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研究生:許嫚茹
研究生(外文):Man-Ju Hsu
論文名稱:長鏈非編碼核糖核酸調控之網路特性分析
論文名稱(外文):Characterization and Stratification of LncRNA Modulation Networks
指導教授:黃宣誠
指導教授(外文):Hsuan-Cheng Huang
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:40
中文關鍵詞:長鏈非編碼核糖核酸競爭內生性核糖核酸微型核糖核酸核糖核酸調節網路
外文關鍵詞:lncRNAceRNAmicroRNAmodulation network
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  • 被引用被引用:0
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  • 下載下載:7
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近年來的研究發現,人類的轉錄組中有大量的非編碼核糖核酸,而且非編碼核糖核酸也與癌症的生成有關。非編碼核糖核酸中的其中一種為長鏈非編碼核糖核酸(long non-coding RNA,簡稱 lncRNA),與其他小型的核糖核酸(例如:微型核糖核酸,簡稱 miRNA)的差別為:lncRNA是長度大於兩百個核甘酸的非蛋白編碼轉錄本。就像信使核糖核酸(message RNA,簡稱 mRNA),lncRNA也含有miRNA的結合位置。而且它們也能透過競爭相同的miRNA達到相互調控的功能,就像競爭內生性核糖核酸(compecting endogenous RNA,簡稱 ceRNA)。了解這些RNA轉錄本之間的關係,可以讓我們有效分析基因調控網路,也能讓我們更了解人類的疾病。
在我們的研究中,我們從癌症基因體圖譜(TCGA)中收集了膠質母細胞瘤的lncRNA,mRNA,miRNA表現量資料,並發展了新的綜合方法來分析調控網路。我們的方法能將癌症病人分成不同的群集,並對不同群的癌症病人的調控網路進行分析。首先,我們利用網路融合的方法將三種不同的RNA資料融合再一起,並將病人分成不同的群集。接著,利用不同的關聯索引方法來量化lncRNAs,mRNAs和miRNAs之間的關係。最後,我們利用它們所產生的調控網路來推論ceRNA對mRNA及lncRNA的影響。研究結果顯示不同病人群集之間的調控網路差異及lncRNA與ceRNA對膠質母細胞瘤的影響。

Recent studies found that non-coding RNAs represent most of the human transcriptome and were often dysregulated in cancer. A major family among them is the long non-coding RNA (lncRNA), defined as the non-protein coding transcripts longer than 200 nucleotides to distinguish from small regulatory RNAs such as microRNAs (miRNAs). Like mRNAs, lncRNAs contain miRNA-binding sites and can communicate with and regulate each other by competing specifically for shared miRNAs, thus acting as competing endogenous RNAs (ceRNAs). Understanding the crosstalk among these RNA transcripts will lead to significant insight into gene regulatory networks and have implications in human disease.
In this study, we collected the lncRNA, mRNA, and miRNA expression profiles of glioblastoma multiforme from The Cancer Genome Atlas, and developed a novel integrative network analysis approach to construct the modulation networks in diverse cancer clusters and elucidate their regulatory complexity. We first applied a similarity network fusion method to aggregate the three types of RNA data and stratify the cancer clusters. Next, we evaluated several association index methods to quantify the correlation among lncRNAs, mRNAs, and miRNAs. Finally, we applied a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs. The results revealed the network characteristics and stratification of lncRNA and ceRNA modulation in cancer.

中文摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vii
Chapter 1 Introduction 1
1.1 Long Non-Coding RNAs 1
1.2 Competing Endogenous RNAs 1
Chapter 2 Materials and Methods 4
2.1 Expression Profiles 4
2.2 MicroRNA Targets 4
2.3 Unify MicroRNA Name 4
2.4 Gene Ontology Annotations 5
2.5 Analysis of lncRNA Modulation Networks 5
2.5.1 Similarity Network Fusion 5
2.5.2 Pearson Correlation Coefficient 7
2.5.3 Refinement of Co-expression Network 8
2.5.4 Association Index 8
2.5.5 Association Modules 10
Chapter 3 Results 11
3.1 Patient Clustering 11
3.2 Co-expression Networks 12
3.3 Intersection with miRNA Target Profile 14
3.4 Association Index 15
3.5 Modulation Networks and Their Functions 22
Chapter 4 Discussion and Conclusions 38
REFERENCE 39

