大腸癌細胞株之 EGFR—K-ras 訊號路徑的基因微陣列實驗
與化學基因體學分析

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：24

、訪客IP：18.223.125.226

姓名

楊庭(Ting Yang) 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

大腸癌細胞株之 EGFR—K-ras 訊號路徑的基因微陣列實驗與化學基因體學分析
(Analysis of EGFR—K-ras pathways through microarrayconjunction with chemical genomics in colon cancer cell lines )

相關論文

★ 發展酵素非限制性全基因體調控因子解析方法	★ 小鼠胚胎幹細胞株之建立及人類誘導多能性幹細胞之培養技術
★ 由神經生長因子誘導之細胞內訊號路徑活化的化學基因體學分析	★ 細胞週期蛋白D1 mRNA在小鼠胚胎及成體幹細胞和腫瘤細胞中的表現及其受多能性相關因子影響之探討
★ 運用時間序列微陣列資料來預測調控基因	★ 以大鼠嗜鉻性瘤細胞株建立神經訊號傳遞之細胞分子生物學模型
★ 運用高通量基因微矩陣列方法解析由嗜鉻細胞分化成神經細胞之全基因體的調控	★ 神經生長因子在神經分化中轉錄因子活性及基因調控機制之橫觀
★ 以CRSBP-1接合子調控巨噬細胞的移動及吞噬	★ Chemogenomic and Molecular Analysis of Signal Transduction Pathways in In Vivo and In Vitro Models
★ 探討人類子宮內膜 L-selectin ligands 在月經週期的表現

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)

了解和分析複雜的基因調控-表達-回饋的過程，必頇以系統生物學觀念，藉由運用高通量檢
測及資訊分析等方法來完成。微陣列實驗及其高通量檢測技術興起，為系統生物學奠定了
一個重要的里程碑。
本研究利用外顯子微陣列晶片(Exon array)高通量基因分析實驗，篩選 Sw480、Caco2、Ht29
細胞株經由 EGF 刺激和 Cetuximab 阻斷後，對於 EGFR 下游外顯子基因的啟動或其它路徑
的變化基因。利用 SAM 的生物晶片顯著分析方法，篩選出 3052 個有顯著意義變化的基因，
並做基因功能分類以及經 KegArray 軟體處理對應到 KEGG 路徑的列表，找出各細胞株之
間相對的特異性。再利用 R 套件畫出熱相圖並用歐氏距離和 Pearson 相關係數計算出每株
細胞控制組和實驗組之相似性。
經由 KegArray 聯結 KEGG 生物訊息路徑資料初步比對後，發現 Sw480、 Ht29 、Caco2
細胞株經 Cetuximab 阻斷 EGFR 之後於 EGFR-Ras 路徑的差異性以及不同下游子生物路徑
的改變情形。
未來在我們研究當中，希望可以針對大腸直腸癌細胞經 Cetuximab 阻斷 EGFR-Ras 路徑後
之整體訊息路徑變化以及相關關鍵基因進行分析，並觀看其各子路徑中的基因變化。希望
可以藉由系統生物學的概念整合出關鍵基因在不同的生物路徑中複雜的關係與交互作用，
並探討基因分子訊息路徑與上下游基因之調控機制。

摘要(英)

In order to understand the complexity of regulation, expression and feedback mechanism of
genes, the methodology of system biology through the high-throughput analysis and information
analysis must be utilized to elucidate the gene regulation network. The advance in microarray
experiment and its high throughput analysis has set up an important milestone in the system
biology.
In this study, we used the exon array and high throughput data analysis software developed
by Partek Corporation to observe the change of EGFR downstream targets after EGF and
Cetuximab blockade in Sw480, Caco2 and Ht29 cells. By using SAM biochip significance
analysis method, 3052 significantly different genes were selected. These genes were grouped by
different functions and relativity between different cell lines by correlating these genes with
KEGG mapping .We analyze the heat map of each cell line before and after stimulation by using
R package. We also compare the difference of control and experiment group and similarity
between other two groups by using Euclidean distance and Pearson correlation.
Using KegArray in conjunction with KEGG biological information pathway data，we found
the different downstream pathway transformation.of the Sw480、 Ht29 、Caco2 cell line after
treatment with Cetuximab to block EGFR and EGFR-Ras pathway.
In the furture, we hope to detect different gene mutation after treated with Cetuximab using
colon cancer cell lines and observe gene alteration in various EGFR-Ras pathway. We want to
integrate complex gene interaction by using system biology. It is valuable to study and
investigate the regulation mechanism of upstream and downstream interaction in a pathway.

