研究生: |
王國安 Wang, Kuo-An |
---|---|
論文名稱: |
Rac 蛋白與多形性神經膠質母細胞瘤之臨床關聯性分析 The Clinical Correlation Between Rac GTPase and Glioblastoma Multiforme |
指導教授: |
沈林琥
Sher, Singh |
學位類別: |
碩士 Master |
系所名稱: |
生命科學系 Department of Life Science |
論文出版年: | 2018 |
畢業學年度: | 106 |
語文別: | 中文 |
論文頁數: | 65 |
中文關鍵詞: | 多形性膠質母細胞瘤 、Rac 、微型核醣核酸 、存活分析 、表觀遺傳學 |
英文關鍵詞: | overall survival |
DOI URL: | http://doi.org/10.6345/THE.NTNU.SLS.023.2018.D01 |
論文種類: | 學術論文 |
相關次數: | 點閱:137 下載:0 |
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多型性神經膠母細胞瘤(Glioblastoma multiform,GBM) 為最常見且高侵襲性的原發性腦瘤, 並具高復發率,目前的治療方式為手術切除搭配放射線治療及化學藥物治療。然而預後狀況仍然不理想。
Rac 蛋白質屬於 Rho GTP酶的亞家族成員,其功能為細胞遷移、侵襲和存活,Rac 所調控的訊息傳遞路徑可能導致腫瘤生成。研究發現 Rac 蛋白質在癌幹細胞中扮演維持幹性(stemness)和增生(proliferation)的角色。
本研究利用基因圖譜計畫資料庫(TCGA)中的RNA-Seq、全基因組甲基化圖譜、微型核醣核酸晶片及臨床病患資料等數據探討Rac family表達模式與膠質母細胞瘤的臨床預後關係。
我們使用RNA-seq 來測定Rac family 在GBM的mRNA level,發現GBM病患的Rac1和Rac2表現量跟正常組織相比有顯著的上升,其表現量分別在Proneural、Classical 亞型的存活時間有顯著影響。
在本次實驗中我們探討可能調控Rac 蛋白的機制,我們發現Rac2有5個低甲基化的位點可能與 Rac2 在腦癌中的高表現量有關,而在Rac1及Rac3,則沒有明顯甲基化的差異。
此外在微型核糖核酸分析中,miR-148a、miR-155、miR-34a與Rac家族表現量呈正相關,其表現量對存活時間有顯著影響,因此Rac family 及 miR148a、miR-155、miR-34a有潛力成為多形性神經膠質母細胞瘤的治療策略。
Glioblastoma is the most common and aggressive primary brain tumor. One of its malignant characters is high recurrence. The standard treatment is surgical removal combined with radio-chemotherapy. However, the prognosis remains poor. Rac proteins belong to the Rho small GTPase which regulate cell migration, invasion, and survival. All the signaling pathways regulated by Rac may contribute to tumorigenesis. Our previous study had shown that Rac family proteins play an important role in maintaining the stemness and proliferation of glioblastoma stem cells.
In this study, we used RNA-seq data, methylation data, microRNA array data , and clinical data obtained from TCGA database to explore the relationship between the expression patterns of Rac (family and the clinical outcomes of glioblastoma. We observed that Rac1 and Rac2 mRNA expression levels were significantly increased in glioblasoma patients compared with solid normal tissue. Glioblastoma patients with high expression levels of Rac1 and Rac2 had significantly shorter overall survival in Proneural and Classical subtype, respectively.
Moreover, we observed that several hypomethylated CpG sites in Rac2 of glioblastoma pateints which may account for Rac2 overexpression in these patients.but there are not significant difference in Rac1 and Rac3.
In addition, in microRNA analysis, the expression of miR-148a, miR-155, miR-34a was positively correlated with the expression of Rac family. Moreover, these expression patterns had a significant impact on overall survival time. Therefore, Rac family and microRNA miR148a, miR-155, and miR-34a may serve as potential therapeutical targets for glioblastoma multiforme.
1. Agnihotri, S., et al., Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies. Arch Immunol Ther Exp (Warsz), 2013. 61(1): p. 25-41.
2. Doerks, T., et al., Systematic identification of novel protein domain families associated with nuclear functions. Genome Res, 2002. 12(1): p. 47-56.
3. Louis, D.N., et al., The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol, 2016. 131(6): p. 803-20.
4. Jovcevska, I., N. Kocevar, and R. Komel, Glioma and glioblastoma - how much do we (not) know? Mol Clin Oncol, 2013. 1(6): p. 935-941.
