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研究生：	陳疇丞 Chen, Chou-Cheng
論文名稱：	文章探勘和資料探勘在癌症研究之運用 Application of text mining and data mining in cancer research
指導教授：	何中良 Ho, Chung-Liang
學位類別：	博士 Doctor
系所名稱：	醫學院 - 基礎醫學研究所 Institute of Basic Medical Sciences
論文出版年：	2017
畢業學年度：	105
語文別：	英文
論文頁數：	93
中文關鍵詞：	文章探勘、資料探勘、幹細胞、大腸癌、肝癌
外文關鍵詞：	data mining, text mining, stem cell, colorectal cancer, liver cancer, cancer stem cell, TCGA
相關次數：	點閱：141 下載：17
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這個研究主要是利用文章探勘與資料探勘來篩選有關肝癌與大腸癌的基因，我們利用EST資料探勘與文獻回顧找出五十個未知功能的基因，並且藉由實驗發現ZNF496、RMI2和U41可能為WNT標的基因並且與肝癌有關。在已知基因部分，我利用自己撰寫的文章探勘工具PubstractHelper、StemTextSearch與現有的資料探勘工具GeneCards、NCBI的GEO找出二十個可能基因，經由實驗驗發現IGF2BP1可能為再復發肝癌的在血液循環中oncofetal幹細胞相似標記。我利用TCGA的資料來資料探勘並且尋找出二十三個可能與大腸癌相關的基因，我們最後選出可以買到抗體的三個基因並且利用免疫組織化學染色測試是否在大腸癌有表現。這個研究顯示了我們可以利用文章探勘與資料探勘來幫助科學家縮小可能與癌症相關的候選基因。

This study aimed to use text and data mining to select candidate genes which are associated with liver and colorectal cancer. Fifty unknown candidate genes were selected by data mining the EST library, and ZNF496, RMI2 and U41 were found that may be associated with WNT target genes and liver cancer. Twenty known candidate genes were selected by text mining PubstractHelper and StemTextSearch, and data mining GeneCards and GEO of NCBI. IGF2BP1 was found to be associated with the oncofetal circulating cancer stem cell-like markers associated with the recurrence of hepatocellular carcinoma by experiment. Twenty-three candidate genes were selected by data mining from TCGA (the cancer genome atlas) data, and the three remaining candidate genes are examined as to whether they are expressed in colorectal cancer by IHC (immunohistochemistry). This study shows that text and data mining are alternative methods to help scientist narrow down their candidate genes which are associated with cancer.

Contents
Introduction ..........................8
Methods ..........................12
Results ..........................25
Discussion...........................32
Conclusion...........................36
References  ..........................36
Appendix 1  ..........................74
Appendix 2  ..........................78
Appendix 3  ..........................79
Figure Contents
Figure 1.The flowchart of the unknown gene selection and the main steps of the computational procedure...........41
Figure 2. An example query from PubstractHelper ....................42
Figure 3.The flowchart of the StemTextSearch database and the main steps of the computational procedure ..........43
Figure 4.The flowchart of the collection of stem-cell terms and the main steps of the computational procedure .........44
Figure 5.An example of a sentence in the new abstract which is generated by replacing the gene name by the symbol ‘* .........45
Figure 6.Example token sentences......................46
Figure 7.Example results defined by the R score ....................47
Figure 8.Example training corpus......................48
Figure 9.The flowchart of gene selection from TCGA and the main steps of the computational procedure............49
Figure 10.An example of the results obtained by the algorithm, with the algorithm shown in Appendix 2 ...........50
Figure 11.Example of the results containing multiple gene names or multiple stem-cell terms ............51
Figure 12.An example of the results on the web interface from a user query...............53
Figure 13.RT-PCR results of twenty candidate genes in liver cancer cell lines and whole blood tissue............54
Figure 14.RT-PCR results of twenty candidate genes in oncofetal pattern and IPS ..............55
Figure 15.RT-PCR results of IGF2BP1 in normal (N) and cancer (T) tissue ................56
Table Contents
Table 1.Sixty-five genes associated the Wnt pathway and EST libraries .................57
Table 2.The abbreviations of species terms were produced from taxdump.tar.gz. ...............61
Table 3.Twenty remaining genes were selected by text mining, data mining and literature review............. 62
Table 4.Primers of each gene for RT-PCR ......................63
Table 5.Primers of HNF4A and MSI1 for RT-qPCR .....................64
Table 6.Twenty-three remaining genes selected by data mining..................65
Table 7.Fifty remaining unknown genes were selected by data mining and literature review. .............67
Table 8.Precision and recall of each step .....................69
Table 9.RT-qPCR results of HNF4A and MSI1 in whole blood..................70
Table 10.RT-qPCR results of HNF4A and MSI1 in normal tissue and cancer tissue ...............71
Table 11.RT-qPCR results of IGF2BP1 (IMP1), U41 and Lin28B in liver tissue...............72
Table 12.The published information of 23 genes obtained by PubMed query ................73
                                    

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