研究生: |
張秉堯 Chang, Ping-Yao |
---|---|
論文名稱: |
藉由系統生物學來建構基因與表觀遺傳網路研究肝癌第一期到第四期的進行機制 Investigating the Progression Mechanism in Hepatocellular Carcinoma from Stage I to Stage IV by Constructing Genetic and Epigenetic Networks via Systems Biology |
指導教授: |
陳博現
Chen, Bor-Sen |
口試委員: |
楊嘉鈴
蘇士哲 王慧菁 陳博現 |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 英文 |
論文頁數: | 55 |
中文關鍵詞: | 肝癌 、進行機制 、表關遺傳學 、藥物設計 |
相關次數: | 點閱:2 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
肝癌是全世界癌症死亡的第三大死因。造成肝癌發展和進行的機制相當複雜且涉及了基因與表觀遺傳的調控。由於高通量技術的進步,使用此這些科技的大量資料來研究在肝癌中基因與表觀遺傳的改變成為可能。一些基因、蛋白質、miRNAs、和DNA甲機化被發現在肝癌的發展中有重要性,但這些結果缺乏了系統性的觀點來說明肝癌進行的機制。因此,藉由來自病患的mRNA、miRNA、甲基化的大量資料,我們使用了系統生物學方法以及大資料庫探勘來建構肝癌不同時期的基因與表觀遺傳網路(GENs),此網路結合了基因調控網路(GRNs)、蛋白質交互作用網路(PPINs)、以及表觀遺傳網路。為了找出每一期最重要的核心網路,主要網路投影(PNP)的方法被使用來萃取出核心網路成員。藉由比較臨近期肝癌的訊號路徑所包含的核心網路的成員,我們研究了DNA甲基化的改變和miRNA的異常調節,造成在肝癌進行中ErbB、MAPK、TGF-beta、和JAK-STAT訊號路徑擾動的影響,透過這些路徑,外部訊號進一步的傳遞到轉錄因子(TF)而異常的調控目標基因,進一步地造成有利於肝癌進行的細胞反映,包含:細胞增生,抗凋亡、異常的細胞週期、細胞存活、和轉移。此外,基於在每一期進行中表觀遺傳網路成員,我們設計了一些藥物標靶來做為藥物設計進而防止肝癌病患的病情惡化。在第一期到第二期的肝癌中,NTK2、MYC、AKT1被選為多藥物標靶而lestaurtinib、dinaciclib和perifosine則是相對應的多藥物分子。在第二期到第三期的肝癌中,DDIT3、PDGFB和JUN被選為多藥物標靶,而celecoxib、axitinib 和vinblastine則是相對應的多藥物分子。在第三期到第四期的肝癌中,STAT3、IL1B、和NFKB1被選為多藥物標靶,而atiprimod、celastrol和bortezomib則是相對應的多藥物分子。此研究所提出的方法不僅探究了肝癌進行的分子機制,也提供了多標靶給肝癌的多藥物設計。
Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. The mechanisms leading to development and progression of HCC are complicated and implicated in both genetic and epigenetic regulation. Thanks to the advance of high-throughput technologies, it becomes possible to use such technologies to investigate the genetic and epigenetic alteration in HCC with large amount of data. Some genes, proteins, miRNAs, and DNA methylation have been found important in the development of HCC, but these results are lack of a systematic view to elucidate the progression mechanisms of HCC. Therefore, with abundant data of mRNA profiles, miRNA profiles and methylation profiles from HCC patients, we used a systems biology approach and big database mining to construct genetic and epigenetic networks (GENs) that combine gene regulatory networks (GRNs), protein-protein networks (PPINs) and epigenetic network at different stages of hepatocarcinogenesis. For figuring out the most important core networks in each stage, principal network projection (PNP) is further facilitated to extract the core network markers. By comparing the pathways involved in core network biomarks between neighboring stages of HCC, we investigate how the changes of DNA methylation and aberrant regulations of miRNAs have impacts on the perturbation of ErbB, MAPK, TGF-beta, and JAK-STAT signaling pathways in the hepatocarcinogenesis, through which extracellular signals are further transduced to TFs to aberrantly regulate their target genes, resulting in favorable cellular responses for progression of HCC i.e. cell proliferation, anti-apoptosis, aberrant cell cycle, cell survival, and metastasis. Moreover, based on epigenetic network markers of each progression stage, we select several potential multiple drug targets for