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研究生:戴珮瑄
研究生(外文):Pei-Syuan Tai
論文名稱:乳癌中超級增強子核醣核酸與基因共表現網路之關係
論文名稱(外文):Super-enhancer RNA associated gene co-expression networks in breast cancer cell
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:2017
畢業學年度:105
語文別:英文
論文頁數:44
中文關鍵詞:超級增強子MCF-7共表現網路
外文關鍵詞:super-enhancersMCF7co-expression networks
相關次數:
  • 被引用被引用:0
  • 點閱點閱:161
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  • 下載下載:4
  • 收藏至我的研究室書目清單書目收藏:0
超級增強子是在基因附近發現由一群密集增強子所組成的,並且在調節基因的表現上扮演一個關鍵的角色,也可以確定細胞變化和功能。過去研究將有活性的增強子區域產生雙向轉錄的產物稱為enhancer RNAs。在FANTOM5 已經提出利用Cap Analysis of Gene Expression (CAGE)定序技術,經過時間的分析,我們可以延伸了解在啟動子調節下促進細胞狀態轉變的先後順序。在本研究使用CAGE時間序列資料中,受到表皮生長因子(epidermal growth factor, EGF)或是調蛋白
(heregulin, HRG)刺激MCF-7 乳癌的資料進行分析。首先,我們利用權重基因共表現網路分析(weighted gene co-expression network
analysis, WGCNA)建立基因共表現網路,和多個共表現基因的模組。第二,我們使用非負矩陣分解(non-negative matrix factorization, NMF)將超級增強子RNA 的表現量資料依照時間序列去分群,細胞受到刺激後的狀態轉換過程中,會對應到不同的群。接著比較不同群中排名前面的超級增強子RNA 所對應到的基因和共表現模組的基因之相似程度。我們選擇在不同群下較顯著的模組,最後利用富集
分析解釋其生物過程功能和確認每個細胞狀態轉變中可能存在的先驅轉錄因子。我們的分析結果發現ErbB 受體是刺激乳癌細胞導致細胞變化的關鍵因子。
Super-enhancers are enhancer-dense regions found near genes that play key roles in determining cellular and functional identity through regulation of gene xpression. It has been found that active enhancers generate bidirectional transcripts called enhancer RNAs. The time-course Cap Analysis of Gene Expression (CAGE) has been proposed by the FANTOM5 Consortium to extend the understanding of the sequence of events facilitating cell state transition at the level of promoter regulation. Here, we have performed an integrative analysis on the CAGE time-course datasets of MCF-7 breast cancer cells stimulated by epidermal growth factor (EGF) or heregulin (HRG). We first constructed their gene co-expression networks using Weighted Gene Co-expression Network Analysis (WGCNA) and observed several co-expression gene modules. Second, we applied non-negative matrix factorization (NMF) that can divide the super-enhancer RNA expression profiles into different states
corresponding to the stimulated cell state transitions along the time course. By comparison of the genomic
proximity of the top super-enhancer RNAs in different states with the co-expression gene modules, we identified the active modules for each decomposed cell state. Last,
we performed enrichment analyses to elucidate their associated biological functions and to identify the plausible pioneering transcription factors for each cell state transition. Our analysis results revealed a clue of the key regulatory factors for cellular changes of breast cancer cells stimulated by ErbB receptors.
中文摘要i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vii
Chapter 1 Introduction 1
Chapter 2 Materials and Methods 5
2.1 Method Overview 5
2.2 Data Collection 7
2.3 Correlation analysis 7
2.4 Determination of cell states and super-enhancers selection 8
2.5 Network construction and modules detection 10
2.6 Fisher’s exact test 12
2.7 Gene Ontology Annotation 15
Chapter 3 Results 16
3.1 Cell states of EGF and HRG stimulation 16
3.2 Significant transcription factors21
3.3 Gene co-expression network and modules 24
3.4 Significant modules in different states 27
3.5 Key cancer genes in modules 31
3.6 Biological functions of significant modules 35
Chapter 4 Discussion 39
Chapter 5 Conclusion 41
References 42

List of Figures
Figure 1 Clustering of enhancers 2
Figure 2 Determination of super-enhancer RNAs 2
Figure 3 Data analysis workflow 6
Figure 4 Non-negative matrix factorization illustration diagram 9
Figure 5 Significant transcription factors and module 14
Figure 6 Super-enhancer RNA of HRG in two states 17
Figure 7 Super-enhancer RNA of HRG in three states 18
Figure 8 Super-enhancer RNA of EGF in two states 19
Figure 9 Super-enhancer RNA of EGF in three states 20
Figure 10 Top 30 percent of super-enhancer RNA of HRG in two states 22
Figure 11 Enrichment of 120 transcription factors 23
Figure 12 Clustering of 8906 genes 24
Figure 13 Network heatmap plot 26
Figure 14 Significant modules 28
Figure 15 Dark-green module 29
Figure 16 Green module 29
Figure 17 Orange module 30
Figure 18 Black module 30
Figure 19 Green module with transcription factors 33
Figure 20 Darkgreen module with transcription factors 33
Figure 21 Orange module with transcription factors 34
Figure 22 Black module with transcription factors 34
Figure 23 Biological functions of black module 36
Figure 24 Biological functions of orange module 36
Figure 25 Biological functions of darkgreen module 37
Figure 26 Biological functions of green module 37

List of Tables
Table 1 List of co-expression modules 25
Table 2 Number of interaction of top 30% SE-proximal genes 28
Table 3 About the transcription factors and SE-proximal genes in late state 32
Table 4 About the transcription factors and SE-proximal genes in early state 32
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