Abstract
Microarray technology has proven to be a powerful tool for simultaneous profiling of the expression of a large number of genes in various cells and tissues under normal physiological or pathological conditions. The introduction of microarray in profiling the pattern of gene expression in various female reproductive tract tissues and cells resulted in the generation of a large body of new information representing their molecular environments under both normal physiological and pathological conditions. The identification of transcripts for many genes, whose products’ functional relevance in reproductive tissues was not previously predicated, has opened new research opportunities in the field of reproductive endocrinology. Here, we shall discuss the application of microarray in studying gene expression profiling in myometrium and leiomyoma and the consequence of GnRHa therapy on their expression, as well as their profiles in smooth muscle cells isolated from these tissues in response to direct action of GnRHa. We shall also discuss the influence of transforming growth factor beta (TGF-β), a profibrotic cytokine, on leiomyoma and myometrial smooth muscle gene expression profile and the consequence of TGF-β receptor type II antisense treatment as an alternative approach to suppress leiomyoma growth. Furthermore, we shall discuss the experimental design with appropriate steps and procedures for RNA preparation, microarray hybridization, image acquisition and data analysis, interpretation, confirmation, and finally the biological significance of these genes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wallach, E.E. and Vlahos, N.F. (2004) Uterine myomas: an overview of development, clinical features, and management. Obstet. Gynecol. 104, 393–406.
Ohara, N. (2005) Selective estrogen receptor modulator and selective progesterone receptormodulator: therapeutic efficacy in the treatment of uterine leiomyoma. Clin. Exp. Obstet. Gynecol. 32, 9–11
Chabbert-Buffet N., Meduri, G., Bouchard, P., and Spitz, I.M. (2005) Selective progesterone receptor modulators and progesterone antagonists: mechanisms of action and clinical applications. Hum. Reprod. Update. 11, 293–307
Chwalisz, K., Perez, M.C., Demanno, D., Winkel, C., Schubert, G., and Elger, W. (2005) Selective progesterone eceptor modulator development and use in the treatment of leiomyomata and endometriosis. Endocr. Rev. 26, 423–438
Chegini, N. (2000) Implication of growth factor and cytokine networks in leiomyomas. In: Hill J, ed. Cytokines in human reproduction. New York: Wiley & Sons; 133–162
Sandberg, A.A. (2005) Updates on the cytogenetics and molecular genetics of bone and soft tissue tumors: leiomyoma. Cancer Genet. Cytogenet. 158, 1–26
Walker, C.L., and Stewart, E.A. (2005) Uterine fibroids: the elephant in the room. Science 308, 1589–592
Tsibris, J.C., Segars, J., Coppola, D., Mane, S., Wilbanks, G.D., O’Brien, W.F., and Spellacy, W.N. (2002) Insights from gene arrays on the development and growth regulation of uterine leiomyomata. Fertil. Steril. 78, 114–121
Wu, X., Blanck, A., Norstedt, G., Sahlin, L., and Flores-Morales, A. (2002) Identification of genes with higher expression in human uterine leiomyomas than in the corresponding myometrium. Mol. Hum. Reprod. 8, 246–254
Chegini, N., Verala, J., Luo, X., Xu, J, and Williams, R.S. (2003) Gene expression profile of leiomyoma and myometrium and the effect of gonadotropin releasing hormone analogue therapy. J. Soc. Gynecol. Investig. 10, 161–171
Catherino, W.H., Prupas, C., Tsibris, J.C., Leppert, P.C., Payson, M., Nieman, L.K., and Segars, J.H. (2003) Strategy for elucidating differentially expressed genes in leiomyomata identified by microarray technology. Fertil. Steril. 80, 282–290