multiple drug design to prevent the progression for HCC patients; NTK2, MYC and AKT1 are selected as potential multiple drug targets and lestaurtinib, dinaciclib and perifosine are suggested as corresponding multiple drug molecules in HCC from stage I to stage II. DDIT3, PDGFB and JUN are selected as potential multiple drug targets and celecoxib, axitinib and vinblastine are suggested as corresponding multiple drug molecules in HCC from stage II to stage III. STAT3, IL1B and NFKB1 are selected as potential multiple drug targets and atiprimod, celastrol and bortezomib are suggested as corresponding multiple drug molecules in HCC from stage III to stage IV. The proposed method could not only investigate the progression molecular mechanisms of HCC but also provide potential multiple targets for multiple drug design of HCC.
1. Yang, J.D. and L.R. Roberts, Epidemiology and Management of Hepatocellular Carcinoma. Infectious Disease Clinics of North America, 2010. 24(4): p. 899-+.
2. Waly Raphael, S., Z. Yangde, and C. Yuxiang, Hepatocellular carcinoma: focus on different aspects of management. ISRN Oncol, 2012. 2012: p. 421673.
3. Venook, A.P., et al., The Incidence and Epidemiology of Hepatocellular Carcinoma: A Global and Regional Perspective. Oncologist, 2010. 15: p. 5-13.
4. Libbrecht, L., V. Desmet, and T. Roskams, Preneoplastic lesions in human hepatocarcinogenesis. Liver International, 2005. 25(1): p. 16-27.
5. Liu, M., L.X. Jiang, and X.Y. Guan, The genetic and epigenetic alterations in human hepatocellular carcinoma: a recent update. Protein & Cell, 2014. 5(9): p. 673-691.
6. Scaggiante, B., et al., Novel hepatocellular carcinoma molecules with prognostic and therapeutic potentials. World Journal of Gastroenterology, 2014. 20(5): p. 1268-1288.
7. Pogribny, I.P. and I. Rusyn, Role of epigenetic aberrations in the development and progression of human hepatocellular carcinoma. Cancer Letters, 2014. 342(2): p. 223-230.
8. Shibata, T. and H. Aburatani, Exploration of liver cancer genomes. Nature Reviews Gastroenterology & Hepatology, 2014. 11(6): p. 10.
9. Papaconstantinou, I., et al., The role of microRNAs in liver cancer. European Journal of Gastroenterology & Hepatology, 2012. 24(3): p. 223-228.
10. Hayes, J., P.P. Peruzzi, and S. Lawler, MicroRNAs in cancer: biomarkers, functions and therapy. Trends in Molecular Medicine, 2014. 20(8): p. 460-469.
11. Kozomara, A. and S. Griffiths-Jones, miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research, 2014. 42(D1): p. D68-D73.
12. Morishita, A. and T. Masaki, miRNA in hepatocellular carcinoma. Hepatology Research, 2015. 45(2): p. 128-141.
13. Chu, R., et al., miRNAs affect the development of hepatocellular carcinoma via dysregulation of their biogenesis and expression. Cell Communication and Signaling, 2014. 12: p. 12.
14. Huang, S. and X. He, The role of microRNAs in liver cancer progression. British Journal of Cancer, 2011. 104(2): p. 235-240.
15. Schwarzenbach, H., et al., Clinical relevance of circulating cell-free microRNAs in cancer. Nature Reviews Clinical Oncology, 2014. 11(3): p. 145-156.
