以系統生物學方法探討肺腺癌抗藥性成因

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：38

、訪客IP：3.144.233.150

姓名

李伯詮(Po-Chuan Lee) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

以系統生物學方法探討肺腺癌抗藥性成因
(Systems Biology Approaches to Explore Lung Adenocarcinoma Resistant to Gefitinib)

相關論文

★ 整合深度學習方法預測年齡以及衰老基因之研究	★ 運用深度學習方法預測阿茲海默症惡化與腦中風手術存活
★ 運用深度學習方法預測癌症種類及存活死亡與治癒復發	★ 基於檢驗數值的糖尿病腎病變預測模型
★ 機械循環拉伸對肺癌細胞功能的影響之研究	★ 整合多種基因組型態資料預測肺腺癌患者存活之研究
★ 以系統生物學策略探討臍帶血來源之造血幹細胞分子調控網路	★ TP53突變對具有EGFR突變的非小細胞肺癌患者帶來的影響
★ 機械循環拉伸力對3D培養肺癌細胞之影響	★ PM2.5對人類心肺細胞的影響
★ 尼曼匹克症轉錄體學研究	★ 體外仿生肺肝纖維化3D模型研究
★ 肝纖維化細胞與動物模型以轉錄體資料分析比較	★ 基於深度學習之皮膚病兆切割之研究
★ 在大腸桿菌與酵母菌蛋白質體晶片中量化其蛋白質的濃度	★ 探討血液透析患者中腸胃道菌相之變化

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

時至今日，肺癌仍是台灣十大癌症死因之首，其中肺腺癌占大宗。已經有許多文
獻對肺腺癌產生gefitinib 抗藥性做出研究。雖然已有部分研究透過分析細胞及外泌體
中的遺傳物質找出抗藥性產生的相關訊號通路，但是都僅針對單一一種遺傳物質進行
探討。在這裡我們使用兩種因EGFR 突變而導致的人類肺腺癌細胞系，分別為PC-9
和PC-9/IR，其中後者對gefitinib 具有抗藥性，並進行細胞培養。我們從細胞系中分
離其外泌體，最後篩選出外泌體中具有顯著表現差異的miRNA、RNA 和蛋白質以及
細胞系自身的RNA 等基因產物（fold change > 2 或 < -2），並透過富集分析的方式推
測miRNA 下游調控的基因。透過交叉比對外泌體中三種基因產物，我們共找到109
個在基因在外泌體的RNA 和蛋白質有明顯上調的趨勢，其中又有16 個基因的交互作
用miRNA 呈現下調，包含IGF2。在KEGG PATHWAY 富集分析中，發現IGF2 與
EEGFR 相關的KEGG 途徑有關。並透過KEA 富集分析表明，IGF2 相較於激酶，更
有可能是受到miRNA 導致的上調控。

摘要(英)

To date, lung cancer is still the top ten cause of death from cancer in Taiwan, and lung
adenocarcinoma accounts for the majority. There have been many studies on the development
of gefitinib resistance in lung adenocarcinoma. Although some studies have found out the
signaling pathways related to drug resistance through the analysis of genetic material in cells
and exosomes, they have only focused on a single genetic material. Here we use two human
lung adenocarcinoma cell lines caused by EGFR mutations for cell culture, PC-9 and PC-9/IR,
the latter of which is resistant to gefitinib.
We isolated exosomes from cell lines, and finally screened out miRNAs, RNAs and
proteins with significant performance differences in exosomes, as well as the cell line’s own
RNA and other gene products (fold change > 2 or < -2). Through cross-comparison of the three
gene products in exosomes, we found a total of 109 genes in exosomes that have a significant
up-regulation trend in RNA and protein. Among them, 16 gene interaction miRNAs showed
down-regulation, including IGF2.In the KEGG PATHWAY enrichment analysis, IGF2 was
found to be related to EEGFR-related enrichment KEGG PATHWAY. And through the KEA
enrichment analysis, IGF2 is more likely to be upregulated by miRNA than kinase.

