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研究生:郭慶妤
研究生(外文):Ching-Yu Kuo
論文名稱:Imiquimod與Resiquimod對基底細胞癌微陣列晶片分析與基因網路之建構
論文名稱(外文):Microarray Analysis and Gene Network Construction of Imiquimod and Resiquimod Treatment to Basal Cell Carcinoma
指導教授:蔡孟勳蔡孟勳引用關係劉俊吉
指導教授(外文):Meng-Hsiun TsaiChun-Chi Liu
口試委員:陳健祺
口試委員(外文):Jian-Chyi Chen
口試日期:2017-06-21
學位類別:碩士
校院名稱:國立中興大學
系所名稱:基因體暨生物資訊學研究所
學門:生命科學學門
學類:生物訊息學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:66
中文關鍵詞:基底細胞癌微陣列ImiquimodResiquimod
外文關鍵詞:Basal cell carcinomaMicroarrayImiquimodResiquimod
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皮膚癌是白種人中最常見的癌症疾病之一,且病患人數逐年增加。基底細胞癌占所有皮膚癌的八成病例,而市面上治療的藥物─Imiquimod能有效減緩基底細胞癌帶來的困擾。然而,Imiquimod於基底細胞癌的藥物分子機制途徑尚未明瞭。因此,本研究使用資料探勘技術來篩選出Imiquimod與其類似物Resiquimod的標記基因,並根據IPA生物資料庫建立基因網路。許多科學家利用生物化學及分子方法逐步篩選相關標記物,耗費大量實驗材料、人力與時間。本研究以藥物施予基底細胞癌細胞之微陣列資料為樣本,利用資訊增益進行篩選標記基因,並透過火山圖分析對標記基因進行驗證,達到快速預測標記基因。結果顯示,Imiquimod之標記基因為ARRDC3、INSIG1與GDF15;Resiquimod之標記基因為CYP1A1、CUL1與EVL;兩藥物之間的標記基因為ID3、B4GALT1與ZMAT3,並根據IPA生物資料庫探討各標記基因之相關性。本研究所預測之基因網路仍須以生物實驗驗證,期望驗證後能提供未來臨床研究之依據。
Skin cancer is one of the most common cancer diseases among white people and the number of patients increases every year. Basal cell carcinoma accounts for 80% of all skin cancer cases; the mainstream treatment drug, Imiquimod, can effectively slow down its symptoms. However, the drug's molecular mechanism of Imiquimod in basal cell carcinoma is still unclear. In this research, data mining technique is used to filter out the biomarkers of Imiquimod and its analogue – Resiquimod, to set up a genetic network based on the IPA biological database. Many scientists use biochemical and molecular methods to gradually filter the drug-marker, which consumes a lot of experimental materials, manpower, and time. In this study, the microarray data of basal cell carcinoma was used as sample, which the biomarkers can be filtered out by Information Gain and verified by volcano plot analysis to quickly predict. The results showed that, the biomarkers of Imiquimod are ARRDC3, INSIG1, and GDF15, and for Resiquimod are CYP1A1, CUL1, and EVL. Since the biomarkers of both drugs are ID3, B4GALT1, and ZMAT3, the relevance between each biomarker was investigated based on IPA bioinformatics database. In this study, we expect to provide a basis for future clinical research. A biological experiment will still be needed to validate the prediction of the genetic network.
誌謝 i
摘要 ii
ABSTRACT iii
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 3
第二章 文獻探討 4
2.1 皮膚癌(Skin cancer) 4
2.2 Imiquimod與Resiquimod 7
2.3 微陣列基因晶片(Microarray) 8
2.4 資料探勘(Data Mining) 9
第三章 研究方法 13
3.1 資料來源(Data Source) 13
3.2 研究環境(Research Environment) 14
3.2.1 Weka資料探勘軟體 14
3.2.2 Matlab軟體 15
3.2.3 IPA生物資訊軟體 15
3.3 資料預處理(Data Preprocessing) 16
3.3.1 皮爾森相關係數 (Pearson correlation coefficient) 16
3.3.2 SMOTE 17
3.3.3 分位數正規化(Quantile Normalization) 18
3.3.4 資料離散化(Discretization) 19
3.4 標記基因篩選(Biomarkers Selection) 20
3.4.1 資訊增益(Information gain) 20
3.4.2 假設檢定(Hypothesis Testing) 21
3.4.3 火山圖分析(Volcano Plot) 23
3.4.4 階層式分群法(Hierarchical Clustering) 23
3.5 基因網路(Genetic Network) 24
3.5.1 建構基因網路之流程 25
3.5.2 建構基因網路之探討 25
第四章 研究結果 26
4.1 資料預處理結果 26
4.1.1 皮爾森相關係數(Pearson correlation coefficient) 26
4.1.2 SMOTE 31
4.1.3 分位數正規化(Quantile Normalize) 32
4.1.4 資料離散化(Discretization) 33
4.2 標記基因篩選結果 34
4.2.1 資訊增益篩選結果 34
4.2.2 火山圖篩選結果 35
4.2.3 階層式分群法結果 40
4.3 基因網路建構 41
4.3.1 標記基因與基底細胞癌相關性 42
4.3.2 標記基因之疾病與功能 52
第五章 結論與未來展望 53
5.1 結論 53
5.2 未來展望 53
參考文獻 54
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