16. Goldman, M., et al., The UCSC Cancer Genomics Browser: update 2015. Nucleic Acids Research, 2015. 43(D1): p. D812-D817.
17. Li, J.H., et al., starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Research, 2014. 42(D1): p. D92-D97.
18. Hsu, S.D., et al., miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Research, 2014. 42(D1): p. D78-D85.
19. Subramanian, A., et al., Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(43): p. 15545-15550.
20. Bovolenta, L.A., M.L. Acencio, and N. Lemke, HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. Bmc Genomics, 2012. 13: p. 10.
21. Zheng, G.Y., et al., ITFP: an integrated platform of mammalian transcription factors. Bioinformatics, 2008. 24(20): p. 2416-2417.
22. Chatr-aryamontri, A., et al., The BioGRID interaction database: 2015 update. Nucleic Acids Research, 2015. 43(D1): p. D470-D478.
23. Chen, B.S.a.C.C.W., Systems Biology: An Integrated Platform for Bioinformatics, Systems Synthetic Biology and Systems Metabolic Engineering. NOVA Science Publisher, 2014.
24. Yarden, Y. and M.X. Sliwkowski, Untangling the ErbB signalling network. Nature Reviews Molecular Cell Biology, 2001. 2(2): p. 127-137.
25. Kim, E.K. and E.J. Choi, Pathological roles of MAPK signaling pathways in human diseases. Biochimica Et Biophysica Acta-Molecular Basis of Disease, 2010. 1802(4): p. 396-405.
26. Vivanco, I., et al., A kinase-independent function of AKT promotes cancer cell survival. Elife, 2014. 3: p. 35.
27. Wang, Y.-y., R. Zhao, and H. Zhe, The emerging role of CaMKII in cancer. Oncotarget, 2015. 6(14): p. 11725-11734.
28. Sriram, G. and R.B. Birge, Emerging Roles for Crk in Human Cancer. Genes & Cancer, 2010. 1(11): p. 1132-1139.
29. Dang, C.V., MYC, Metabolism, Cell Growth, and Tumorigenesis. Cold Spring Harbor Perspectives in Medicine, 2013. 3(8): p. 15.
30. Bradley, J.R. and J.S. Pober, Tumor necrosis factor receptor-associated factors (TRAFs). Oncogene, 2001. 20(44): p. 6482-6491.
31. Zhang, W. and H.T. Liu, MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Research, 2002. 12(1): p. 9-18.
32. Kristjánsdóttir, K. and J. Rudolph, Cdc25 Phosphatases and Cancer. Chemistry & Biology. 11(8): p. 1043-1051.
33. Zhang, Z.L., et al., Plasma adrenomedullin levels and nasopharyngeal carcinoma prognosis. Clinica Chimica Acta, 2015. 440: p. 172-176.
34. Ropero, S. and M. Esteller, The role of histone deacetylases (HDACs) in human cancer. Molecular Oncology, 2007. 1(1): p. 19-25.
35. Dai, Z., et al., Capn4 contributes to tumour growth and metastasis of hepatocellular carcinoma by activation of the FAK-Src signalling pathways. Journal of Pathology, 2014. 234(3): p. 316-328.
36. Wagner, A.H., et al., DGIdb 2.0: mining clinically relevant drug–gene interactions. Nucleic Acids Research, 2015.
37. Vazquez-Ortiz, G., et al., Drug repurposing screen identifies lestaurtinib amplifies the ability of the poly (ADP-ribose) polymerase 1 inhibitor AG14361 to kill breast cancer associated gene-1 mutant and wild type breast cancer cells. Breast Cancer Research, 2014. 16(3): p. 14.
38. Richardson, P.G., et al., Perifosine, an oral, anti-cancer agent and inhibitor of the Akt pathway: mechanistic actions, pharmacodynamics, pharmacokinetics, and clinical activity. Expert Opinion on Drug Metabolism & Toxicology, 2012. 8(5): p. 623-633.
