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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林文澧(Win-Li Lin) | |
dc.contributor.author | Hsiao-Wei Chen | en |
dc.contributor.author | 陳筱瑋 | zh_TW |
dc.date.accessioned | 2021-06-13T00:07:22Z | - |
dc.date.available | 2012-07-30 | |
dc.date.copyright | 2007-07-30 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-28 | |
dc.identifier.citation | 1. Brinton L LJ, Devesa SS: Epidemiology of breast cancer In: Cancer of the Breast, 5th ed Edited by Donegan WL, Spratt JS Philadelphia: WB Saunders 2002:111-132.
2. Hsiao WC, Young KC, Lin SL, Lin PW: Estrogen receptor-alpha polymorphism in a Taiwanese clinical breast cancer population: a case-control study. Breast Cancer Res 2004, 6(3):R180-186. 3. Deroo BJ, Korach KS: Estrogen receptors and human disease. J Clin Invest 2006, 116(3):561-570. 4. Holst F, Stahl PR, Ruiz C, Hellwinkel O, Jehan Z, Wendland M, Lebeau A, Terracciano L, Al-Kuraya K, Janicke F et al: Estrogen receptor alpha (ESR1) gene amplification is frequent in breast cancer. Nature genetics 2007, 39(5):655-660. 5. Gold B, Kalush F, Bergeron J, Scott K, Mitra N, Wilson K, Ellis N, Huang H, Chen M, Lippert R et al: Estrogen receptor genotypes and haplotypes associated with breast cancer risk. Cancer Res 2004, 64(24):8891-8900. 6. Feigelson HS, Cox DG, Cann HM, Wacholder S, Kaaks R, Henderson BE, Albanes D, Altshuler D, Berglund G, Berrino F et al: Haplotype analysis of the HSD17B1 gene and risk of breast cancer: a comprehensive approach to multicenter analyses of prospective cohort studies. Cancer Res 2006, 66(4):2468-2475. 7. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS et al: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001, 98(19):10869-10874. 8. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 2003, 100(18):10393-10398. 9. Gruvberger S RM, Chen Y, Panavally S, Saal LH, Borg A, et al.: Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Research 2001, 61:5979-5984. 10. Deena Damsky Dell R, AOCN, BC, MSN: Spread the world about breast Nursing 2005 2005, 35(10):56-63. 11. Breast cancer. In.: Wikimedia Foundation, Inc. 12. Rusiecki JA, Holford TR, Zahm SH, Zheng T: Breast cancer risk factors according to joint estrogen receptor and progesterone receptor status. Cancer detection and prevention 2005, 29(5):419-426. 13. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL: Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science (New York, NY 1987, 235(4785):177-182. 14. Osborne CK: Steroid hormone receptors in breast cancer management. Breast Cancer Res Treat 1998, 51:227-238. 15. Rachel Ann Clark SS, Carol Devine: Estrogen & Breast Cancer Risk: The Relationship. In. 16. What Role Do Hormones Play in Breast Cancer Treatment? In. 17. DNA microarray. In.: Wikimedia Foundation, Inc. 18. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science (New York, NY 1995, 270(5235):467-470. 19. Draghici S: Data Analysis Tools For DNA Microarrays: Chapman & Hall/CRC; 2003. 20. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001, 98(9):5116-5121. 21. Single nucleotide polymorphism. In.: Wikimedia Foundation, Inc. 22. Greg Gibson SVM: A Primer of Genome Science, Second edition edn. Sunderland: Sinauer Associates, Inc. ; 2004. 23. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M et al: TM4: a free, open-source system for microarray data management and analysis. BioTechniques 2003, 34(2):374-378. 24. Leek JT, Monsen E, Dabney AR, Storey JD: EDGE: extraction and analysis of differential gene expression. Bioinformatics (Oxford, England) 2006, 22(4):507-508. 