臺灣博碩士論文加值系統

直至民國九十九年，癌症為我國十大癌症死因之首，尤以肺癌為之最。其中，非小細胞癌的治療方式有所謂的標靶治療，標靶治療的原則是針對癌細胞的突變、增值或擴散的機制，阻斷癌細胞生長或修復的作用，或是抑制腫瘤血管新生，達到抑制癌細胞生長、促進癌細胞死亡、防止癌細胞擴散。因此，我們希望可以找到在人類鱗狀肺癌中，具有影響力的組合轉錄因子和被其調控的標靶基因。在本研究中，我們使用了Gene Expression Omnibus (GEO) 和Stanford Microarray Database (SMD) 這兩個資料庫所提供的基因晶片資料，並且利用TRANSFAC這個資料庫所提供的和轉錄因子相關的資料及Match這個工具。我們希望能夠用已知的和肺癌相關的轉錄因子(transcription factor, TF)和其會在一群啟動子(promoter)序列上共同出現 (co-occurrence) 的轉錄因子結合位置(transcription factor binding site, TFBS)來分析，用生物資訊的方式去找出有可能和該轉錄因子的轉錄因子結合位置成為組合的轉錄因子結合位置(combinatorial TFBSs)，並且用了Match工具所提供的co-score和matrix-score以及兩個轉錄因子結合位置之間相距的距離當作過濾的條件，進而去找出可能會共同調控(cooperative)的其他轉錄因子。希望能將有關肺癌的基因調控網路建構的更加完整，或許就能使得標靶治療成功率更加提升。

Until the year 2010, cancer is the top one of ten leading cause of deaths, in which lung cancer is the most critical one. Target therapy is an effective method to cure non-small cell lung cancer. The idea of target therapy is using medicine to prevent from the mechanism of mutation, proliferation, and the extension to inhibit growth or repair of cancer cells. Target therapy also can repress tumor angiogenesis to reduce growth, enhance death, and avoid extension of cancer cells. Therefore, we want to find combinatorial TFs and their a group of target genes in human squamous cell lung cancer. In our research, we not only use microarray data from Gene Expression Omnibus (GEO) and Stanford Microarray Database (SMD) but also extract the information of transcription factors (TFs) and the TF binding site detection tool (Match) from TRANSFAC database. We develop a method to identify the known transcription factors related to lung cancer and the co-occurrence of transcription factor binding sites in a group of co-regulated genes. Moreover, we use co-score and matrix-score calculated by Match and the distance between combinatorial transcription factor binding sites as the detection constraints. This work can identify potential transcription factors that cooperate with known transcription factors in squamous cell lung cancer. The regulatory network of cooperative TFs can improve the success rate of target therapy in lung cancer.

書名頁 i
審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目 錄 vii
表 目 錄 ix
圖 目 錄 x
第一章 簡介 1
1.1 背景知識 2
1.1.1 基因轉錄 2
1.1.2 基因表現 3
1.1.3 轉錄起始位置、轉錄因子、轉錄因子結合位置 4
1.1.4 共同出現、組合的轉錄因子結合位置、共同調控 5
1.2 動機 6
1.3 目標 6
第二章 相關研究 7
2.1 IRF1與肺癌 7
2.2 定義共同出現的轉錄因子結合位置 7
2.2.1 TRANSFAC資料庫 7
第三章 資料與方法 9
3.1 資料 9
3.1.1 人類基因序列 9
3.1.1.1 基因序列長度選取 10
3.1.2 基因表現資料(Gene Expression Data) 10
3.1.3 已知的TFBS資料庫 12
3.1.4 基因和蛋白質功能資料庫 13
3.2 方法 13
3.2.1 系統流程 14
3.2.2 基因晶片分析工具(Tree View) 15
3.2.3 基因表現差異量 15
3.2.4 特定轉錄因子和基因的篩選 16
3.2.5 過濾標準 18
第四章 結果 19
4.1 Case Study 1 19
4.1.1 IRF1的20個targets 19
4.1.2 SP3的20個targets 20
4.1.3 IRF1的組合轉錄因子結合位置 20
4.1.4 SP3的組合轉錄因子結合位置 22
4.1.5 Down-regulated Group的GO統計 24
4.1.6 Up-regulated Group的GO統計 27
4.2 Case Study 2 30
4.2.1 統計三組晶片數量 31
4.2.2 GO － Biological Processes 32
4.2.2.1 Down-regulated Group 32
4.2.2.2 Up-regulated Group 35
4.3 Case Study 3 39
第五章 討論 42
5.1 用已知的TF和targets找出可能的組合 42
5.2 同時看三組晶片的分析 43
5.2.1 比較Case 1的TF和基因於三組晶片的值 43
5.2.2 利用GO ID對同一群的基因和TF做分析 46
第六章 結論與未來工作 49
參考文獻 51

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