跳到主要內容

臺灣博碩士論文加值系統

(3.229.137.68) 您好!臺灣時間:2021/07/25 16:55
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:李孟杰
研究生(外文):Meng-Chieh Lee
論文名稱:探討阿茲海默症的基因生物標誌及微型核糖核酸生物標誌
論文名稱(外文):The exploration of gene biomarkers and microRNA biomarkers on Alzheimer’s disease
指導教授:陳信志陳信志引用關係
指導教授(外文):Austin H Chen
學位類別:碩士
校院名稱:慈濟大學
系所名稱:醫學資訊學系碩士班
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2012
畢業學年度:101
語文別:中文
論文頁數:63
中文關鍵詞:微型核醣核酸基因微陣列生物標誌隨機森林
外文關鍵詞:microRNAmicroarraybiomarkerrandom forestCART
相關次數:
  • 被引用被引用:3
  • 點閱點閱:347
  • 評分評分:
  • 下載下載:45
  • 收藏至我的研究室書目清單書目收藏:0
依據世界衛生組織WHO統計腦神經系統疾病在2005、2015、2030 的Disability Adjusted Life Years(DALY)成長率平均高於其他疾病,其中阿茲海默症成長率為1.5倍遠高於其他腦神經系統疾病,顯示出阿茲海默症在未來社會負擔的重要性,因此本研究以阿茲海默症為主要的研究對象。
微型核醣核酸(microRNA) 是一種長度約22個核甘酸的非密碼核醣核酸(non-coding RNA)能抑制訊息核糖核酸(messageRNA)轉譯成蛋白質。在大腦神經發展上microRNA已經被證明有相當重要的地位,它的表現量比起其它身體組織更加活躍,但是在神經退化性疾病的microRNA研究卻相對於癌症來的少,大多數在神經退化性疾病上的研究都是單方面針對microRNA資料作分析,鮮少同時進行microRNA與基因網路並解釋其中關係的研究,發展一種尋找具有生物意義的microRNA生物標誌的方法更是目前研究上的挑戰。
此研究計畫發展四種尋找阿茲海默症生物標誌的新方法:一種探討基因生物標誌以及三種探討microRNA生物標誌的方法。用分類迴歸樹基因網路探討基因生物標誌,此預測法是以分類迴歸樹為主要核心結合基因選取,找出兩兩相關基因進行網路拓樸分析。針對mircoRNA生物標誌所使用的方法有三種:第一,基因目標預測法,基因資料經過基因選取處理,得到的基因透過microRNA目標預測資料庫比對找尋microRNA生物標誌。第二,mircoRNA網路法,利用microRNA目標預測資料庫存在的microRNA配合已處理後的基因資料,依照每個microRNA之間所對應到的基因,來決定各個microRNA是否有關係來建立拓樸網路。第三,隨機森林microRNA選取法,將microRNA表現資料使用隨機森林演算法將最多為根節點的microRNA作為生物標誌。
結果顯示分類迴歸樹基因網路尋找基因生物標誌,有3個基因具有成為生物標誌的潛力分別為ZDHHC23,ZNF264和ZNF614,8個有醫學文獻證實或已應用的生物標誌則是ADARB2, KCNN3, SCG3, PLCB1, PPP3R1, BNDF, CDK5和TPM3。基因目標預測法、mircoRNA網路法和隨機森林microRNA選取法找出的生物標誌有4個可能為生物標誌分別是miR-3163, miR-4282, miR-128a, miR-34c,4個由醫學文獻證實的生物標誌為miR-16, miR-590-3p, hsa-miR-29a和miR-106b。
Introduction: According to the statistics of World Health Organization from 2005 to 2030, Disability Adjusted Life Years (DALYS) in Alzheimer’s disease (AD) increase 1.5 times, having far more increasing rate than any other neurological disorder. MicroRNAs (miRNAs) are a class of small noncoding (19-24 nucleotide) RNAs that regulate the expression of target mRNAs at the post-transcriptional level. In vertebrates, more distinct miRNAs are expressed in the brain than in any other tissue, where we are hypothesized to function in AD development. Recent studies reveal that genes and microRNAs play as regulators of development in AD. Discovering gene and microRNA biomarkers is an increasingly aware issue.