Wang, H., Mahadevappa, M., Yamamoto, K., Wen, Y., Chen, B., Warrington, J.A., and Polan, M.L. (2003) Distinctive proliferative phase differences in gene expression in human myometrium and leiomyomata. Fertil. Steril. 80, 266–276
Weston, G., Trajstman, A.C., Gargett, C.E., Manuelpillai, U., Vollenhoven, B.J., and Rogers, P.A. (2003) Fibroids display an anti-angiogenic gene expression profile when compared with adjacent myometrium. Mol. Hum. Reprod. 9, 541–549
Ahn, W.S., Kim, K.W., Bae, S.M., Yoon, J.H., Lee, J.M., Namkoong, S.E., Kim, J.H., Kim, C.K., Lee, Y.J., and Kim, Y.W. (2003) Targeted cellular process profiling approach for uterine leiomyoma using cDNA microarray, proteomics and gene ontology analysis. Int. J. Exp. Pathol. 84, 267–279
Kanamori, T., Takakura, K., Mandai, M., Kariya, M., Fukuhara, K., Kusakari, T, Momma, C., Shime, H., Yagi, H., Konishi, M., Suzuki, A., Matsumura, N., Nanbu, K., Fujita, J., and Fujii, S. (2003) PEP-19 overexpression in human uterine leiomyoma. Mol. Hum. Reprod. 9, 709–717
Skubitz, K.M., and Skubitz, A.P. (2003) Differential gene expression in uterine leiomyoma J. Lab. Clin. Med. 141, 297–308
Hoffman, P.J., Milliken, D.B., Gregg, L.C., Davis, R.R. and Gregg, J.P. (2004) Molecular characterization of uterine fibroids and its implication for underlying mechanisms of pathogenesis. Fertil. Steril. 82, 639–649
Quade, B.J., Wang, T.Y., Sornberger, K., DalCin, P., Mutter, G.L., and Morton, C.C. (2004) Molecular pathogenesis of uterine smooth muscle tumors from transcriptional profiling. Genes Chromosomes Cancer 40, 97–108
Lee, E.J., Kong, G., Lee, S.H., Rho, S.B., Park, C.S., Kim, B.G., Bae, D.S., Kavanagh, J.J., and Lee, J.H. (2005) Profiling of differentially expressed genes in human uterine leiomyomas. Int. J. Gynecol. Cancer 15, 146–154
Luo, X., Ding, L., Xu, J., Williams, R.S., and Chegini, N. (2005) Leiomyoma and myometrial gene expression profiles and their responses to gonadotropin-releasing hormone analog therapy. Endocrinology 146, 1074–1096
Luo, X., Ding, L., Xu, J., and Chegini, N. (2005) Gene expression profiling of leiomyoma and myometrial smooth muscle cells in response to transforming growth factor-beta. Endocrinology 146, 1097–1118
Swartz, C.D., Afshari, C.A., Yu, L., Hall, K.E., and Dixon, D. (2005) Estrogen-induced changes in IGF-I, Myb family and MAP kinase pathway genes in human uterine leiomyoma and normal uterine smooth muscle cell lines. Mol. Hum. Reprod. 11, 441–450
Arslan, A.A., Gold, L.I., Mittal, K., Suen, T.C., Belitskaya-Levy, I., Tang, M.S., and Toniolo, P. (2005) Gene expression studies provide clues to the pathogenesis of uterine leiomyoma: new evidence and a systematic review. Hum. Reprod. 20, 852–863
Chegini, N., Ma, C., Tang, X.M., and Williams, R.S. (2002) Effects of GnRH analogues, ‘add-back’ steroid therapy, antiestrogen and antiprogestins on leiomyoma and myometrial smooth muscle cell growth and transforming growth factor-beta expression. Mol. Hum. Reprod. 8, 1071–1078.
Rossi, M.J., Chegini, N., and Masterson, B.J. (1992) Presence of epidermal growth factor, platelet-derived growth factor, and their receptors in human myometrial tissue and smooth muscle cells: their action in smooth muscle cells in vitro. Endocrinology 130, 1716–1727.
Ding, L., Xu, J., Luo, X., and Chegini, N. (2004) Gonadotropin releasing hormone and transforming growth factor beta activate mitogen-activated protein kinase/extracellularly regulated kinase and differentially regulate fibronectin, type I collagen, and plasminogen activator inhibitor-1 expression in leiomyoma and myometrial smooth muscle cells. J. Clin. Endocrinol. Metab. 89, 5549–5557.