25. Storey JD: The Optimal Discovery Procedure: A New Approach to Simultaneous Significance Testing. UW Biostatistics Working Paper Series 2005:Working Paper 259. . 26. Storey JD, Dai JY, Leek JT: The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics (Oxford, England) 2007, 8(2):414-432. 27. Tsuchiya Y, Nakajima M, Takagi S, Taniya T, Yokoi T: MiRNAs regulates the expression of human cytochrome P450 1B1. Cancer Res 2006, 66(18):9090-9098. 28. Storey JD: The Optimal discovery precedure : a new approach to simultaneous significance testing In: UW Biostatistics working paper series working paper. 2005: 259. 29. Amlal H, Faroqui S, Balasubramaniam A, Sheriff S: Estrogen up-regulates neuropeptide Y Y1 receptor expression in a human breast cancer cell line. Cancer Res 2006, 66(7):3706-3714. 30. Ruscica M, Dozio E, Boghossian S, Bovo G, Martos Riano V, Motta M, Magni P: Activation of the Y1 receptor by neuropeptide Y regulates the growth of prostate cancer cells. Endocrinology 2006, 147(3):1466-1473. 31. Kuang WW, Thompson DA, Hoch RV, Weigel RJ: Differential screening and suppression subtractive hybridization identified genes differentially expressed in an estrogen receptor-positive breast carcinoma cell line. Nucleic Acids Res 1998, 26(4):1116-1123. 32. Roodi N, Bailey LR, Kao WY, Verrier CS, Yee CJ, Dupont WD, Parl FF: Estrogen receptor gene analysis in estrogen receptor-positive and receptor-negative primary breast cancer. J Natl Cancer Inst 1995, 87(6):446-451. 33. Iwase H GJ, Barnes DM, Hodgson S, Bobrow L, Mathew CG: Sequence variants of the estrogen receptor (ER) gene found in breast cancer patients with ER negative and progesterone receptor positive tumors. . Cancer Lett 1996, 108:179-184. 34. Kang HJ, Kim SW, Kim HJ, Ahn SJ, Bae JY, Park SK, Kang D, Hirvonen A, Choe KJ, Noh DY: Polymorphisms in the estrogen receptor-alpha gene and breast cancer risk. Cancer Lett 2002, 178(2):175-180. 35. Murphy LC, Dotzlaw H, Leygue E, Douglas D, Coutts A, Watson PH: Estrogen receptor variants and mutations. J Steroid Biochem Mol Biol 1997, 62(5-6):363-372. 36. Hu Z, Shao M, Yuan J, Xu L, Wang F, Wang Y, Yuan W, Qian J, Ma H, Wang Y et al: Polymorphisms in DNA damage binding protein 2 (DDB2) and susceptibility of primary lung cancer in the Chinese: a case-control study. Carcinogenesis 2006, 27(7):1475-1480. 37. Adjaye J, Huntriss J, Herwig R, BenKahla A, Brink TC, Wierling C, Hultschig C, Groth D, Yaspo ML, Picton HM et al: Primary differentiation in the human blastocyst: comparative molecular portraits of inner cell mass and trophectoderm cells. Stem Cells 2005, 23(10):1514-1525. 38. Sun J, Wiklund F, Hsu FC, Balter K, Zheng SL, Johansson JE, Chang B, Liu W, Li T, Turner AR et al: Interactions of sequence variants in interleukin-1 receptor-associated kinase4 and the toll-like receptor 6-1-10 gene cluster increase prostate cancer risk. Cancer Epidemiol Biomarkers Prev 2006, 15(3):480-485. 39. Cao H, Hegele RA: Identification of single-nucleotide polymorphisms in the human LPIN1 gene. Journal of human genetics 2002, 47(7):370-372. 40. Mazal PR, Stichenwirth M, Koller A, Blach S, Haitel A, Susani M: Expression of aquaporins and PAX-2 compared to CD10 and cytokeratin 7 in renal neoplasms: a tissue microarray study. Mod Pathol 2005, 18(4):535-540. 41. Chen ZM, Wang HL: Alteration of cytokeratin 7 and cytokeratin 20 expression profile is uniquely associated with tumorigenesis of primary adenocarcinoma of the small intestine. Am J Surg Pathol 2004, 28(10):1352-1359. 