關鍵字(中)

★ 大腸直腸癌細胞
★ 生物路徑

關鍵字(英)

★ EGFR
★ KEGG
★ K-ras

論文目次

v
目錄
中文摘要 ..................................................................................................................... i
Abstract ...................................................................................................................... ii
致謝 ............................................................................................................... iii
中英專有名詞對照表 ............................................................................................... iv
圖目錄 ....................................................................................................................... vi
第一章緒論 ......................................................................................................... 1
1-1 前言 ........................................................................................................... 1
1-2 生物路徑重要性與意義 ........................................................................... 1
1-3 目前常用觀察生物路徑之工具................................................................... 2
1-3-1 生物資料庫 ....................................................................................... 2
1-3-2 生物資料庫應用之軟體 .................................................................... 3
1-3-3 KEGG 和 KegArray ........................................................................... 3
1-4 本實驗所採用之細胞訊號傳遞模式 ....................................................... 4
1-4-1 大腸直腸癌與 EGFR、K-ras 基因突變 ........................................... 4
第二章實驗材料與方法 .......................................................................................... 7
2-1 藥品與材料 .................................................................................................. 7
2-1-1 儀器.................................................................................................... 7
2-2 大腸直腸癌細胞株 SW480、 CACO2 、HT29 之培養與處理 ............. 8
2-3 K-RAS 突變檢測 .......................................................................................... 9
2-3-1 DNA 萃取 ........................................................................................... 9
2-3-2 聚合酶連鎖反應 ................................................................................ 9
2-3-3 K-ras 突變基因檢測 .......................................................................... 9
2-4 外顯子晶片實驗設計與流程 ..................................................................... 10
2-4-1 RNA 樣品準備 ................................................................................. 10
2-4-2 Ribosome RNA 移除 ........................................................................ 11
2-4-3 cDNA 反轉錄 ................................................................................... 11
2-4-4 cDNA 片段化 ................................................................................... 11
2-4-5 單股 cDNA 標識(lable) ................................................................... 12
2-5 資料分析 ................................................................................................. 12
2-5-1 晶片輸出資料之初階分析 ............................................................. 12
2-5-2 生物晶片顯著性分析(Significance analysis of microarray(SAM))
.................................................................................................................... 13
2-5-3 叢集(clustering) ................................................................................ 14
2-5-4 基因表現於 KEGG 資料庫網路分析 ............................................. 14
vi
第三章結果 ...................................................................................................... 15
3-1 細胞生長情形與刺激後結果.................................................................... 15
3-2 EGFR 染色體基因於各細胞株的表現 ..................................................... 15
3-3 生物晶片分析結果 ..................................................................................... 15
3-3-1 晶片結果的初階評估 ...................................................................... 15
3-3-2 晶片資料叢集分析結果 ................................................................. 15
3-3-3 各細胞株特異性的叢集與參與重要路徑 ..................................... 16
第四章討論 .................................................................................................... 17
4-1 三株細胞對 Cetuximab 阻斷後型態變化 ................................................ 17
4-2 EGFR 染色體基因定量 ............................................................................. 17
4-3 生物晶片分析結果及生物意義................................................................. 18
第五章結論 .......................................................................................................... 20
Reference .................................................................................................................. 21