LIST OF FIGURES
Figure 1 Competing endogenous RNA (ceRNA). 2
Figure 2 The relation of ceRNA pair and shared miRNA. 3
Figure 3 Example of SNF steps. 6
Figure 4 The flowchart of the analysis on lncRNA modulation networks. 7
Figure 5 Concept of association index. 9
Figure 6 Analysis of modulation networks. 10
Figure 7 Results of patient clustering. 11
Figure 8 Co-expression of RNA pairs. 12
Figure 9 Scatter plots of mRNA association score of cluster 1 in mRNA-miRAN and lncRNA-mRNA co-expression networks. 16
Figure 10 Scatter plots of mRNA association score of cluster 2 in mRNA-miRAN and lncRNA-mRNA co-expression networks. 17
Figure 11 Scatter plots of lncRNA association score of cluster 1 in lncRNA-miRAN and lncRNA-mRNA co-expression networks. 18
Figure 12 Scatter plots of lncRNA association score of cluster 2 in lncRNA-miRAN and lncRNA-mRNA co-expression networks. 19
Figure 13 Scatter plots of mRNA association score of cluster 1 in original mRNA-miRAN and lncRNA-mRNA co-expression networks. 20
Figure 14 Scatter plots of mRNA association score of cluster 1 in original mRNA-miRAN and lncRNA-mRNA co-expression networks. 21
Figure 15 The modulation network of cluster 1 from mRNA pairs in new co-expression network by cosine index. 25
Figure 16 The modulation network of cluster 2 from mRNA pairs in new co-expression network by cosine index. 26
Figure 17 The modulation network of cluster 1 from lncRNA pairs by cosine index. 27
Figure 18 The modulation network of cluster 2 from lncRNA pairs by cosine index. 28
Figure 19 The modulation network of cluster 1 from mRNA pairs in original co-expression network by cosine index. 29
Figure 20 The modulation network of cluster 2 from mRNA pairs in original co-expression network by cosine index. 30
Figure 21 The result of intersecting with mRNA modulation network of cluster 1 and cluster 2. 31
Figure 22 The result of intersecting with lncRNA modulation network of cluster 1 and cluster 2. 31
Figure 23 The result of intersecting with mRNA modulation network of cluster 1 and cluster 2 from original co-expression network. 32
Figure 24 The GO function of modulation network. 32
Figure 25 The GO function of cluster 1 lncRNA modulation network. 33
Figure 26 The GO function of cluster 2 lncRNA modulation network. 34
Figure 27 The GO function of cluster 1 mRNA modulation network from original co-expression network. 35
Figure 28 The GO function of cluster 2 mRNA modulation network from original co-expression network. 36

LIST OF TABLES
Table 1 Amount of interactions and RNAs in each network. 13
Table 2 The amount of interactions, mRNAs and miRNAs of original and new mRNA-miRNA co-expression network. 14
Table 3 The number of high association score RNA pairs. 22
Table 4 The amounts of each types of RNAs and interactions in each modulation network. 24
Table 5 The amounts of each type of RNAs and interactions of modulation networks constructed form original co-expression networks. 24
Table 6 The lncRNA list in each modulation network. 37
Table 7 The highly appearing frequently lncRNAs list. 37

Aguilo, F., Zhou, M.M., and Walsh, M.J. (2011). Long noncoding RNA, polycomb, and the ghosts haunting INK4b-ARF-INK4a expression. Cancer Res. 71, 5365-5369.
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.H., Pages, F., Trajanoski, Z., and Galon, J. (2009). ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091-1093.
Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M.C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C., et al. (2005). The transcriptional landscape of the mammalian genome. Science 309, 1559-1563.
Du, Z., Fei, T., Verhaak, R.G., Su, Z., Zhang, Y., Brown, M., Chen, Y., and Liu, X.S. (2013). Integrative genomic analyses reveal clinically relevant long noncoding RNAs in human cancer. Nat. Struct Mol. Biol. 20, 908-913.
Enright, A.J., John, B., Gaul, U., Tuschl, T., Sander, C., and Marks, D.S. (2003). MicroRNA targets in Drosophila. Genome Biol. 5, R1.
Faghihi, M.A., Modarresi, F., Khalil, A.M., Wood, D.E., Sahagan, B.G., Morgan, T.E., Finch, C.E., St Laurent, G., 3rd, Kenny, P.J., and Wahlestedt, C. (2008). Expression of a noncoding RNA is elevated in Alzheimer's disease and drives rapid feed-forward regulation of beta-secretase. Nat. Med. 14, 723-730.
Fuxman Bass, J.I., Diallo, A., Nelson, J., Soto, J.M., Myers, C.L., and Walhout, A.J. (2013). Using networks to measure similarity between genes: association index selection. Nat. Methods 10, 1169-1176.
Lu, T.P., Lee, C.Y., Tsai, M.H., Chiu, Y.C., Hsiao, C.K., Lai, L.C., and Chuang, E.Y. (2012). miRSystem: an integrated system for characterizing enriched functions and pathways of microRNA targets. PloS One 7, e42390.
Perkel, J.M. (2013). Visiting "noncodarnia". BioTechniques 54, 301, 303-304.
Salmena, L., Poliseno, L., Tay, Y., Kats, L., and Pandolfi, P.P. (2011). A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146, 353-358.
Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504.
Wang, B., Mezlini, A.M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., and Goldenberg, A. (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333-337.

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