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Published in: BMC Cancer 1/2010

Open Access 01-12-2010 | Research article

Prioritizing genes associated with prostate cancer development

Authors: Ivan P Gorlov, Kanishka Sircar, Hongya Zhao, Sankar N Maity, Nora M Navone, Olga Y Gorlova, Patricia Troncoso, Curtis A Pettaway, Jin Young Byun, Christopher J Logothetis

Published in: BMC Cancer | Issue 1/2010

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Abstract

Background

The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.

Methods

A Z score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.

Results

Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4, and AURKA--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.

Conclusions

By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.
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Literature
1.
go back to reference Shen L, Tan EC: Dimension reduction-based penalized logistic regression for cancer classification using microarray data. IEEE/ACM Trans Comput Biol Bioinform. 2005, 2: 166-175. 10.1109/TCBB.2005.22.CrossRefPubMed Shen L, Tan EC: Dimension reduction-based penalized logistic regression for cancer classification using microarray data. IEEE/ACM Trans Comput Biol Bioinform. 2005, 2: 166-175. 10.1109/TCBB.2005.22.CrossRefPubMed
2.
go back to reference Wang L, Zhu JS, Song MQ, Chen GQ, Chen JL: Comparison of gene expression profiles between primary tumor and metastatic lesions in gastric cancer patients using laser microdissection and cDNA microarray. World J Gastroenterol. 2006, 12: 6949-6954.CrossRefPubMedPubMedCentral Wang L, Zhu JS, Song MQ, Chen GQ, Chen JL: Comparison of gene expression profiles between primary tumor and metastatic lesions in gastric cancer patients using laser microdissection and cDNA microarray. World J Gastroenterol. 2006, 12: 6949-6954.CrossRefPubMedPubMedCentral
3.
go back to reference Zigeuner R, Droschl N, Tauber V, Rehak P, Langner C: Biologic significance of fascin expression in clear cell renal cell carcinoma: systematic analysis of primary and metastatic tumor tissues using a tissue microarray technique. Urology. 2006, 68: 518-522. 10.1016/j.urology.2006.03.032.CrossRefPubMed Zigeuner R, Droschl N, Tauber V, Rehak P, Langner C: Biologic significance of fascin expression in clear cell renal cell carcinoma: systematic analysis of primary and metastatic tumor tissues using a tissue microarray technique. Urology. 2006, 68: 518-522. 10.1016/j.urology.2006.03.032.CrossRefPubMed
4.
go back to reference Jansen MP, Foekens JA, Klijn JG, Berns EM: Re: Limits of predictive models using microarray data for breast cancer clinical treatment outcome [comment]. J Natl Cancer Inst. 2005, 97: 1851-1853. 10.1093/jnci/dji433.CrossRefPubMed Jansen MP, Foekens JA, Klijn JG, Berns EM: Re: Limits of predictive models using microarray data for breast cancer clinical treatment outcome [comment]. J Natl Cancer Inst. 2005, 97: 1851-1853. 10.1093/jnci/dji433.CrossRefPubMed
5.
go back to reference Reid JF, Lusa L, De Cecco L, Coradini D, Veneroni S, Daidone MG, Gariboldi M, Pierotti MA: Limits of predictive models using microarray data for breast cancer clinical treatment outcome. J Natl Cancer Inst. 2005, 97: 927-930. 10.1093/jnci/dji153.CrossRefPubMed Reid JF, Lusa L, De Cecco L, Coradini D, Veneroni S, Daidone MG, Gariboldi M, Pierotti MA: Limits of predictive models using microarray data for breast cancer clinical treatment outcome. J Natl Cancer Inst. 2005, 97: 927-930. 10.1093/jnci/dji153.CrossRefPubMed
6.
go back to reference Habeck M: DNA microarray technology to revolutionise cancer treatment. Lancet Oncol. 2001, 2: 5-10.1016/S1470-2045(00)00206-0.CrossRefPubMed Habeck M: DNA microarray technology to revolutionise cancer treatment. Lancet Oncol. 2001, 2: 5-10.1016/S1470-2045(00)00206-0.CrossRefPubMed
7.
go back to reference Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B: Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics. 2006, 22: e184-e190. 10.1093/bioinformatics/btl230.CrossRefPubMed Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B: Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics. 2006, 22: e184-e190. 10.1093/bioinformatics/btl230.CrossRefPubMed