Methods: In this study, we propose four new methods to discover the potential gene and microRNA biomarkers of AD. Those four methods are: classification and regression trees gene network (CART-GN), gene target predictions, microRNA network and random forest microRNA selection. Those methods include three kinds techniques, statistic methods, microRNA targets predictions and machine learning.
Results: In this study, we use two microarray set one of gene and other is miNRA. As result, we find 11 AD gene biomarkers. Include 8 genes (ADARB2, KCNN3, SCG3, PLCB1, PPP3R1, BNDF, CDK5 and TPM3) true biomarkers that have been evidenced by medical literature, 3 potential biomarkers (ZDHHC23, ZNF264 and ZNF614) with function Zinc finger DHHC domain-containing protein. Another important result comes from microRNA microarray analysis. We find 8 AD microRNA biomarkers, include 4 biomarkers (miR-16,miR-590-3p,hsa-miR-29a and miR-106b) have been evidenced by medical literature and 4 potential biomarkers (miR-3163, miR-4282, miR-128a and miR-34c).
致謝詞 i
摘要 ii
Abstract iv
目錄 v
圖目錄 vii
表目錄 viii
第一章緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究流程 3
第二章文獻探討 4
2.1 阿茲海默症 4
2.2 微型核糖核酸 7
2.3 microRNA目標預測資料庫 9
2.4 False discovery rate 10
2.5 基因網路 11
第三章研究方法 14
3.1 分類迴歸樹基因網路 15
3.2 基因目標預測法 19
3.3 microRNA網路法 21
3.4 隨機森林(RandomForests)microRNA選取法 24
第四章結果與討論 27
4.1 阿茲海默症的基因生物標誌 27
4.2 阿茲海默症的microRNA生物標誌 38
第五章結論與未來展望 46
5.1 結論 46
5.2 未來展望 48
參考文獻 49
1. Organization, W. H. (2007). Global burden of neurological disorders stimates and projections.Neurological disorders: Public health challenges,Geneva: WHO.
2. Fineberg, S. K., Kosik, K. S., & Davidson, B. L. (2009). MicroRNAs Potentiate Neural Development. Neuron, 64(3), 303-309.
3. Nunez-Iglesias, J., Liu, C.-C., Morgan, T. E., Finch, C. E., & Zhou, X. J. (2010). Joint Genome-Wide Profiling of microRNA and mRNA Expression in Alzheimer's Disease Cortex Reveals Altered microRNA Regulation. PLoS ONE, 5(2), e8898. doi: 10.1371/journal.pone.0008898
4. Jiang, W., Li, X., Rao, S., Wang, L., Du, L., Li, C., . . . Yang, B. (2008). Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements. BMC Systems Biology, 2(1), 72.
5. Liu, C.-C., Chen, W.-S. E., Lin, C.-C., Liu, H.-C., Chen, H.-Y., Yang, P.-C., . . . Chen, J. J. W. (2006). Topology-based cancer classification and related pathway mining using microarray data. Nucleic Acids Research, 34(14), 4069-4080. doi: 10.1093/nar/gkl583
6. Berchtold, N. C., &Cotman, C. W. (1998). Evolution in the Conceptualization of Dementia and Alzheimer’s Disease: Greco-Roman Period to the 1960s. Neurobiology of aging, 19(3), 173-189.
7. Yankner, B. A., Lu, T., & Loerch, P. (2008). The aging brain.Annu. Rev. pathmechdis. Mech. Dis., 3, 41-66.
8. Bertram, L., McQueen, M. B., Mullin, K., Blacker, D., & Tanzi, R. E. (2007). Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nature genetics, 39(1), 17-23.
9. O'Barr, S., Schultz, J., & Rogers, J. (1996).Expression of the protooncogene bcl-2 in Alzheimer's disease brain.Neurobiology of aging, 17(1), 131-136.
