臺灣博碩士論文加值系統

在生物學中，了解身體功能在分子間的交互作用是極其重要的。近年來的研究顯示微型核糖核酸和目標信使核糖核酸之間的交互作用並非單方向及單調的上升或下降，而且這種調節機制存在於許多疾病。在微型核糖核酸的目標基因當中，一個微型核糖核酸可以調控多個靶基因並且相同靶基因可以被不同微型微型核酸調控，其中有些基因被命名為競爭性內源核糖核酸，在能被同一微型核糖核酸調控的情況下，這些基因彼此便構成了一種競爭關係，由於至今已被證實為競爭性內源核糖核酸的基因數量並不多，因此，探索這種複雜的調節機制成為近年來一大挑戰。至今已發展出許多辨識競爭性內源核糖核酸及其動態調節系統的演算法，絕大部份的演算法依據微型核糖核酸的表現量將其分組後進行後續分析，然而，微型核糖核酸的表現量為一連續變數而非離散，若視為離散變數進行分組可能將其調節作用阻斷於分析時被忽略。對此，我們基於隨機漫步的概念以及環狀二元分段法發展一個新的演算法。在隨機漫步的方法中，分數會隨著下一步移動而上下波動，最終的統計量為距離0最遠的分數接著做排列檢定(permutation test)，接著用環狀二元分段法得到兩基因發生最高度相關的微型核糖核酸表現量。在模擬研究中顯示我們所提出的演算法能正確的辨識競爭性內源核糖核酸與微型核糖核酸間的交互作用，此外，我們將其應用於TCGA的兩種癌症資料，發現有些共同的微型核糖核酸與競爭性內源核糖核酸於兩筆資料中被找到，且由文獻證實與非小細胞肺癌(non-small cell lung cancer)有關。基於模擬研究與實際資料應用的結果，我們的方法能有效辨識這種特殊的調節機制，特別的是，我們的方法能取得微型核糖核酸在特定區域的表現量值下才會發生此種交互作用。我們相信此演算法能提供競爭性內源與微型核糖核酸調節機制更進一步的了解。

Understanding physical and functional interactions between molecules in living systems is of vital importance in biology. Recent studies have shown that the interaction of microRNA (miRNA) and mRNA is not unidirectional and monotonic, which has been suggested as an important regulating mechanism in many diseases. Among the target genes of miRNAs, some of them are named as competing endogenous RNAs (ceRNAs), and their expression levels affected by the expression level of miRNAs can be regulated through competing for a pool of common binding miRNAs. Therefore, challenge arises when trying to systematically explore the association of miRNA and its target genes. Several algorithms have been developed to identify ceRNAs and their dynamic regulating systems. Most of the algorithms divide a miRNA into different groups based on its expression level and then perform the analysis accordingly. However, the expression level of a miRNA is actually a continuous variable instead of a discrete variable. To address this issue, we developed a new algorithm based on the random walk concept and circular binary algorithm. The score obtained from the random walk method was the maximum deviation from zero weighted by the correlation within each window. We then applied the circular binary algorithm to get the peaks from the miRNA expression levels across samples. Simulation studies demonstrate our proposed algorithm can accurately identify a ceRNA-miRNA triplet with high correlation. Also, we applied the algorithm to two TCGA cancers. Some common bridging miRNA and ceRNAs were found in two cancers and were verified by previous studies. Based on the results of simulation and application, our methods are effective and feasible to identify ceRNA-miRNA triplet. In particular, we can also capture the multiple peaks of correlation at specific miRNA expression levels. We believed that our algorithm is able to provide an insight in miRNA-modulated ceRNA regulatory mechanisms.

口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract v
CONTENTS vii
LIST OF FIGURES ix
LIST OF TABLES ix
Chapter 1 Introduction 1
1.1 Competing endogenous RNA 1
1.2 ceRNA prediction algorithm 2
1.3 Overview 4
Chapter 2 Materials and Methods 6
2.1 Algorithm flow 6
2.2 ceRNA pair filtering 7
2.2.1 Correlation coefficients calculated by sliding windows 7
2.2.2 Random walk 8
2.3 Segment clustering 9
2.3.1 Circular binary segmentation algorithm 9
2.4 Adjacent Peak merging 10
2.5 Simulation study 11
2.6 Application in real datasets 13
Chapter 3 Results 14
3.1 Simulation results 14
3.1.1 Performance 14
3.1.2 Computation 16
3.2 Application results 17
3.2.1 Application I: TCGA-LUAD 17
3.2.2 Application II: TCGA-LUSC 18
Chapter 4 Discussion 21
4.1 Results interpretation 21
4.1.1 Identified miRNAs 21
4.1.2 Candidate ceRNAs 21
4.2 Method comparison 22
4.3 Limitations 24
4.4 Challenges 24
4.5 Computation time 25
Chapter 5 Conclusion 27
References 43
Appendix 46