5. Ostrom, Q.T., et al., CBTRUS statistical report: Primary brain and central nervous system tumors diagnosed in the United States in 2006-2010. Neuro Oncol, 2013. 15 Suppl 2: p. ii1-56.
6. Krex, D., et al., Long-term survival with glioblastoma multiforme. Brain, 2007. 130(Pt 10): p. 2596-606.
7. Adams, H., et al., Adult cerebellar glioblastoma: understanding survival and prognostic factors using a population-based database from 1973 to 2009. World Neurosurg, 2013. 80(6): p. e237-43.
8. Wilson, T.A., M.A. Karajannis, and D.H. Harter, Glioblastoma multiforme: State of the art and future therapeutics. Surg Neurol Int, 2014. 5: p. 64.
9. Chakrabarti, I., et al., A population-based description of glioblastoma multiforme in Los Angeles County, 1974-1999. Cancer, 2005. 104(12): p. 2798-806.
10. Konar, S.K., et al., Predictive Factors Determining the Overall Outcome of Primary Spinal Glioblastoma Multiforme: An Integrative Survival Analysis. World Neurosurg, 2016. 86: p. 341-8 e1-3.
11. Nicholas, M.K., et al., Epidermal growth factor receptor - mediated signal transduction in the development and therapy of gliomas. Clin Cancer Res, 2006. 12(24): p. 7261-70.
12. Zheng, Y., et al., Targeting protein kinase CK2 suppresses prosurvival signaling pathways and growth of glioblastoma. Clin Cancer Res, 2013. 19(23): p. 6484-94.
13. Hatanpaa, K.J., et al., Epidermal Growth Factor Receptor in Glioma: Signal Transduction, Neuropathology, Imaging, and Radioresistance. Neoplasia, 2010. 12(9): p. 675-684.
14. Anaya, J., OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. PeerJ Computer Science, 2016. 2: p. e67.
15. Furnari, F.B., et al., Malignant astrocytic glioma: genetics, biology, and paths to treatment. Genes Dev, 2007. 21(21): p. 2683-710.
16. Knudsen, E.S. and J.Y. Wang, Targeting the RB-pathway in cancer therapy. Clin Cancer Res, 2010. 16(4): p. 1094-9.
17. Grzmil, M. and B.A. Hemmings, Deregulated signalling networks in human brain tumours. Biochim Biophys Acta, 2010. 1804(3): p. 476-83.
18. Verhaak, R.G., et al., Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 2010. 17(1): p. 98-110.
19. Van Meir, E.G., et al., Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J Clin, 2010. 60(3): p. 166-93.
20. Meletis, K., et al., p53 suppresses the self-renewal of adult neural stem cells. Development, 2006. 133(2): p. 363-9.
21. Shangary, S. and S. Wang, Small-molecule inhibitors of the MDM2-p53 protein-protein interaction to reactivate p53 function: a novel approach for cancer therapy. Annu Rev Pharmacol Toxicol, 2009. 49: p. 223-41.
22. Zawlik, I., et al., Common polymorphisms in the MDM2 and TP53 genes and the relationship between TP53 mutations and patient outcomes in glioblastomas. Brain Pathol, 2009. 19(2): p. 188-94.
23. Crespo, I., et al., Molecular and Genomic Alterations in Glioblastoma Multiforme. Am J Pathol, 2015. 185(7): p. 1820-33.
24. Ohgaki, H. and P. Kleihues, The definition of primary and secondary glioblastoma. Clin Cancer Res, 2013. 19(4): p. 764-72.
25. Sturm, D., et al., Paediatric and adult glioblastoma: multiform (epi)genomic culprits emerge. Nat Rev Cancer, 2014. 14(2): p. 92-107.
26. Brennan, C.W., et al., The somatic genomic landscape of glioblastoma. Cell, 2013. 155(2): p. 462-77.
27. Crespo, I., et al., Detailed characterization of alterations of chromosomes 7, 9, and 10 in glioblastomas as assessed by single-nucleotide polymorphism arrays. J Mol Diagn, 2011. 13(6): p. 634-47.
28. Watanabe, T., et al., IDH1 mutations are early events in the development of astrocytomas and oligodendrogliomas. Am J Pathol, 2009. 174(4): p. 1149-53.