關鍵字(中)

★ 肺腺癌
★ 抗藥性
★ 外泌體

關鍵字(英)

★ Lung Adenocarcinoma
★ Resistant
★ exosome

論文目次

中文摘要 ..................................................................................................................................... I
ABSTRACT .............................................................................................................................. II
目錄 .......................................................................................................................................... III
圖目錄 ....................................................................................................................................... V
表目錄 ...................................................................................................................................... VI
一、緒論 .................................................................................................................................... 1
1-1 肺腺癌 ............................................................................................................................. 1
1-2 肺腺癌治療方法 ............................................................................................................. 1
1-3 基因表現量 ..................................................................................................................... 2
1-4 外泌體 ............................................................................................................................. 2
1-5 研究動機與目的 ............................................................................................................. 3
二、材料與方法 ........................................................................................................................ 4
2-1 資料來源 ......................................................................................................................... 4
2-2 資料分析 ......................................................................................................................... 5
2-2-1 分析環境與套件 ..................................................................................................... 5
2-2-2 資料前處理 ............................................................................................................. 5
2-2-3 超幾何檢定(富集分析) ........................................................................................... 6
2-2-4 MIENTURNET 富集分析 ....................................................................................... 6
2-2-5 KEGG 途徑 .............................................................................................................. 7
2-2-6 KEA: kinase enrichment analysis ............................................................................. 7
三、結果 ................................................................................................................................... 9
3-1 基因篩選 ......................................................................................................................... 9
3-2 外泌體中基因產物的共同差異表達基因 ................................................................... 11
3-3 KEGG PATHWAY 富集分析 ........................................................................................ 14
3-4 細胞和外泌體中RNA 的表達差異 ............................................................................ 20
3-5 KEA（激酶富集分析） ............................................................................................... 22
3-6 與T790M 突變的非小細胞肺癌外泌體比較 ............................................................. 25
四、討論與結論 ...................................................................................................................... 27
4-1 討論 ............................................................................................................................... 27
4-1-1 基因篩選 ............................................................................................................... 27
4-1-2 外泌體中基因產物的共同差異表達基因 ........................................................... 27
4-1-3 KEGG 途徑富集分析 ............................................................................................ 27
4-1-4 RNA 在外泌體和細胞間表現量的差異 ............................................................... 28
4-1-5 KEA 富集分析 ....................................................................................................... 28
4-1-6 與T790M 突變的非小細胞肺癌外泌體比較 ..................................................... 29
4-2 結論 ............................................................................................................................... 31
五、參考文獻 .......................................................................................................................... 32