Stoughton, R.B. (2005) Applications of DNA microarrays in biology. Annu. Rev. Biochem. 74, 53–82.
Auburn, R.P., Kreil, D.P., Meadows, L.A., Fischer, B., Matilla, S.S., and Russell, S. (2005) Robotic spotting of cDNA and oligonucleotide microarrays. Trends Biotechnol. 23, 374–379.
Barrett, J.C., and Kawasaki, E.S. (2003) Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression. Drug Discov. Today 8, 134–141.
Larkin, J.E., Frank, B.C., Gavras, H., Sultana, R., and Quackenbush, J. (2005) Independence and reproducibility across microarray platforms. Nat. Methods 2, 337–344.
Irizarry, R.A., Warren, D., Spencer, F., Kim, I.F., Biswal, S., Frank, B.C., Gabrielson, E., Garcia, J.G., Geoghegan, J., Germino, G., Griffin, C., Hilmer, S.C., Hoffman, E., Jedlicka, A.E., Kawasaki, E., Martinez-Murillo, F., Morsberger, L., Lee, H., Petersen, D., Quackenbush, J., Scott, A., Wilson, M., Yang, Y., Ye, S.Q., and Yu, W. (2005) Multiple-laboratory comparison of microarray platforms. Nat. Methods 2, 345–350.
Engelen, K., Coessens, B., Marchal, K., and DeMoor, B. (2003) MARAN: normalizing micro-array data. Bioinformatics 19, 893–894.
Quackenbush, J. (2002) Microarray data normalization and transformation. Nat. Genet. 32 (Suppl), 496–501.
Smyth, G.K, and Speed, T. (2003) Normalization of cDNA microarray data. Methods 31, 265–273.
Park, T., Yi, S.G., Kang, S.H., Lee, S.Y., Lee, Y.S., and Simon, R. (2003) Evaluation of normalization methods for microarray data. BMC Bioinformatics 4, 33.
Butte, A. (2002) The use and analysis of microarray data. Nat. Rev. Drug. Discov. 1, 951–960.
Curtis, R.K., Oresic, M., and Vidal-Puig, A. (2005) Pathways to the analysis of microarray data. Trends Biotechnol. 23, 429–435.
Raychaudhuri, S., Sutphin, P.D., Chang, J.T., and Altman, R.B. (2001) Basic microarray analysis: grouping and feature reduction. Trends Biotechnol. 19, 189–193.
Toronen, P., Kolehmainen, M., Wong, G., and Castren, E. (1999) Analysis of gene expression data using self-organizing maps. FEBS. Lett. 451, 142–146.
Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868.
Chen, J.J., Delongchamp, R.R., Tsai, C.A., Hsueh, H.M., Sistare, F., Thompson, K.L., Desai, V.G., and Fuscoe, J.C. (2004) Analysis of variance components in gene expression data. Bioinformatics 20, 1436–1446.
Chuaqui, R.F., Bonner, R.F., Best, C.J., Gillespie, J.W., Flaig, M.J., Hewitt, S.M., Phillips, J.L., Krizman, D.B., Tangrea, M.A., Ahram, M., Linehan, W.M., Knezevic, V., and Emmert-Buck, M.R. (2002) Post-analysis follow-up and validation of microarray experiments. Nat. Genet. 32 (Suppl), 509–514.
Churchill, G.A. (2002) Fundamentals of experimental design for cDNA microarrays. Nature Genet 32 (Suppl), 490–495.
Cole, S.W., Galic, Z., and Zack, J.A. (2003) Controlling false negative errors in microarray differential expression analysis: a PRIM approach. Bioinformatics 19, 1808–1816.
Cui, X., and Churchill, G.A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 4, 210.
Dudoit, S., Yang, Y.H., Callow, M.J., and Speed, T.P. (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sin. 12, 111–139.
Dudoit, S., Yang, Y.H., and Bolstad, B. (2002) Using R for the analysis of DNA microarray data. R News 2, 24–32.
Ihaka, R., and Gentleman, R. (1996) R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314.
Tusher, V.G., Tibshirani, R., and Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121.
Storey, J.D., and Tibshirani, R. (2003) Statistical significance for genome-wide studies. Proc. Natl. Acad. Sci. USA 100 9440–9445.