10. Rocchi, A., Pellegrini, S., Siciliano, G., &Murri, L. (2003). Causative and susceptibility genes for Alzheimer’s disease: a review. Brain Research Bulletin, 61(1), 1-24. doi:10.1016/s0361-9230(03)00067-4
11. Lee, R. C., Feinbaum, R. L., &Ambros, V. (1993). The C. elegansheterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75(5), 843-854.
12. Wang, X., & Wang, X. (2006). Systematic identification of microRNA functions by combining target prediction and expression profiling. Nucleic Acids Research, 34(5), 1646-1652.
13. Arora, A., & Simpson, D. A. (2008). Individual mRNA expression profiles reveal the effects of specific microRNAs. Genome Biology, 9(5), R82-R82.
14. Cheng, C., & Li, L. M. (2008).Inferring microRNA activities by combining gene expression with microRNA target prediction.Plos One, 3(4), e1989-e1989.
15. Yu, Z., Jian, Z., Shen, S.-H., Purisima, E., & Wang, E. (2007). Global analysis of microRNA target gene expression reveals that miRNA targets are lower expressed in mature mouse and Drosophila tissues than in the embryos. Nucleic Acids Research, 35(1), 152-164.
16. Fuxiao, X., Meng, L., Curt, B., Michael, T., Meiyun, F., Yunlong, L., . . . Kenneth, P. N. (2009). Computational analysis of microRNA profiles and their target genes suggests significant involvement in breast cancer antiestrogen resistance. Bioinformatics, 25(4), 430-430.
17. Hebert, S. S., Horre, K., Nicolai, L., Bergmans, B., Papadopoulou, A. S., Delacourte, A., & De Strooper, B. (2009). MicroRNA regulation of Alzheimer's Amyloid precursor protein expression.Neurobiology of Disease, 33(3), 422-428.doi: 10.1016/j.nbd.2008.11.009
18. Betel, D., Koppal, A., Agius, P., Sander, C., Leslie, C. (2010).Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biology, 11:R90.doi: 10.1186/gb-2010-11-8-r90.
19. Wang, X., & El Naqa, I. M. (2008). Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics, 24(3), 325-332.
20. Xiaowei Wang (2008) miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 14(6):1012-1017
21. Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.Journal of the Royal Statistical Society.Series B (Methodological), 57(1), 289-300.
22. Butte, A. J., Tamayo, P., Slonim, D., Golub, T. R., &Kohane, I. S. (2000).Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proceedings Of The National Academy Of Sciences Of The United States Of America, 97(22), 12182-12186.
23. Basso, K., Margolin, A. A., Stolovitzky, G., Klein, U., Dalla-Favera, R., & Califano, A. (2005).Reverse engineering of regulatory networks in human B cells.Nature Genetics, 37(4), 382-390. doi: 10.1038/ng1532
24. Ergun, A., Lawrence, C. A., Kohanski, M. A., Brennan, T. A., & Collins, J. J. (2007).A network biology approach to prostate cancer.Molecular Systems Biology, 3, 82-82.
25. Webster, J. A., Gibbs, J. R., Clarke, J., Ray, M., Zhang, W., Holmans, P.,Kaleem, M. (2009). Genetic control of human brain transcript expression in Alzheimer disease.The American Journal of Human Genetics, 84(4), 445-458.
26. Ron Kohavi.(1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.International Joint Conference on Artificial Intelligence(IJCAI), 14, 1137-1145.
27. Olshen, L. B. J. H. F. R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth International Group.
28. Scott, J. (2000). Social network analysis: A handbook: Sage Publications Limited..
29. Breiman, L. (2001). Random Forests.Machine Learning, 45(1), 5-32. doi: 10.1023/a:1010933404324
30. Huang, X., Cuajungco, M. P., Atwood, C. S., Moir, R. D., Tanzi, R. E., & Bush, A. I. (2000). Alzheimer’s disease, β-amyloid protein and zinc.The Journal of nutrition, 130(5), 1488S-1492S.