42. Koike T, Kimura N, Miyazaki K, Yabuta T, Kumamoto K, Takenoshita S, Chen J, Kobayashi M, Hosokawa M, Taniguchi A et al: Hypoxia induces adhesion molecules on cancer cells: A missing link between Warburg effect and induction of selectin-ligand carbohydrates. Proc Natl Acad Sci U S A 2004, 101(21):8132-8137. 43. Frasor J, Danes JM, Funk CC, Katzenellenbogen BS: Estrogen down-regulation of the corepressor N-CoR: Mechanism and implications for estrogen derepression of N-CoR-regulated genes. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(37):13153-13157. 44. Kawakubo H, Brachtel E, Hayashida T, Yeo G, Kish J, Muzikansky A, Walden PD, Maheswaran S: Loss of B-cell translocation gene-2 in estrogen receptor-positive breast carcinoma is associated with tumor grade and overexpression of cyclin d1 protein. Cancer Res 2006, 66(14):7075-7082. 45. Gay F, Anglade I, Gong ZY, Salbert G: The LIM/Homeodomain protein islet-1 modulates estrogen receptor functions. Molecular Endocrinology 2000, 14(10):1627-1648. 46. Rushmere NK, Knowlden JM, Gee JMW, Harper ME, Robertson JF, Morgan BP, Nicholson RI: Analysis of the level of mRNA expression of the membrane regulators of complement, Cd59, Cd55 and Cd46, in breast, cancer. International Journal of Cancer 2004, 108(6):930-936. 47. Lacroix M, Leclercq G: About GATA3, HNF3A, and XBP1, three genes co-expressed with the oestrogen receptor-alpha gene (ESRI) in breast cancer. Molecular and Cellular Endocrinology 2004, 219(1-2):1-7. 48. Oh DS, Troester MA, Usary J, Hu Z, He X, Fan C, Wu J, Carey LA, Perou CM: Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers. J Clin Oncol 2006, 24(11):1656-1664. 49. Doane AS, Danso M, Lal P, Donaton M, Zhang L, Hudis C, Gerald WL: An estrogen receptor-negative breast cancer subset characterized by a hormonally regulated transcriptional program and response to androgen. Oncogene 2006. 50. Milde-Langosch K, Bamberger AM, Rieck G, Kelp B, Loning T: Overexpression of the p16 cell cycle inhibitor in breast cancer is associated with a more malignant phenotype. Breast Cancer Research and Treatment 2001, 67(1):61-70. 51. Usary J, Llaca V, Karaca G, Presswala S, Karaca M, He X, Langerod A, Karesen R, Oh DS, Dressler LG et al: Mutation of GATA3 in human breast tumors. Oncogene 2004, 23(46):7669-7678. 1. Brinton L LJ, Devesa SS: Epidemiology of breast cancer In: Cancer of the Breast, 5th ed Edited by Donegan WL, Spratt JS Philadelphia: WB Saunders 2002:111-132. 2. Hsiao WC, Young KC, Lin SL, Lin PW: Estrogen receptor-alpha polymorphism in a Taiwanese clinical breast cancer population: a case-control study. Breast Cancer Res 2004, 6(3):R180-186. 3. Deroo BJ, Korach KS: Estrogen receptors and human disease. J Clin Invest 2006, 116(3):561-570. 4. Holst F, Stahl PR, Ruiz C, Hellwinkel O, Jehan Z, Wendland M, Lebeau A, Terracciano L, Al-Kuraya K, Janicke F et al: Estrogen receptor alpha (ESR1) gene amplification is frequent in breast cancer. Nature genetics 2007, 39(5):655-660. 5. Gold B, Kalush F, Bergeron J, Scott K, Mitra N, Wilson K, Ellis N, Huang H, Chen M, Lippert R et al: Estrogen receptor genotypes and haplotypes associated with breast cancer risk. Cancer Res 2004, 64(24):8891-8900. 6. Feigelson HS, Cox DG, Cann HM, Wacholder S, Kaaks R, Henderson BE, Albanes D, Altshuler D, Berglund G, Berrino F et al: Haplotype analysis of the HSD17B1 gene and risk of breast cancer: a comprehensive approach to multicenter analyses of prospective cohort studies. Cancer Res 2006, 66(4):2468-2475. 7. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS et al: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001, 98(19):10869-10874. 8. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 2003, 100(18):10393-10398. 9. Gruvberger S RM, Chen Y, Panavally S, Saal LH, Borg A, et al.: Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Research 2001, 61:5979-5984. 10. Deena Damsky Dell R, AOCN, BC, MSN: Spread the world about breast Nursing 2005 2005, 35(10):56-63. 11. Breast cancer. In.: Wikimedia Foundation, Inc. 12. Rusiecki JA, Holford TR, Zahm SH, Zheng T: Breast cancer risk factors according to joint estrogen receptor and progesterone receptor status. Cancer detection and prevention 2005, 29(5):419-426. 13. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL: Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science (New York, NY 1987, 235(4785):177-182. 14. Osborne CK: Steroid hormone receptors in breast cancer management. Breast Cancer Res Treat 1998, 51:227-238. 15. Rachel Ann Clark SS, Carol Devine: Estrogen & Breast Cancer Risk: The Relationship. In. 16. What Role Do Hormones Play in Breast Cancer Treatment? In. 17. DNA microarray. In.: Wikimedia Foundation, Inc. 18. Draghici S: Data Analysis Tools For DNA Microarrays: Chapman & Hall/CRC; 2003. 19. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science (New York, NY 1995, 270(5235):467-470. 20. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001, 98(9):5116-5121. 21. Single nucleotide polymorphism. In.: Wikimedia Foundation, Inc. 22. Greg Gibson SVM: A Primer of Genome Science, Second edition edn. Sunderland: Sinauer Associates, Inc. ; 2004. 23. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M et al: TM4: a free, open-source system for microarray data management and analysis. BioTechniques 2003, 34(2):374-378. 24. Leek JT, Monsen E, Dabney AR, Storey JD: EDGE: extraction and analysis of differential gene expression. Bioinformatics (Oxford, England) 2006, 22(4):507-508. 25. Storey JD: The Optimal Discovery Procedure: A New Approach to Simultaneous Significance Testing. UW Biostatistics Working Paper Series 2005:Working Paper 259. . 26. Storey JD, Dai JY, Leek JT: The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics (Oxford, England) 2007, 8(2):414-432. 27. Tsuchiya Y, Nakajima M, Takagi S, Taniya T, Yokoi T: MicroRNA regulates the expression of human cytochrome P450 1B1. Cancer Res 2006, 66(18):9090-9098. 28. Storey JD: The Optimal discovery precedure : a new approach to simultaneous significance testing In: UW Biostatistics working paper series working paper. 2005: 259. 29. Amlal H, Faroqui S, Balasubramaniam A, Sheriff S: Estrogen up-regulates neuropeptide Y Y1 receptor expression in a human breast cancer cell line. Cancer Res 2006, 66(7):3706-3714. 30. Ruscica M, Dozio E, Boghossian S, Bovo G, Martos Riano V, Motta M, Magni P: Activation of the Y1 receptor by neuropeptide Y regulates the growth of prostate cancer cells. Endocrinology 2006, 147(3):1466-1473. 31. Kuang WW, Thompson DA, Hoch RV, Weigel RJ: Differential screening and suppression subtractive hybridization identified genes differentially expressed in an estrogen receptor-positive breast carcinoma cell line. Nucleic Acids Res 1998, 26(4):1116-1123. 32. Roodi N, Bailey LR, Kao WY, Verrier CS, Yee CJ, Dupont WD, Parl FF: Estrogen receptor gene analysis in estrogen receptor-positive and receptor-negative primary breast cancer. J Natl Cancer Inst 1995, 87(6):446-451. 33. Iwase H GJ, Barnes DM, Hodgson S, Bobrow L, Mathew CG: Sequence variants of the estrogen receptor (ER) gene found in breast cancer patients with ER negative and progesterone receptor positive tumors. . Cancer Lett 1996, 108:179-184. 