Baldi, P., and Long, A. (2001) A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17, 509–519.
Herrero, J., Al-Shahrour, F., Diaz-Uriarte, R., Mateos, A., Vaquerizas, J.M., Santoyo, J., and Dopazo, J. (2003), GEPAS: A web-based resource for microarray gene expression data analysis. Nucleic Acids Res. 31, 3461–3467.
Al-Shahrour, F., Diaz-Uriarte, R., and Dopazo, J. (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578–580.
Adryan, B., and Schuh R. (2004) Gene ontology-based clustering of gene expression data. Bioinformatics 20, 2851–2852.
Shoop, E., Casaes, P., Onsongo, G., Lesnett, L., Petursdottir, E.O., Donkor, E.K., Tkach, D., and Cosimini, M. (2004) Data exploration tools for the Gene Ontology database, Bioinformatics 20, 3442–3454.
Zeeberg, B.R., Qin, H., Narasimhan, S., Sunshine, M., Cao, H., Kane, D.W., Reimers, M., Stephens, R.M., Bryant, D., Burt, S.K.., Elnekave, E., Hari, D.M., Wynn, T.A., Cunningham-Rundles, C., Stewart, D.M., Nelson, D., and Weinstein, J.N. (2005) High-Throughput GoMiner, an ‘industrial-strength’ integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID). BMC Bioinformatics 6, 168.
Beissbarth T., and Speed T.P. (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20, 1464–1465.
Zhong, S., Li, C., and Wong, W.H. (2003) ChipInfo: software for extracting gene annotation and gene ontology information for microarray analysis. Nucleic Acids Res. 31 3483–3486.
Doniger, S.W., Salomonis, N., Dahlquist, K.D., Vranizan, K., Lawlor, S.C., and Conklin, B.R. (2003) MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 4, R7.
Cheng, J., Sun, S., Tracy, A., Hubbell, E., Morris, J., Valmeekam, V., Kimbrough, A., Cline, M.S., Liu, G., Shigeta, R., Kulp, D., and Siani-Rose, M.A. (2004) NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis. Bioinformatics 20, 1462–1463.
Khatri, P., and Draghici, S. (2005) Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21, 3587–3595.
Kanno J., Aisaki, K-I., Igarashi, K., Nakatsu, N., Ono, A., Kodama, Y., and Nagao T. (2006) Per cell" normalization method for mRNA measurement by quantitative PCR and microarrays. BMC Genomics. 7, 64.
Pandey, R., Guru, R.K., and Mount, D.W. (2004) Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data. Bioinformatics 20, 2156–2158.
Knight, J. (2001) When the chips are down. Nature 410, 860–861.
Kothapalli, R., Yoder, S.J., Mane, S, and Loughran, T.P. Jr. (2002) Microarray results: how accurate are they? BMC Bioinformatics 3, 22.
Stears, R.L., Martinsky, T., and Schena, M. (2003) Trends in microarray analysis. Nat. Med. 9, 140–145.
Taniguchi, M., Miura. K., Iwao, H., and Yamanaka, S. (2001) Quantitative assessment of DNA microarrays – comparison with Northern blot analyses. Genomics 71, 34–39.
Rajeevan, M.S., Vernon, S.D., Taysavang, N., and Unger, E.R. (2001) Validation of array-based gene expression profiles by real-time (kinetic) RT-PCR. J. Mol. Diagn. 3, 26–3.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Humana Press
About this chapter
Cite this chapter
Chegini, N., Luo, X. (2008). Gene Expression Profiling in Leiomyoma in Response to GnRH Therapy and TGF-β. In: Handwerger, S., Aronow, B. (eds) Genomics in Endocrinology. Contemporary Endocrinology. Humana Press. https://doi.org/10.1007/978-1-59745-309-7_4
Download citation
DOI: https://doi.org/10.1007/978-1-59745-309-7_4
Publisher Name: Humana Press
Print ISBN: 978-1-58829-651-1
Online ISBN: 978-1-59745-309-7
eBook Packages: MedicineMedicine (R0)