1.Ideker, T. and N.J. Krogan, Differential network biology. Molecular systems biology, 2012. 8(1): p. 565.
2.Sanchez-Mejias, A. and Y. Tay, Competing endogenous RNA networks: tying the essential knots for cancer biology and therapeutics. Journal of hematology & oncology, 2015. 8(1): p. 30.
3.Salmena, L., et al., A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell, 2011. 146(3): p. 353-358.
4.Le, T.D., et al., Computational methods for identifying miRNA sponge interactions. Briefings in bioinformatics, 2016: p. bbw042.
5.Wang, K., et al., Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nature biotechnology, 2009. 27(9): p. 829-837.
6.Zhou, X., J. Liu, and W. Wang, Construction and investigation of breast-cancer-specific ceRNA network based on the mRNA and miRNA expression data. IET systems biology, 2014. 8(3): p. 96-103.
7.Xu, J., et al., The mRNA related ceRNA–ceRNA landscape and significance across 20 major cancer types. Nucleic acids research, 2015. 43(17): p. 8169-8182.
8.Shao, T., et al., Identification of module biomarkers from the dysregulated ceRNA–ceRNA interaction network in lung adenocarcinoma. Molecular Biosystems, 2015. 11(11): p. 3048-3058.
9.Chiu, Y.-C., et al., Analyzing differential regulatory networks modulated by continuous-state genomic features in glioblastoma multiforme. IEEE/ACM transactions on computational biology and bioinformatics, 2016.
10.Paci, P., T. Colombo, and L. Farina, Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC systems biology, 2014. 8(1): p. 83.
11.Sumazin, P., et al., An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell, 2011. 147(2): p. 370-381.
12.Bosia, C., A. Pagnani, and R. Zecchina, Modelling competing endogenous RNA networks. PLoS One, 2013. 8(6): p. e66609.
13.Ala, U., et al., Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments. Proceedings of the National Academy of Sciences, 2013. 110(18): p. 7154-7159.
14.Figliuzzi, M., E. Marinari, and A. De Martino, MicroRNAs as a selective channel of communication between competing RNAs: a steady-state theory. Biophysical journal, 2013. 104(5): p. 1203-1213.
15.Olshen, A.B., et al., Circular binary segmentation for the analysis of array‐based DNA copy number data. Biostatistics, 2004. 5(4): p. 557-572.
16.Chou, C.-H., et al., miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic acids research, 2015. 44(D1): p. D239-D247.
17.Xiao, F., et al., miRecords: an integrated resource for microRNA–target interactions. Nucleic acids research, 2008. 37(suppl_1): p. D105-D110.
18.Paraskevopoulou, M.D., et al., DIANA-microT web server v5. 0: service integration into miRNA functional analysis workflows. Nucleic acids research, 2013. 41(W1): p. W169-W173.
19.Gaidatzis, D., et al., Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC bioinformatics, 2007. 8(1): p. 69.
20.Wang, X., miRDB: a microRNA target prediction and functional annotation database with a wiki interface. Rna, 2008. 14(6): p. 1012-1017.
21.John, B., et al., Human microRNA targets. PLoS biology, 2004. 2(11): p. e363.
22.Kertesz, M., et al., The role of site accessibility in microRNA target recognition. Nature genetics, 2007. 39(10): p. 1278-1284.
23.Miranda, K.C., et al., A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell, 2006. 126(6): p. 1203-1217.
24.Lewis, B.P., C.B. Burge, and D.P. Bartel, Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. cell, 2005. 120(1): p. 15-20.
25.Sen, A. and M.S. Srivastava, On tests for detecting change in mean. The Annals of statistics, 1975: p. 98-108.
26.Linn, S.C., et al., Gene expression patterns and gene copy number changes in dermatofibrosarcoma protuberans. The American journal of pathology, 2003. 163(6): p. 2383-2395.
27.Zhu, C., et al., MicroRNA-183 promotes migration and invasion of CD133+/CD326+ lung adenocarcinoma initiating cells via PTPN4 inhibition. Tumor Biology, 2016. 37(8): p. 11289-11297.
28.Chen, Y., et al., MiR-142-3p Overexpression Increases Chemo-Sensitivity of NSCLC by Inhibiting HMGB1-Mediated Autophagy. Cellular Physiology and Biochemistry, 2017. 41(4): p. 1370-1382.
29.Chuang, J.C., et al., ERBB2-Mutated Metastatic Non–Small Cell Lung Cancer: Response and Resistance to Targeted Therapies. Journal of Thoracic Oncology, 2017. 12(5): p. 833-842.
30.Arcila, M.E., et al., Prevalence, clinicopathologic associations, and molecular spectrum of ERBB2 (HER2) tyrosine kinase mutations in lung adenocarcinomas. Clinical Cancer Research, 2012.
31.Ferone, G., et al., SOX2 is the determining oncogenic switch in promoting lung squamous cell carcinoma from different cells of origin. Cancer Cell, 2016. 30(4): p. 519-532.
32.Devarakonda, S., D. Morgensztern, and R. Govindan, Clinical applications of The Cancer Genome Atlas project (TCGA) for squamous cell lung carcinoma. Oncology, 2013. 27(9): p. 899-899.
33.Zhao, B., et al., MicroRNA let-7c inhibits migration and invasion of human non-small cell lung cancer by targeting ITGB3 and MAP4K3. Cancer letters, 2014. 342(1): p. 43-51.
34.Kawano, O., et al., PIK3CA mutation status in Japanese lung cancer patients. Lung cancer, 2006. 54(2): p. 209-215.
35.Samuels, Y., et al., High frequency of mutations of the PIK3CA gene in human cancers. Science, 2004. 304(5670): p. 554-554.
36.Wang, L., et al., PIK3CA mutations frequently coexist with EGFR/KRAS mutations in non-small cell lung cancer and suggest poor prognosis in EGFR/KRAS wildtype subgroup. PloS one, 2014. 9(2): p. e88291.