參考文獻

1. Siegel, R.L., K.D. Miller, and A. Jemal, Cancer statistics, 2016. CA: a cancer journal for
clinicians, 2016. 66(1): p. 7-30.
2. 衛生福利部, 107年國人死因統計結果. 2019.
3. Travis, W.D., et al., Introduction to the 2015 World Health Organization classification of
tumors of the lung, pleura, thymus, and heart. Journal of Thoracic Oncology, 2015. 10(9): p.
1240-1242.
4. Paris, C., et al., Relationships between lung adenocarcinoma and gender, age, smoking and
occupational risk factors: a case–case study. Lung Cancer, 2010. 68(2): p. 146-153.
5. Xiao, D., et al., Analysis of ultra-deep targeted sequencing reveals mutation burden is
associated with gender and clinical outcome in lung adenocarcinoma. Oncotarget, 2016.
7(16): p. 22857.
6. Agarwal, V., et al., Predicting effective microRNA target sites in mammalian mRNAs. elife,
2015. 4: p. e05005.
7. Midha, A., S. Dearden, and R. McCormack, EGFR mutation incidence in non-small-cell lung
cancer of adenocarcinoma histology: a systematic review and global map by ethnicity
(mutMapII). American journal of cancer research, 2015. 5(9): p. 2892.
8. Koehler, J. and M. Schuler, Afatinib, erlotinib and gefitinib in the first-line therapy of EGFR
mutation-positive lung adenocarcinoma: a review. Oncology Research and Treatment, 2013.
36(9): p. 510-518.
9. Ohashi, K., et al., Epidermal growth factor receptor tyrosine kinase inhibitor–resistant
disease. Journal of Clinical Oncology, 2013. 31(8): p. 1070.
10. Helena, A.Y., et al., Analysis of tumor specimens at the time of acquired resistance to EGFRTKI
therapy in 155 patients with EGFR-mutant lung cancers. Clinical cancer research, 2013.
19(8): p. 2240-2247.
11. Maglott, D., et al., Entrez Gene: gene-centered information at NCBI. Nucleic acids research,
2010. 39(suppl_1): p. D52-D57.
12. Keller, S., et al., Exosomes: from biogenesis and secretion to biological function. Immunology
letters, 2006. 107(2): p. 102-108.
13. Pan, B.-T. and R.M. Johnstone, Fate of the transferrin receptor during maturation of sheep
reticulocytes in vitro: selective externalization of the receptor. Cell, 1983. 33(3): p. 967-978.
14. Chiba, M., M. Kimura, and S. Asari, Exosomes secreted from human colorectal cancer cell
lines contain mRNAs, microRNAs and natural antisense RNAs, that can transfer into the
human hepatoma HepG2 and lung cancer A549 cell lines. Oncology reports, 2012. 28(5): p.
1551-1558.
15. Choi, D.Y., et al., Extracellular vesicles shed from gefitinib‐resistant nonsmall cell lung
cancer regulate the tumor microenvironment. Proteomics, 2014. 14(16): p. 1845-1856.
16. Li, B., et al., MiR-21 overexpression is associated with acquired resistance of EGFR-TKI in
non-small cell lung cancer. Lung cancer, 2014. 83(2): p. 146-153.
17. Gentleman, R., R programming for bioinformatics. 2008: CRC Press.
18. Carvalho, B.S. and R.A. Irizarry, A framework for oligonucleotide microarray preprocessing.
Bioinformatics, 2010. 26(19): p. 2363-2367.
19. Yu, G., et al., clusterProfiler: an R package for comparing biological themes among gene
clusters. Omics: a journal of integrative biology, 2012. 16(5): p. 284-287.
20. Mortazavi, A., et al., Mapping and quantifying mammalian transcriptomes by RNA-Seq.
Nature methods, 2008. 5(7): p. 621-628.
21. Irizarry, R.A., et al., Exploration, normalization, and summaries of high density
oligonucleotide array probe level data. Biostatistics, 2003. 4(2): p. 249-264.
22. Hsu, S.-D., et al., miRTarBase: a database curates experimentally validated microRNA–target
interactions. Nucleic acids research, 2011. 39(suppl_1): p. D163-D169.
23. Licursi, V., et al., MIENTURNET: an interactive web tool for microRNA-target enrichment
and network-based analysis. BMC bioinformatics, 2019. 20(1): p. 1-10.
24. Ogata, H., et al., KEGG: Kyoto encyclopedia of genes and genomes. Nucleic acids research,
1999. 27(1): p. 29-34.
25. Huang, D.W., et al., DAVID Bioinformatics Resources: expanded annotation database and
novel algorithms to better extract biology from large gene lists. Nucleic acids research, 2007.
35(suppl_2): p. W169-W175.
26. Lachmann, A. and A. Ma′ayan, KEA: kinase enrichment analysis. Bioinformatics, 2009.
25(5): p. 684-686.
27. Kuleshov, M.V., et al., Enrichr: a comprehensive gene set enrichment analysis web server
2016 update. Nucleic acids research, 2016. 44(W1): p. W90-W97.
28. Manning, G., et al., The protein kinase complement of the human genome. Science, 2002.
298(5600): p. 1912-1934.
29. Eid, S., et al., KinMap: a web-based tool for interactive navigation through human kinome
data. BMC bioinformatics, 2017. 18(1): p. 16.
30. Manabe, T., et al., IGF2 autocrine-mediated IGF1R activation is a clinically relevant
mechanism of osimertinib resistance in lung cancer. Molecular Cancer Research, 2020. 18(4):
p. 549-559.
31. Sun, Z., et al., NVP-BEZ235 overcomes gefitinib-acquired resistance by down-regulating
PI3K/AKT/mTOR phosphorylation. OncoTargets and therapy, 2015. 8: p. 269.
32. Vaquero, J., et al., The IGF2/IR/IGF1R pathway in tumor cells and myofibroblasts mediates
resistance to EGFR inhibition in cholangiocarcinoma. Clinical Cancer Research, 2018.
24(17): p. 4282-4296.
33. Moorehead, R.A., et al., Transgenic overexpression of IGF-II induces spontaneous lung
tumors: a model for human lung adenocarcinoma. Oncogene, 2003. 22(6): p. 853-857.

指導教授

許藝瓊(Yi-Chiung Hsu)

審核日期

2020-8-24

推文