31. Owen, J. B., Di Domenico, F., Sultana, R., Perluigi, M., Cini, C., Pierce, W. M., & Butterfield, D. A. (2008). Proteomics-determined differences in the concanavalin-A-fractionated proteome of hippocampus and inferior parietal lobule in subjects with Alzheimer’s disease and mild cognitive impairment: implications for progression of AD. Journal of proteome research, 8(2), 471-482.
32. Cruz, J. C., Tseng, H. C., Goldman, J. A., Shih, H., & Tsai, L. H. (2003). Aberrant Cdk5 activation by p25 triggers pathological events leading to neurodegeneration and neurofibrillary tangles. Neuron, 40(3), 471-483.
33. Jose, V. H., Ignacio, M., Pascual, S. J., Eloy, R. R., Jon, I., Jose, B., & Onofre, C. (2009). No association of CDK5 genetic variants with Alzheimer's disease risk.
34. Lee, G., Newman, S. T., Gard, D. L., Band, H., &Panchamoorthy, G. (1998). Tau interacts with src-family non-receptor tyrosine kinases. Journal of Cell Science, 111(21), 3167-3177.
35. Zoladz, J., Pilc, A., Majerczak, J., Grandys, M., Zapart-Bukowska, J., & Duda, K. (2008). Endurance training increases plasma brain-derived neurotrophic factor concentration in young healthy men. J Physiol Pharmacol, 59(Suppl 7), 119-132.
36. Phillips, H. S., Hains, J. M., Armanini, M., Laramee, G. R., Johnson, S. A., & Winslow, J. W. (1991). BDNF mRNA is decreased in the hippocampus of individuals with Alzheimer's disease. Neuron, 7(5), 695-702.
37. Ridolfi, E., Villa, C., Fenoglio, C., Cortini, F., Serpente, M., Cantoni, C., . . . Galimberti, D. (2011). Role of hnRNP-A1 and miR-590-3p in neuronal death: genetics and expression analysis in patients with Alzheimer’s disease and Frontotemporal Lobar Degeneration.
38. Bettens, K., Brouwers, N., Engelborghs, S., De Pooter, T., De Deyn, P. P., Sleegers, K., & Van Broeckhoven, C. (2009). DNMBP is genetically associated with Alzheimer dementia in the Belgian population. Neurobiology of aging, 30(12), 2000-2009.
39. Suzuki, T., Nishiyama, K., Yamamoto, A., Inazawa, J., Iwaki, T., Yamada, T., . . . Sakaki, Y. (2000). Molecular Cloning of a Novel Apoptosis-Related Gene, Human Nap1( NCKAP1), and Its Possible Relation to Alzheimer Disease. Genomics, 63(2), 246-254.
40. Lukiw, W. J. (2007). Micro-RNA speciation in fetal, adult and Alzheimer's disease hippocampus.Neuroreport, 18(3), 297-300..
41. Wang, X., Liu, P., Zhu, H., Xu, Y., Ma, C., Dai, X., . . . Qin, C. (2009). miR-34a, a microRNA up-regulated in a double transgenic mouse model of Alzheimer's disease, inhibits bcl2 translation.Brain research bulletin, 80(4), 268-273.
42. Liu, W., Liu, C., Zhu, J., Shu, P., Yin, B., Gong, Y., . . . Peng, X. (2012). MicroRNA-16 targets amyloid precursor protein to potentially modulate Alzheimer's-associated pathogenesis in SAMP8 mice. Neurobiology of aging, 33(3), 522-534.
43. Hebert, S. S., Horre, K., Nicolai, L., Papadopoulou, A. S., Mandemakers, W., Silahtaroglu, A. N., . . . De Strooper, B. (2008). Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/β-secretase expression. Proceedings of the National Academy of Sciences, 105(17), 6415-6420.
44. Hebert, S. S., Horre, K., Nicolai, L., Bergmans, B., Papadopoulou, A. S., Delacourte, A., & De Strooper, B. (2009).MicroRNA regulation of Alzheimer's Amyloid precursor protein expression.Neurobiology of disease, 33(3), 422-428.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