性地研究疾病網路有助於發現新的生物標記,此方法具通用性也可使用在其他種類的癌症或複雜疾病。 | zh_TW |
dc.description.abstract | Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The pace of discovery of HCC biomarkers seems to be slowing and that advanced discovery methods are needed. Recent studies shows systems approaches can guide the identification of biomarkers based on a deeper understanding of their underlying biology.
Deciphering the network of signaling pathways in cancer via protein-protein interactions (PPIs) at the cellular level is a promising approach but remains incomplete. We used computational approaches to identify PPIs among interlinked pathways and an in situ proximity ligation assay to verify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). We then applied a differential network biology analysis and determined that the novel interaction, CRKL-FLT1, has a high centrality ranking. Further validation shows the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Moreover Knockdown of CRKL and FLT1 in HCC cells lead to a decrease in cell migration via ERK signaling and the epithelial-mesenchymal transition (EMT) process. Our immunohistochemical analysis shows high expression levels of CRKL and CRKL-FLT1 pair that strongly correlate with reduced disease-free and overall survival in HCC patient samples and a multivariate analysis further established CRKL and the CRKL-FLT1 as novel prognosis markers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T00:05:33Z (GMT). No. of bitstreams: 1 ntu-102-D96922022-1.pdf: 6017649 bytes, checksum: 7ba502a192589507bbd26bed5edc2343 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員會審定書
誌謝 中文摘要 Abstract Table of Contents i List of Figures iii List of Tables vi Chapter 1 Introduction 1 1.1 Hepatocellular carcinoma 1 1.2 Biological system and cancer 1 1.3 Motivation 8 1.4 Summary 10 Chapter 2 Materials and Methods 13 2.1 Generation of HCC-related genes 13 2.1.1 UCSF 14 2.1.2 CGED 14 2.1.3 FUDAN 14 2.1.4 PASTEUR 14 2.1.5 TOKYO 14 2.1.6 POFG 15 2.1.7 EHCO2 15 2.2 Generation of POFG Set 17 2.2.1 Splicing patterns and EST library data preparation for enriching EHCO2 17 2.2.2 The distribution of isoform expression 17 2.2.3 The estimation of similarity and signi_cance tests 18 2.2.4 Functional Validation of POFG by the Enriched Gene Ontology Terms 21 2.3 Identification of HCC-related pathways 21 2.4 The interlinking of HCC-related pathways via PPIs 21 2.5 Detection of PPIs by in situ proximity ligation assay 22 2.6 Clustering and visualization 24 2.7 Estimation of the degree centrality for differential hub 25 2.8 Migration assay 26 2.9 Viral infection 26 2.10 MTT assay for cell growth 26 2.11 Immunoprecipitation and immunoblotting 27 2.12 Patient clinicopathological data 27 2.13 Tissue microarray (TMA) construction and immunohistochemical staining 28 Chapter 3 Result 29 3.1 Identification of interlinked PPIs in cross-talk pathways in human hepatocellular carcinoma 29 3.2 Analysis of the differential interaction hubs in PPI networks 38 3.3 Functional and histopathological analysis of CRKL and FLT1 in HCC 42 3.4 CRKL is a novel marker and is associated with FLT1 expression and poor prognosis in HCC 46 Chapter 4 Discussion 53 Chapter 5 Conclusions 58 Chapter 6 Supplementary information 59 Bibliography 78 | |
dc.language.iso | en | |
dc.title | 系統生物學方法發現肝癌嶄新預後標記 | zh_TW |
dc.title | Systems Biology Approache Reveals Novel Prognostic Markers in Hepatocellular Carcinoma | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 黃奇英(Chi-Ying F Huang) | |
dc.contributor.oralexamcommittee | 賴飛羆(Feipei Lai),阮雪芬(Hsueh-Fen Juan),楊進木(Jinn-Moon Yang),游偉絢(Wei-Hsuan Yu) | |
dc.subject.keyword | 系統生物學,計算生物學,肝癌,蛋白質交互作用,反應路徑,生物標記,預後標記, | zh_TW |
dc.subject.keyword | systems biology,computational biology,hepatocellular carcinoma,protein-protein interactions,pathways,biomarker,prognostic marker, | en |
dc.relation.page | 87 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2013-08-13 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
顯示於系所單位: | 生醫電子與資訊學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
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ntu-102-1.pdf 目前未授權公開取用 | 5.88 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。