29. Phillips, H.S., et al., Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell, 2006. 9(3): p. 157-73.
30. Cancer Genome Atlas Research, N., Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 2008. 455(7216): p. 1061-8.
31. Deaton, A.M. and A. Bird, CpG islands and the regulation of transcription. Genes Dev, 2011. 25(10): p. 1010-22.
32. Mansoori, B., S. Sandoghchian Shotorbani, and B. Baradaran, RNA interference and its role in cancer therapy. Adv Pharm Bull, 2014. 4(4): p. 313-21.
33. Gunesdogan, U., H. Jackle, and A. Herzig, A genetic system to assess in vivo the functions of histones and histone modifications in higher eukaryotes. EMBO Rep, 2010. 11(10): p. 772-6.
34. Hadnagy, A., R. Beaulieu, and D. Balicki, Histone tail modifications and noncanonical functions of histones: perspectives in cancer epigenetics. Mol Cancer Ther, 2008. 7(4): p. 740-8.
35. Peterson, C.L. and M.A. Laniel, Histones and histone modifications. Curr Biol, 2004. 14(14): p. R546-51.
36. Berdasco, M. and M. Esteller, Aberrant epigenetic landscape in cancer: how cellular identity goes awry. Dev Cell, 2010. 19(5): p. 698-711.
37. Fullgrabe, J., E. Kavanagh, and B. Joseph, Histone onco-modifications. Oncogene, 2011. 30(31): p. 3391-403.
38. Seligson, D.B., et al., Global levels of histone modifications predict prognosis in different cancers. Am J Pathol, 2009. 174(5): p. 1619-28.
39. Yu, J., et al., A polycomb repression signature in metastatic prostate cancer predicts cancer outcome. Cancer Res, 2007. 67(22): p. 10657-63.
40. Mack, N.A., et al., The diverse roles of Rac signaling in tumorigenesis. Cell Cycle, 2011. 10(10): p. 1571-81.
41. Vega, F.M. and A.J. Ridley, Rho GTPases in cancer cell biology. FEBS Lett, 2008. 582(14): p. 2093-101.
42. Cancelas, J.A., On how Rac controls hematopoietic stem cell activity. Transfusion, 2011. 51 Suppl 4: p. 153S-159S.
43. Hanna, S. and M. El-Sibai, Signaling networks of Rho GTPases in cell motility. Cell Signal, 2013. 25(10): p. 1955-61.
44. Cardama, G.A., et al., Proapoptotic and antiinvasive activity of Rac1 small molecule inhibitors on malignant glioma cells. Onco Targets Ther, 2014. 7: p. 2021-33.
45. Wheeler, A.P., et al., Rac1 and Rac2 regulate macrophage morphology but are not essential for migration. J Cell Sci, 2006. 119(Pt 13): p. 2749-57.
46. Azim, A.C., et al., Regulation of cyclooxygenase-2 expression by small GTPase Rac2 in bone marrow macrophages. Am J Physiol Lung Cell Mol Physiol, 2007. 293(3): p. L668-73.
47. Hwang, S.L., et al., Expression of Rac3 in human brain tumors. J Clin Neurosci, 2005. 12(5): p. 571-4.
48. Jung, I.H., et al., Glioma is formed by active Akt1 alone and promoted by active Rac1 in transgenic zebrafish. Neuro Oncol, 2013. 15(3): p. 290-304.
49. Chan, A.Y., et al., Roles of the Rac1 and Rac3 GTPases in human tumor cell invasion. Oncogene, 2005. 24(53): p. 7821-9.
50. Tomczak, K., P. Czerwinska, and M. Wiznerowicz, The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn), 2015. 19(1A): p. A68-77.
51. Su, E.C., et al., ChemiRs: a web application for microRNAs and chemicals. BMC Bioinformatics, 2016. 17: p. 167.
52. Chou, C.H., et al., miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res, 2018. 46(D1): p. D296-D302.
53. Anders, S., P.T. Pyl, and W. Huber, HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics, 2015. 31(2): p. 166-9.
54. Love, M.I., W. Huber, and S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014. 15(12): p. 550.
55. Colaprico, A., et al., TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res, 2016. 44(8): p. e71.
56. Lopez-Romero, P., Pre-processing and differential expression analysis of Agilent microRNA arrays using the AgiMicroRna Bioconductor library. BMC Genomics, 2011. 12: p. 64.