8.
go back to reference Brennan DJ, O'Brien SL, Fagan A, Culhane AC, Higgins DG, Duffy MJ, Gallagher WM: Application of DNA microarray technology in determining breast cancer prognosis and therapeutic response. Expert Opin Biol Ther. 2005, 5: 1069-1083. 10.1517/14712598.5.8.1069.CrossRefPubMed Brennan DJ, O'Brien SL, Fagan A, Culhane AC, Higgins DG, Duffy MJ, Gallagher WM: Application of DNA microarray technology in determining breast cancer prognosis and therapeutic response. Expert Opin Biol Ther. 2005, 5: 1069-1083. 10.1517/14712598.5.8.1069.CrossRefPubMed
9.
go back to reference Dolled-Filhart M, Camp RL, Kowalski DP, Smith BL, Rimm DL: Tissue microarray analysis of signal transducers and activators of transcription 3 (Stat3) and phospho-Stat3 (Tyr705) in node-negative breast cancer shows nuclear localization is associated with a better prognosis. Clin Cancer Res. 2003, 9: 594-600.PubMed Dolled-Filhart M, Camp RL, Kowalski DP, Smith BL, Rimm DL: Tissue microarray analysis of signal transducers and activators of transcription 3 (Stat3) and phospho-Stat3 (Tyr705) in node-negative breast cancer shows nuclear localization is associated with a better prognosis. Clin Cancer Res. 2003, 9: 594-600.PubMed
10.
go back to reference Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R: NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res. 2007, 35 (Database issue): D760-D765. 10.1093/nar/gkl887.CrossRefPubMed Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R: NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res. 2007, 35 (Database issue): D760-D765. 10.1093/nar/gkl887.CrossRefPubMed
11.
go back to reference Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30: 207-210. 10.1093/nar/30.1.207.CrossRefPubMedPubMedCentral Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30: 207-210. 10.1093/nar/30.1.207.CrossRefPubMedPubMedCentral
12.
go back to reference Gur-Dedeoglu B, Konu O, Kir S, Ozturk AR, Bozkurt B, Ergul G, Yulug IG: A resampling-based meta-analysis for detection of differential gene expression in breast cancer. BMC Cancer. 2008, 8: 396-10.1186/1471-2407-8-396.CrossRefPubMedPubMedCentral Gur-Dedeoglu B, Konu O, Kir S, Ozturk AR, Bozkurt B, Ergul G, Yulug IG: A resampling-based meta-analysis for detection of differential gene expression in breast cancer. BMC Cancer. 2008, 8: 396-10.1186/1471-2407-8-396.CrossRefPubMedPubMedCentral
13.
go back to reference Lin R, Dai S, Irwin RD, Heinloth AN, Boorman GA, Li L: Gene set enrichment analysis for non-monotone association and multiple experimental categories. BMC Bioinformatics. 2008, 9: 481-10.1186/1471-2105-9-481.CrossRefPubMedPubMedCentral Lin R, Dai S, Irwin RD, Heinloth AN, Boorman GA, Li L: Gene set enrichment analysis for non-monotone association and multiple experimental categories. BMC Bioinformatics. 2008, 9: 481-10.1186/1471-2105-9-481.CrossRefPubMedPubMedCentral
14.
go back to reference Ochsner SA, Steffen DL, Hilsenbeck SG, Chen ES, Watkins C, McKenna NJ: GEMS (Gene Expression MetaSignatures), a Web resource for querying meta-analysis of expression microarray datasets: 17β-estradiol in MCF-7 cells. Cancer Res. 2009, 69: 23-26. 10.1158/0008-5472.CAN-08-3492.CrossRefPubMedPubMedCentral Ochsner SA, Steffen DL, Hilsenbeck SG, Chen ES, Watkins C, McKenna NJ: GEMS (Gene Expression MetaSignatures), a Web resource for querying meta-analysis of expression microarray datasets: 17β-estradiol in MCF-7 cells. Cancer Res. 2009, 69: 23-26. 10.1158/0008-5472.CAN-08-3492.CrossRefPubMedPubMedCentral
15.
go back to reference Rosenthal R: The file drawer problem and tolerance for null results. Psychol Bull. 1979, 86: 638-641. 10.1037/0033-2909.86.3.638.CrossRef Rosenthal R: The file drawer problem and tolerance for null results. Psychol Bull. 1979, 86: 638-641. 10.1037/0033-2909.86.3.638.CrossRef
16.