39. Mita, M.M., et al., Randomized Phase II Trial of the Cyclin-Dependent Kinase Inhibitor Dinaciclib (MK-7965) Versus Capecitabine in Patients With Advanced Breast Cancer. Clinical Breast Cancer, 2014. 14(3): p. 169-176.
40. Li, J., et al., Berberine represses DAXX gene transcription and induces cancer cell apoptosis. Laboratory investigation; a journal of technical methods and pathology, 2013. 93(3): p. 354-364.
41. Bento, C., M.K. Andersson, and P. Aman, DDIT3/CHOP and the sarcoma fusion oncoprotein FUS-DDIT3/TLS-CHOP bind cyclin-dependent kinase 2. Bmc Cell Biology, 2009. 10: p. 7.
42. Gordon, K.J. and G.C. Blobe, Role of transforming growth factor-beta superfamily signaling pathways in human disease. Biochimica Et Biophysica Acta-Molecular Basis of Disease, 2008. 1782(4): p. 197-228.
43. Hoesel, B. and J.A. Schmid, The complexity of NF-kappa B signaling in inflammation and cancer. Molecular Cancer, 2013. 12: p. 15.
44. Fernández-Medarde, A. and E. Santos, Ras in Cancer and Developmental Diseases. Genes & Cancer, 2011. 2(3): p. 344-358.
45. Zheng, R. and G.A. Blobel, GATA Transcription Factors and Cancer. Genes Cancer, 2010. 1(12): p. 1178-88.
46. Xia, J.L., et al., Role of NEK2A in Human Cancer and Its Therapeutic Potentials. Biomed Research International, 2015.
47. King, J.W. and S.M. Lee, Axitinib for the treatment of advanced non-small-cell lung cancer. Expert Opinion on Investigational Drugs, 2013. 22(6): p. 765-773.
48. Gigant, B., et al., Structural basis for the regulation of tubulin by vinblastine. Nature, 2005. 435(7041): p. 519-522.
49. Jendrossek, V., Targeting apoptosis pathways by Celecoxib in cancer. Cancer Letters, 2013. 332(2): p. 313-324.
50. Rawlings, J.S., K.M. Rosler, and D.A. Harrison, The JAK/STAT signaling pathway. Journal of Cell Science, 2004. 117(8): p. 1281-1283.
51. Heldin, C.H., Targeting the PDGF signaling pathway in tumor treatment. Cell Communication and Signaling, 2013. 11: p. 18.
52. Zarogoulidis, P., et al., Interleukin-7 and Interleukin-15 for Cancer. Journal of Cancer, 2014. 5(9): p. 765-773.
53. Yu, H., D. Pardoll, and R. Jove, STATs in cancer inflammation and immunity: a leading role for STAT3. Nature Reviews Cancer, 2009. 9(11): p. 798-809.
54. Rivadeneira, D.B., et al., Survivin promotes oxidative phosphorylation, subcellular mitochondrial repositioning, and tumor cell invasion. Science Signaling, 2015. 8(389): p. 12.
55. Ozanne, B.W., et al., Transcription factors control invasion: AP-1 the first among equals. Oncogene, 2007. 26(1): p. 1-10.
56. Ding, X.M., et al., Epigenetic Activation of AP1 Promotes Squamous Cell Carcinoma Metastasis. Science Signaling, 2013. 6(273): p. 13.
57. Amit-Vazina, M., et al., Atiprimod blocks STAT3 phosphorylation and induces apoptosis in multiple myeloma cells. British Journal of Cancer, 2005. 93(1): p. 70-80.
58. Roccaro, A.M., A. Vacca, and D. Ribatti, Bortezomib in the treatment of cancer. Recent Patents on Anti-Cancer Drug Discovery, 2006. 1(3): p. 397-403.
59. Lee, H.W., et al., Celastrol inhibits gastric cancer growth by induction of apoptosis and autophagy. Bmb Reports, 2014. 47(12): p. 697-702.