參考文獻

21
Reference
1. Amanda B. Spurdle, et al,. A Systematic Approach to Analysing Gene-Gene Interactions:
Polymorphisms at the Microsomal Epoxide Hydrolase EPHX and Glutathione S-transferase
GSTM1, GSTT1,and GSTP1 Loci and Breast Cancer Risk. Cancer Epidemiol Biomarkers Prev,
2007. 16(4).
2. Laurent Briollais, et al,. Methodological issues in detecting gene-gene interactions in
breast cancer susceptibility: a population-based study in Ontario. BMC Medicine 2007.
5(22): p. 1-15.
3. Kanehisa, M., Toward Pathway Engineering:A New Database of Genetic and Molecular
Pathways, in Institute for Chemical Research, Kyoto University.
4. 楊永正, 未來的生物資學. 科學發展. Vol. 396 期. 2005.
5. Michael P. Cary, et al,. Pathway information for systems biology. FEBS 2005: p.
1815–1820.
6. Wheeler DL, et al,. Database resources of the National Center for Biotechnology
Information. Nucleic Acids Research, 2000 28(1): p. 39-45.
7. Martijn P van Iersel, et al,. Presenting and exploring biological pathways with PathVisio.
BMC Bioinformatics, 2008. 9(399): p. 1-9.
8. G. Joshi-Tope, et al,. Reactome: a knowledgebase of biological pathways. Nucleic Acids
Research, 2005. 33: p. D428-432.
9. Kurt W. Kohn, et al,. Molecular Interaction Maps of Bioregulatory Networks:A General
Rubric for Systems Biology. Molecular Biology of the Cell, 2006. 17: p. 1-13.
10. Ziv Bar-Josdeph, et al,. Computation discovery of gene modules ad regulatory networks.
nature biotechnology, 2003: p. 1-7.
11. Paul Shannon, et al,. Cytoscape: A Software Environment for Integrated Models of
Biomolecular Interaction Networks. Genome Research, 2003. 13: p. 2498-2504
12. Virginia Goss Tusher , et al,. Significance analysis of microarrays applied to the ionizing
radiation response. PNAS, 2001. 98(9 ): p. 5116–5121.
13. Minoru Kanehisa, et al,. From genomics to chemical genomics: new developments in
KEGG. Nucleic Acids Research, 2005. 34: p. D354-357.
14. Minoru Kanehisa, et al,. Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids
Research 2000. 28(1): p. 27-30.
15. Minoru Kanehisa, et al,. KEGG for representation and analysis of molecular networks
involving diseases and drugs. Nucleic Acids Research, 2010. 38: p. 355-360.
16. Thorbergur Ho«gnason, et al,. Epidermal growth factor receptor induced
apoptosis:potentiation by inhibition of Ras signaling. FEBS Letters, 2001. 491 p. 9-15.
17. Laura K. Shawver, D.S.a.A.U., Smart drugs:Tyrosine kinase inhibitors in cancer therapy.
CANCER CELL, 2002. 1: p. 117-123.
18. Nicola Personeni, et al,. Clinical Usefulness of EGFR Gene Copy Number as a Predictive
Marker in Colorectal Cancer PatientsTreated with Cetuximab:A Fluorescent In situ
Hybridization Study. Clin Cancer Res, 2008. 14(18).
19. Wade S. Samowitz, et al,. Relationship of Ki-ras Mutations in Colon Cancers to Tumor
Location,Stage, and Survival:A Population-based Study. Cancer Epidemiology, 2000. 9( ):
22
p. p1193-1197.
20. Takashi Nishikawa, et al,. A simple method of detecting K-ras point mutations in stool
samples for colorectal cancer screening using one-step polymerase chain
reaction/restriction fragment length olymorphism analysis. Clinica Chimica Acta 2002.
318: p. p107-112.
21. V.Bazan , et al,. Specific codon 13 K-ras mutations are predictive of clinical outcome in