34. Kang HJ, Kim SW, Kim HJ, Ahn SJ, Bae JY, Park SK, Kang D, Hirvonen A, Choe KJ, Noh DY: Polymorphisms in the estrogen receptor-alpha gene and breast cancer risk. Cancer Lett 2002, 178(2):175-180. 35. Murphy LC, Dotzlaw H, Leygue E, Douglas D, Coutts A, Watson PH: Estrogen receptor variants and mutations. J Steroid Biochem Mol Biol 1997, 62(5-6):363-372. 36. Hu Z, Shao M, Yuan J, Xu L, Wang F, Wang Y, Yuan W, Qian J, Ma H, Wang Y et al: Polymorphisms in DNA damage binding protein 2 (DDB2) and susceptibility of primary lung cancer in the Chinese: a case-control study. Carcinogenesis 2006, 27(7):1475-1480. 37. Adjaye J, Huntriss J, Herwig R, BenKahla A, Brink TC, Wierling C, Hultschig C, Groth D, Yaspo ML, Picton HM et al: Primary differentiation in the human blastocyst: comparative molecular portraits of inner cell mass and trophectoderm cells. Stem Cells 2005, 23(10):1514-1525. 38. Sun J, Wiklund F, Hsu FC, Balter K, Zheng SL, Johansson JE, Chang B, Liu W, Li T, Turner AR et al: Interactions of sequence variants in interleukin-1 receptor-associated kinase4 and the toll-like receptor 6-1-10 gene cluster increase prostate cancer risk. Cancer Epidemiol Biomarkers Prev 2006, 15(3):480-485. 39. Cao H, Hegele RA: Identification of single-nucleotide polymorphisms in the human LPIN1 gene. Journal of human genetics 2002, 47(7):370-372. 40. Mazal PR, Stichenwirth M, Koller A, Blach S, Haitel A, Susani M: Expression of aquaporins and PAX-2 compared to CD10 and cytokeratin 7 in renal neoplasms: a tissue microarray study. Mod Pathol 2005, 18(4):535-540. 41. 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Kawakubo H, Brachtel E, Hayashida T, Yeo G, Kish J, Muzikansky A, Walden PD, Maheswaran S: Loss of B-cell translocation gene-2 in estrogen receptor-positive breast carcinoma is associated with tumor grade and overexpression of cyclin d1 protein. Cancer Res 2006, 66(14):7075-7082. 45. Gay F, Anglade I, Gong ZY, Salbert G: The LIM/Homeodomain protein islet-1 modulates estrogen receptor functions. Molecular Endocrinology 2000, 14(10):1627-1648. 46. Rushmere NK, Knowlden JM, Gee JMW, Harper ME, Robertson JF, Morgan BP, Nicholson RI: Analysis of the level of mRNA expression of the membrane regulators of complement, Cd59, Cd55 and Cd46, in breast, cancer. International Journal of Cancer 2004, 108(6):930-936. 47. Lacroix M, Leclercq G: About GATA3, HNF3A, and XBP1, three genes co-expressed with the oestrogen receptor-alpha gene (ESRI) in breast cancer. Molecular and Cellular Endocrinology 2004, 219(1-2):1-7. 48. 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May FE, Church ST, Major S, Westley BR: The closely related estrogen-regulated tr | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28405 | - |
dc.description.abstract | 隨著 cDNA 和 oligonucleotide 微陣列技術的進步,我們可以很快速得取得大量的基因表現數據。這些基因表現數據所建立出來的圖譜可以幫助我們了解癌症機制中基因調控的情形。在本次的研究中,我們分析了59位乳癌病患的基因表現數據並探討雌激素受體陽性和陰性的乳癌病患中,基因表現和單核苷酸多型性( Single nucleotide polymorphisms, 簡稱 SNPs ) 以及微型核醣核酸之間的關係。雌激素受體在乳癌的生長過程中扮演了很重要的角色。第一部分的研究中,我們分析比對了台灣地區59位乳癌病患和英國地區99位乳癌病患的基因表現圖譜。再利用已知的 SNP 資料庫內的資料對這67個基因進行統計分析,找到了17個基因,這17個基因內有一個以上的 SNPs 是在華人和高加索人種間具有顯著差異的。因此我們推斷可能因為這些特定人種內存在的 SNPs ,讓這17個基因的功能直接或是間接影響到不同人種內的雌激素受體調控機制。
微型核醣核酸( microRNAs,簡稱 miRNAs )是一種長度約只有 21-25個核苷酸的核醣核酸。它藉由抑制基因的表現進而影響到許多生物機制。在第二部分的研究中,我們建立出了一套有系統的研究方法,利用基因表現圖譜來加速發現 miRNAs 的功能,並且將 miRNAs 連結到可能參與的生物路徑上。我們發展了一個生物資訊的工具 – miLink 。它整合了 miRNAs ,整合了預測的 miRNAs 標的基因(target genes)資料以及生物路徑的資料庫,同時利用統計分析方法讓使用者可以篩選出可能具有影響的 miRNAs。我們將基因表現的分析結果應用到miLink上,並利用即時定量聚合酶連鎖反應( RT-QPCR ),針對幾組可能和 ER有關的 microRNAs 和標的基因進行定量。藉由分析 miRNAs 和其標的基因表現量之間的關係,我們發現了 miR-218 和其標的基因可能和雌激素受體的表現有關。我們的結果不僅可說明 miLink 在 miRNAs 研究上的實用性,也發現了一些可能和乳癌中雌激素受體調控有關的 miRNAs。 | zh_TW |