go back to reference Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4: P3-10.1186/gb-2003-4-5-p3.CrossRefPubMed Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4: P3-10.1186/gb-2003-4-5-p3.CrossRefPubMed
17.
go back to reference Gorlov IP, Byun J, Gorlova OY, Aparicio AM, Efstathiou E, Logothetis CJ: Candidate pathways and genes for prostate cancer: a meta-analysis of gene expression data. BMC Med Genomics. 2009, 2: 48-10.1186/1755-8794-2-48.CrossRefPubMedPubMedCentral Gorlov IP, Byun J, Gorlova OY, Aparicio AM, Efstathiou E, Logothetis CJ: Candidate pathways and genes for prostate cancer: a meta-analysis of gene expression data. BMC Med Genomics. 2009, 2: 48-10.1186/1755-8794-2-48.CrossRefPubMedPubMedCentral
19.
20.
go back to reference Chandran UR, Ma C, Dhir R, Bisceglia M, Lyons-Weiler M, Liang W, Michalopoulos G, Becich M, Monzon FA: Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process. BMC Cancer. 2007, 7: 64-10.1186/1471-2407-7-64.CrossRefPubMedPubMedCentral Chandran UR, Ma C, Dhir R, Bisceglia M, Lyons-Weiler M, Liang W, Michalopoulos G, Becich M, Monzon FA: Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process. BMC Cancer. 2007, 7: 64-10.1186/1471-2407-7-64.CrossRefPubMedPubMedCentral
21.
go back to reference Stanbrough M, Bubley GJ, Ross K, Golub TR, Rubin MA, Penning TM, Febbo PG, Balk SP: Increased expression of genes converting adrenal androgens to testosterone in androgen-independent prostate cancer. Cancer Res. 2006, 66: 2815-2825. 10.1158/0008-5472.CAN-05-4000.CrossRefPubMed Stanbrough M, Bubley GJ, Ross K, Golub TR, Rubin MA, Penning TM, Febbo PG, Balk SP: Increased expression of genes converting adrenal androgens to testosterone in androgen-independent prostate cancer. Cancer Res. 2006, 66: 2815-2825. 10.1158/0008-5472.CAN-05-4000.CrossRefPubMed
22.
go back to reference Price ND, Trent J, El-Naggar AK, Cogdell D, Taylor E, Hunt KK, Pollock RE, Hood L, Shmulevich I, Zhang W: Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas. Proc Natl Acad Sci USA. 2007, 104: 3414-3419. 10.1073/pnas.0611373104.CrossRefPubMedPubMedCentral Price ND, Trent J, El-Naggar AK, Cogdell D, Taylor E, Hunt KK, Pollock RE, Hood L, Shmulevich I, Zhang W: Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas. Proc Natl Acad Sci USA. 2007, 104: 3414-3419. 10.1073/pnas.0611373104.CrossRefPubMedPubMedCentral
23.
go back to reference Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30: e15-10.1093/nar/30.4.e15.CrossRefPubMedPubMedCentral Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30: e15-10.1093/nar/30.4.e15.CrossRefPubMedPubMedCentral
24.
go back to reference Nakagawa T, Kollmeyer TM, Morlan BW, Anderson SK, Bergstralh EJ, Davis BJ, Asmann YW, Klee GG, Ballman KV, Jenkins RB: A tissue biomarker panel predicting systemic progression after PSA recurrence post-definitive prostate cancer therapy. PLoS One. 2008, 3: e2318-10.1371/journal.pone.0002318.CrossRefPubMedPubMedCentral Nakagawa T, Kollmeyer TM, Morlan BW, Anderson SK, Bergstralh EJ, Davis BJ, Asmann YW, Klee GG, Ballman KV, Jenkins RB: A tissue biomarker panel predicting systemic progression after PSA recurrence post-definitive prostate cancer therapy. PLoS One. 2008, 3: e2318-10.1371/journal.pone.0002318.CrossRefPubMedPubMedCentral
25.
go back to reference Nikitin A, Egorov S, Daraselia N, Mazo I: Pathway studio--the analysis and navigation of molecular networks. Bioinformatics. 2003, 19: 2155-2157. 10.1093/bioinformatics/btg290.CrossRefPubMed Nikitin A, Egorov S, Daraselia N, Mazo I: Pathway studio--the analysis and navigation of molecular networks. Bioinformatics. 2003, 19: 2155-2157. 10.1093/bioinformatics/btg290.CrossRefPubMed
Metadata
Title
Prioritizing genes associated with prostate cancer development
Authors
Ivan P Gorlov
Kanishka Sircar
Hongya Zhao
Sankar N Maity
Nora M Navone
Olga Y Gorlova
Patricia Troncoso
Curtis A Pettaway
Jin Young Byun
Christopher J Logothetis
Publication date
01-12-2010
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2010
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/1471-2407-10-599

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