colorectal cancer patient,whereas codon 12 K-ras mutation are associated with mucinous
histotype. Annals of Oncology, 2002. 13: p. 1438-1446.
22. Federico A. Monzon, et al.,, The Role of KRAS Mutation Testing in the Management of
Patients With Metastatic Colorectal Cancer. Arch Pathol Lab Med, 2009. 133: p.
p.1600-1606.
23. Bos, J.L., ras oncogenes in human cancer: a review. Cancer Res., 1989.
49(17): p. p4682–4689.
24. Mirian Brink, et al,. K-ras oncogene mutations in sporadic colorectal cancer in The
Netherlands Cohort Study. Carcinogenesis, 2003. 24: p. p703-710.
25. Astrid Lie`vre, et al.,, KRAS mutation status is predictive of responce to cetuximab therapy
in colorectal cancer. Aacrjournal,, 2006. 66(8): p. p.3992-3995.
26. H. Jervoise N. Andreyev, et al,. Kirsten ras Mutations in Patients With Colorectal Cancer:
the Multicenter ``RASCAL' Study. Journal of the National Cancer Institute, 1998. 9: p.
p675-684.
27. Suzanne Schubbert, et al,. Hyperactive Ras in developmental disorders and cancer.
Nature Reviews Cancer, 2007. 7 p. p295-308.
28. Pellicer, M.M.a.A., RAS PATHWAYS TO CELL CYCLE CONTROL AND CELL TRANSFORMATION.
Frontiers in Bioscience, 98: p. p887-912.
29. Gil Chu, et al,. SAM “Significance Analysis of Microarrays”.
30. Virginia Goss Tusher , e.a., . Significance analysis of microarrays. 2008.
31. K. L. Woodford-Richens, et al,. SMAD4 mutations in colorectal cancer probably occur
before chromosomal instability, but after divergence of the microsatellite instability
pathway. PNAS Early Edition, 2001 98( 17 ): p. 9719±9723.
32. SANCHO, E., Molecular mechanisms involved in the initiation and progression of
colorectal cancer. Scientific Report Oncology Programme, 2008 p. 132-35.
33. Thomas J. Lynch, M.D., ,et al,. Activating Mutations in the Epidermal Growth Factor
Receptor Underlying Responsiveness of Non–Small-Cell Lung Cancer to Gefitinib. N Engl J
Med, 2004. 350: p. 2129-39.
34. Napoleone Ferrara, H.-P.G.J.L., The biology of VEGF and its receptors. NATURE MEDICINE
2003. 9(6): p. 669-676.
35. Nabendu Pore, et al,. EGFR Tyrosine Kinase Inhibitors Decrease VEGF Expression by Both
Hypoxia-Inducible Factor (HIF)-1–Independent and HIF-1–Dependent Mechanisms.
Cancer Res, 2006. 66(6).
36. F Chang, et al,. Involvement of PI3K/Akt pathway in cell cycle progression, apoptosis, and
neoplastic transformation: a target for cancer chemotherapy. Leukemia 2003. 17: p.
590-603.
37. Morgan, D.M.L., Tetrazolium (MTT) Assay for Cellular Viability and Activity. Vol. 79. 1998.
p179-184.
23
38. Minaxi Jhawer, et al,. PIK3CA Mutation/PTEN Expression Status Predicts Response of
Colon Cancer Cells to the Epidermal Growth Factor Receptor Inhibitor Cetuximab.
Cancer Res, 2008;. 68(6): p. p1953-61.
39. Rossella Solmi, et al,. Displayed correlation between gene expression profiles and
submicroscopic alterations in response to cetuximab, gefitinib and EGF in human colon
cancer cell lines. BMC Cancer, 2008. 8(227 ).
40. Alberto Bardelli, et al,. Mutational Analysis of the Tyrosine Kinome in Colorectal Cancers.
SCIENCE 2003. 300.
41. Susumu Kobayashi, M.D., ,et al,. EGFR Mutation and Resistance of Non–Small-Cell Lung
Cancer to Gefitinib. N Engl J Med, 2005. 352: p. 786-92.
42. Mauro Moroni, et al,. Gene copy number for epidermal growth factor receptor (EGFR)