dc.description.abstract | With advanced cDNA and oligonucleotide microarray technique, large amounts of gene expression data can be obtained in a short period of time. Gene expression profiling is a powerful tool for identifying gene activity patterns and discovering pathological mechanisms in cancers. In this study, we analyzed the gene expression data from 59 breast cancer patients in Taiwan and correlated them with SNPs and microRNAs in estrogen receptor (ER)-positive compared to ER-negative breast cancers. Estrogen receptor (ER) activation plays an important role in the progression and development of breast cancer. In the first part, we compared the gene expression profiling in Taiwan with that of 99 breast cancer patients in the United Kingdom to reveal population-unique SNPs in breast cancer of different ethnic origins. Using public SNP databases and statistical analysis, a total of 83 population-unique SNPs in these 17 genes were identified. The association between the distinct expression profiles of these genes in two populations and their population-unique SNPs may imply that these population-unique SNPs are likely related to ER regulation in breast cancer of different populations.
MicroRNAs (miRNAs) are mediators of gene expression repression that control many biological processes in development, differentiation, growth and metabolism. In the second part, we built a systematical approach to facilitate the discovery of miRNAs’ functions and link miRNAs to biological pathways by using the gene expression profiling. We developed a web-based bioinformatics tool, miLink, to integrate miRNAs target prediction information and current major biological pathway resources. Moreover, miLink can provide statistical analysis for selection of possible miRNAs that associate with target genes. Applying the analysis results of the gene expression data to miLink, several possible microRNAs and ER-related genes were chosen and quantified by RT-QPCR. Though the comparison of the expression of the miRNAs and target genes, we found that miR-218 and its target genes are associated with different estrogen receptor status. Our results may not only support the practical use of miLink in miRNA functions but also discover the correlation between the miRNAs and genes in ER regulatory mechanisms of breast cancer. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:07:22Z (GMT). No. of bitstreams: 1 ntu-96-R94548030-1.pdf: 6896993 bytes, checksum: 739387dea9ba5aff378565e66ef3ff75 (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 中文摘要
Abstract Ⅰ. Breast Cancer Microarray Data Analysis ─ The Association of Gene Expression Profiles with SNPs 1. Introduction 2 2. Literature review 4 2.1 Breast cancer 4 2.2 Estrogen and estrogen receptors(ER) 6 2.3.1 cDNA microarray 8 2.3.2 Oligonucleotide microarray 10 2.4 Single nucleotide polymorphisms (SNPs) 13 2.5 Bioinformatics tools 15 2.5.1 Multiple-Experiment Viewer (MeV) 15 2.5.2 EDGE (Extraction of Differential Gene Expression) 16 3. Methods 17 3.1 Experiment and analysis flowchart 17 3.2 RNA extraction and oligo microarray for Taiwanese breast cancers 17 3.3 Breast cancer gene expression datasets 18 3.4 Statistical analysis of microarray data 21 3.5 Selection of candidate genes for SNP analysis 22 3.6 SNPs search and statistical analysis 23 4. Results 24 4.1 Differentially expressed gene selection in ER+/ER- breast cancer subgroups 24 4.2 Comparison of differentially expressed genes in NTUH and NCI datasets 28 