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標題: | 探討轉錄因子與微小核醣核酸的共同調控與肺腺癌的關係 The Co-regulations of Transcription Factors and MicroRNAs in Lung Adenocarcinoma |
作者: | Yun-Ju Sun 孫韻如 |
指導教授: | 陳中明(Chung-Ming Chen) |
關鍵字: | 微小核糖核酸,肺腺癌,轉錄後調控,共同調控,模糊推論系統, microRNAs,lung adenocarcinoma,post-transcriptional regulation,co-regulation,fuzzy inference system, |
出版年 : | 2011 |
學位: | 碩士 |
摘要: | 本研究的目的在探討在肺腺癌 (lung adenocarcinoma, ADC)中哪些轉錄因子 (transcriptional factors, TFs)、微小核糖核酸 (microRNAs, miRNAs)與基因間會發生調控關係,並預測可能影響肺腺癌發生的TF-miRNA共同調控 (co-regulated)目標基因關係。
在基因調控方面,已知TF對目標基因扮演主要的轉錄調控(transcriptional regulation)角色,miRNA在轉錄後扮演微調的角色。MicroRNAs是一群約22nt~28nt短的非編碼RNAs (non-coding RNAs),透過結合上它們的目標基因 (target genes)達到轉錄後調控 (post-transcriptional regulation)。研究顯示許多miRNAs會參與癌症,由分析差異表現得知對不同種類的癌症組織有不同的miRNAs參與調控,並可以分成分別抑制致癌基因 (oncogenes)及腫瘤抑制基因 (tumor suppressor genes)的tumor suppressor miRNAs 及oncomirs。癌症中長久以來以肺癌 (lung cancer)位居癌症死亡率第一,其中80%的病人為非小細胞肺癌 (non-small cell lung cancer, NSCLC),又以肺腺癌為大宗。目前對TF調控基因方面已有眾多研究,但miRNA如何參與調控並不是很了解。為了更了解肺腺癌的形成原因以及TFs與miRNAs在癌症中的功能,因此探討TF與miRNA在肺腺癌中如何共同調控目標基因。 本研究建構了一個兩階段的模糊模型 (two-stage fuzzy model)來模擬TF與miRNA調控目標基因的過程,並預測在肺腺癌中TF與miRNA合作調控目標基因的關係。第一階段模擬TF結合到目標基因,擷取序列資訊用AdaFuzzy演算法做學習並預測TF對目標基因的轉錄調控,第二階段模擬第一階段所預測的目標基因被TF調控後是否受到miRNA轉錄後調控而形成共同調控。在第二階段除了序列資訊還加入肺腺癌的微陣列 (microarray)基因表現量,結合預測軟體TargetScan及miRanda計算分數來學習預測miRNA與基因的結合強度以及第一階段預測TF與基因的結合強度使用模糊推論系統 (fuzzy inference system, FIS)來預測TF-miRNA共同調控的關係。 結果顯示,所預測出來的共同調控關係中,包含一個已由實驗證實在肺腺癌中有調控的三者關係,E2F─Cyclin E─miR-15。透過分析細胞週期 (cell cycle)癌症途徑中Cyclin E的上游基因不顯著表現推論此關係可能參與肺腺癌的形成。進一步分析分數相近或高於已知三者關係可預測出可能透過共同調控影響肺腺癌的TFs與miRNAs,包含與cell cycle有關的E2F─Cyclin E─miR-15ab/195/185/107以及與p53 signaling pathway有關的p53─TIMP3─miR-21。由結果推測,肺腺癌的形成可能受到這些TF-miRNA合作調控的影響。透過此方法使我們更了解肺癌的形成原因與miRNAs扮演的功能,以及miRNAs如何參與癌症途徑。另一方面,對於癌症的治療,共同調控的TF-miRNA可被來設計來更有效率殺死癌細胞。 The purpose of this study is to uncover the relations of certain transcription factors (TFs) and microRNAs (miRNAs) regulating their target genes seperately using genomics data, and to further predict the co-regulation of TF and miRNA on their target genes, which may be involved in the lung adenocarcinoma (ADC) in human. In terms of gene regulation, TFs are known to play a major role on regulating their targets (TF-targets), while miRNAs play a fine-tuning role at the targets' post-transcriptional levels (miRNA-targets). MicroRNAs are a group of short and 22nt ~ 28nt non-coding RNAs, which bind to their targets' 3'UTR sites to carry out post-transcriptional regulation. Previous studies have shown that many miRNAs may be involved in cancers. By analyzing the differential expressions of miRNAs, different miRNAs were uncovered to be involved in different kinds of cancer. MicroRNA could be classified into tumor suppressor miRNAs or oncomirs by their functions of inhibiting oncogenes or tumor suppressor genes. The death rate of lung cancer has long been ranked the top one among all cancers in Taiwan, of which 80% of patients are non-small cell lung cancer (NSCLC), in which the adenocarcinoma subtype is the most common. There many of studies on TF-gene regulations, but how miRNAs are involved in the regulation of lung ADC is not well understood. This study proposes a model to infer which TF and which miRNA co-regulate their target genes in lung ADC. A two-stage fuzzy method was constructed to model the processes of TF and miRNA regulating targets sequentially using genomics data, and a fuzzy inference system (FIS) was used to infer the co-regulation of TFs and miRNAs. The first stage modeled the process of TFs' binding to their targets. We first extracted DNA sequence data and inputed them into AdaFuzzy which learned and infered the TF-target regulation. In the second stage, AdaFuzzy and FIS modeled whithin a target gene in a predicted TF-target pair was further regulated by a miRNA, thus this triple had a co-regulation relation. In addition to sequence information, microarray gene expression data (MGED) containing lung ADC tissues and normal lung tissues was also used in the FIS. Integrating MGED, the TF-target binding strength predicted in the first stage and the miRNA-target relation strength as inputs, the FIS could infer the co-ragulations resonably. The miRNA-target were predicted by AdaFuzzy which learned binding strength scores from miRanda and TargetScan. The results show that the predicted co-regulations include a validated triple E2F─Cyclin E─miR-15 which has been confirmed by lung ADC cell lines. Because the upstream genes of cyclin E were not significantly expressed in the cell cycle pathway, and both E2F and miR-15 differentially expressed, we infered that this triple may be involved in the tumorigenesis of lung ADC. Candidate co-regulation of TF-miRNAs, which consisted of cell cycle-related E2F─Cyclin E─miR-15ab/195 /185/107 and p53 signaling pathway-related p53─TIMP3─miR-21 were further predicted from the triples having similar or higher score of the valified triple. These results are attempts to depict the roles of miRNAs play and their co-regulations in lung ADC. On the other hand, a potential therapy may be designed via the relationship of TF-miRNA co-regulation to kill cancer cells efficiently. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30496 |
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顯示於系所單位: | 醫學工程學研究所 |
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