and clinical response to antiEGFR treatment in colorectal cancer: a cohort study. Lancet
Oncol, 2005. 6: p. p279-286.
43. Thomas J. Lynch, M.D., ,et al,. Activating Mutations in the Epidermal Growth Factor
Receptor Underlying Responsiveness of Non–Small-Cell Lung Cancer to Gefitinib. N Engl J
Med 2004. 350: p. 2129-39.
44. Rafael Rosell, et al,. Epidermal Growth Factor Receptor Activation: How Exon 19 and 21
Mutations Changed Our Understanding of the Pathway. Clin Cancer Res, 2006. 12(24).
45. Liu, Z., Hetero-stagger cloning: efficient and rapid cloning of PCR products. Nucleic Acids
Research, 1996. 24(12): p. 2458-59.
46. Svetlana Baranovskaya, et al,. Down-regulation of Epidermal Growth Factor Receptor by
Selective Expansion of a 5-End Regulatory Dinucleotide Repeat in Colon Cancer
withMicrosatellite Instability. Clin Cancer Res, 2009. 15(14): p. 4531-37.
47. Chun-Hau Chen, et al,. Bidirectional signals transduced by DAPK–ERK interaction promote
the apoptotic effect of DAPK. The EMBO Journal 2005. 24: p. 294-304.
48. Jing Yuan Fang, B.C.R., The MAPK signalling pathways and colorectal cancer. Lancet Oncol,
2005. 6 p. 322-27.
49. Ziqiang Yuan, et al,. An A13 Repeat within the 3-Untranslated Region of Epidermal
Growth Factor Receptor (EGFR) Is Frequently Mutated in Microsatellite Instability Colon
Cancers and Is Associated with Increased EGFR Expression. Cancer Res 2009. 69(19).
50. Hillier, L.W., The DNA sequence of human chromosome 7. NATURE 2003 424 p. 157-63.
51. Poulikos I. Poulikakos, et al,. RAF inhibitors transactivate RAF dimers and ERK signalling in
cells with wild-type BRAF. nature biotechnology, 2010. 464: p. 427-30.
52. RAMESH M. RAY, S.A.M.C.C., AND LEONARD R. JOHNSON, Polyamine depletion arrests
growth of IEC-6 and Caco-2 cells by different mechanisms. Am J Physiol Gastrointest Liver
Physiol 2001. 281: p. 37-43.
53. Silvia Fre, et al,. Notch and Wnt signals cooperatively control cell proliferation and
tumorigenesis in the intestine. PNAS Early Edition, 2009: p. 1-6.
54. Spyros Artavanis-Tsakonas, M.D.R., Robert J. Lake, Notch Signaling: Cell Fate Control and
Signal Integration in Development. SCIENCE 1999. 284
55. Kopan, R., Notch: a membrane bound transcription factor. Journal of Cell Science, 2002.
115: p. 1095-1097.
56. Bruce M. Boman, et al,. Computer Modeling Implicates Stem Cell Overproduction in Colon
Cancer Initiation. CANCER RESEARCH, 2001. 61: p. 8408-11.
24
57. Greenwood, E., Survivin cell death. NATURE REVIEWS | CANCER, 2001. 1.
58. Trevor J Pugh, et al,. Correlations of EGFR mutations and increases in EGFR and HER2
copy number to gefitinib response in a retrospective analysis of lung cancer patients BMC
Cancer, 2007. 7(128).
59. Andrea Sartore-Bianchi, et al,. Epidermal Growth Factor Receptor Gene Copy Number and
Clinical Outcome of Metastatic Colorectal Cancer Treated With Panitumumab Clinical
Oncology, 2007 25: p. 3238-3245.
60. Sanja Dacic, et al,. Significance of EGFR Protein Expression and Gene Amplification in
Non–Small Cell Lung Carcinoma. Am J Clin Pathol, 2006. 125: p. 860-865.
61. MATILDE OLIVE, et al,. Characterization of the DiFi rectal carcinoma cell line derived
from a familial adenomatous polyposis patient. In Vitro Cell. Dev. Biol, 1993. 29A: p.
239-248.

指導教授

凌慶東(Qing-Dong Ling)

審核日期

2010-7-15

推文