4.3 Association analysis of SNPs 33 4.4 Functional categories of identified genes 37 4.5 Mapping identified genes to biological pathways 38 5. Discussion 40 5.1 SNPs in ER-related genes and associated with breast cancer 40 5.2 The seventeen identified genes and breast cancers 41 5.3 The five genes identified in both NTUH and NCI dataset 43 5.4 Application of our analysis approach 44 6. Conclusion 45 Reference 46 1. Introduction 52 2. Literature review 55 2.1 MiRNAs 55 2.2 Relation of miRNAs and cancer 56 2.3 Prediction of miRNAs targets 57 2.4 Biological pathway databases 58 2.4.1 KEGG pathway 58 2.4.2 Biocarta pathway 58 2.4.3 NetPath pathway 59 2.5 Current useful websites for miRNA research 59 2.6 Real-Time Quantitative-Polymerase Chain Reaction (RT-QPCR) 61 3. Materials and Methods 62 3.1 Schema and flowchart of miLink 62 3.3 Pathway integration 64 3.4 Tests of Statistical significance 66 3.5 Graphical association of miRNAs and its predicted targets in biological pathways 67 3.6 Experimental validation 68 3.6.1 Experiment design and flowchart 68 3.6.2 MiRNAs prediction and analysis applying miLink 69 3.6.3 Cell culture 69 3.6.4 Total RNA extraction 73 3.6.5 Reverse Transcription PCR for cDNA 75 3.6.6 Reverse Transcription PCR for miRNAs cDNA 77 3.6.6.1 Materials and instruments 77 3.6.7 Real-Time PCR primer design 80 3.6.8 Real-Time PCR for significant genes confirmation 82 3.6.9 Real-Time PCR for selected miRNAs confirmation 85 3.6.9.1 Materials and instruments 85 4. Results 89 4.1 Implementation of search for miRNAs and its targets 89 4.1.1 Search by genes (main page) 89 4.1.2 Search by miRNAs 92 4.1.3 Search by pathway 93 4.2 Results of miRNAs prediction and analysis applying miLink 94 4.3 Functional classification of the differentially expressed genes 100 4.4 Mapping the differentially expressed genes to biological pathways 102 4.5 Quantification of Selected gene expression using RT-QPCR 106 4.6 Quantification of predicted miRNAs expression using RT-QPCR 110 4.7 Comparison of miRNAs and target gene expression quantified by microarray or RT-PCR 113 4.8 Comparison of miRNAs and target gene expression observed in references and our microarray 118 5. Discussion 123 5.1 Integration of the datasets with diverse identifiers 123 5.2 The differentially expressed genes and association with ER in breast cancers 124 5.2.1 The seven differentially expressed genes treated in this study 124 5.2.2 The rest differentially expressed genes 126 5.3 The eight corresponding miRNAs 127 5.4 The correlation between miRNA, target genes, and biological pathways 128 5.5 The inconsistency miRNAs expression in two pairs of ER-positive vs. ER-negative breast cancer cell lines 130 6. Conclusion 131 Reference 133 Appendix A. 141 Appendix B. 145 Appendix C . 159 Appendix D. 160 Appendix E. 162 | |
dc.language.iso | en | |
dc.title | 乳癌之基因微陣列分析研究—
探討基因表現與單核苷酸多型性及微型核醣核酸之關係 | zh_TW |
dc.title | Breast Cancer Microarray Data Analysis ─
The Association of Gene Expression Profiles with SNPs and microRNAs | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 阮雪芬(Hsuen-Fen Juan) | |
dc.contributor.oralexamcommittee | 黃宣誠,謝豐舟 | |
dc.subject.keyword | 微陣列晶片,基因表現,單核苷,酸多型性,微型核醣核酸,乳癌,雌激素受體,生物資訊, | zh_TW |
dc.subject.keyword | microarray,gene expression,single nucleotide polymorphisms (SNPs),microRNAs,breast cancer,estrogen receptor,bioinformatics, | en |
dc.relation.page | 165 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-30 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
顯示於系所單位: | 醫學工程學研究所 |
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