57. John, B., et al., Human MicroRNA targets. PLoS Biol, 2004. 2(11): p. e363.
58. Rehmsmeier, M., et al., Fast and effective prediction of microRNA/target duplexes. RNA, 2004. 10(10): p. 1507-17.
59. Kim, S.K., et al., miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics, 2006. 7: p. 411.
60. Zheng, Z., et al., MiR-142 acts as a tumor suppressor in osteosarcoma cell lines by targeting Rac1. Oncol Rep, 2015. 33(3): p. 1291-9.
61. Yu, F., et al., MALAT1 functions as a competing endogenous RNA to mediate Rac1 expression by sequestering miR-101b in liver fibrosis. Cell Cycle, 2015. 14(24): p. 3885-96.
62. Meng, Z., et al., miR-194 is a marker of hepatic epithelial cells and suppresses metastasis of liver cancer cells in mice. Hepatology, 2010. 52(6): p. 2148-57.
63. Chiyomaru, T., et al., Genistein up-regulates tumor suppressor microRNA-574-3p in prostate cancer. PLoS One, 2013. 8(3): p. e58929.
64. Jiang, S., et al., A novel miR-155/miR-143 cascade controls glycolysis by regulating hexokinase 2 in breast cancer cells. EMBO J, 2012. 31(8): p. 1985-98.
65. Bosco, E.E., J.C. Mulloy, and Y. Zheng, Rac1 GTPase: a "Rac" of all trades. Cell Mol Life Sci, 2009. 66(3): p. 370-4.
66. Bid, H.K., et al., RAC1: an emerging therapeutic option for targeting cancer angiogenesis and metastasis. Mol Cancer Ther, 2013. 12(10): p. 1925-34.
67. Parri, M. and P. Chiarugi, Rac and Rho GTPases in cancer cell motility control. Cell Commun Signal, 2010. 8: p. 23.
68. Croce, C.M., Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet, 2009. 10(10): p. 704-14.
69. Gabriely, G., et al., MicroRNA 21 promotes glioma invasion by targeting matrix metalloproteinase regulators. Mol Cell Biol, 2008. 28(17): p. 5369-80.
70. Kwak, H.J., et al., Downregulation of Spry2 by miR-21 triggers malignancy in human gliomas. Oncogene, 2011. 30(21): p. 2433-42.
71. Schramedei, K., et al., MicroRNA-21 targets tumor suppressor genes ANP32A and SMARCA4. Oncogene, 2011. 30(26): p. 2975-85.
72. Kefas, B., et al., microRNA-7 inhibits the epidermal growth factor receptor and the Akt pathway and is down-regulated in glioblastoma. Cancer Res, 2008. 68(10): p. 3566-72.
73. Dugas, D.V. and B. Bartel, MicroRNA regulation of gene expression in plants. Curr Opin Plant Biol, 2004. 7(5): p. 512-20.
74. Jacobsen, A., et al., Analysis of microRNA-target interactions across diverse cancer types. Nat Struct Mol Biol, 2013. 20(11): p. 1325-32.
75. Cheng, C.J., et al., MicroRNA silencing for cancer therapy targeted to the tumour microenvironment. Nature, 2015. 518(7537): p. 107-10.
76. Kong, W., et al., Upregulation of miRNA-155 promotes tumour angiogenesis by targeting VHL and is associated with poor prognosis and triple-negative breast cancer. Oncogene, 2014. 33(6): p. 679-89.
77. D'Urso, P.I., et al., miR-155 is up-regulated in primary and secondary glioblastoma and promotes tumour growth by inhibiting GABA receptors. Int J Oncol, 2012. 41(1): p. 228-34.
78. Kim, J., et al., microRNA-148a is a prognostic oncomiR that targets MIG6 and BIM to regulate EGFR and apoptosis in glioblastoma. Cancer Res, 2014. 74(5): p. 1541-53.
79. Liu, C., et al., The microRNA miR-34a inhibits prostate cancer stem cells and metastasis by directly repressing CD44. Nat Med, 2011. 17(2): p. 211-5.
80. Li, Y., et al., MicroRNA-34a inhibits glioblastoma growth by targeting multiple oncogenes. Cancer Res, 2009. 69(19): p. 7569-76.
81. Chandrashekar, D.S., et al., UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia, 2017. 19(8): p. 649-658.
82. Wong, N.W., et al., OncomiR: an online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics, 2018. 34(4): p. 713-715.
83. Durinck, S., et al., BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics, 2005. 21(16): p. 3439-40.