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DNA microarrays in prostate cancer

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Abstract

DNA microarray technology provides a means to examine large numbers of molecular changes related to a biological process in a high throughput manner. This review discusses plausible utilities of this technology in prostate cancer research, including definition of prostate cancer predisposition, global profiling of gene expression patterns associated with cancer initiation and progression, identification of new diagnostic and prognostic markers, and discovery of novel patient classification schemes. The technology, at present, has only been explored in a limited fashion in prostate cancer research. Some hurdles to be overcome are the high cost of the technology, insufficient sample size and repeated experiments, and the inadequate use of bioinformatics. With the completion of the Human Genome Project and the advance of several highly complementary technologies, such as laser capture microdissection, unbiased RNA amplification, customized functional arrays (eg, single-nucleotide polymorphism chips), and amenable bioinformatics software, this technology will become widely used by investigators in the field. The large amount of novel, unbiased hypotheses and insights generated by this technology is expected to have a significant impact on the diagnosis, treatment, and prevention of prostate cancer. Finally, this review emphasizes existing, but currently underutilized, data-mining tools, such as multivariate statistical analyses, neural networking, and machine learning techniques, to stimulate wider usage.

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References and Recommended Reading

  1. American Cancer Society: Cancer facts and figures 2001. http://www.cancer.org/eprise/main/docroot/stt/stt_0.

  2. Peters KF, Menaker TJ, Wilson PL, Hadley DW: The Human Genome Project: an update. Cancer Nurs 2001, 24:287–292; quiz 292–293.

    Article  PubMed  CAS  Google Scholar 

  3. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995, 270:467–470.

    Article  PubMed  CAS  Google Scholar 

  4. Fields C, Adams MD, White O, Venter JC: How many genes in the human genome? Nat Genet 1994, 7:345–346.

    Article  PubMed  CAS  Google Scholar 

  5. Bishop JO, Morton JG, Rosbash M, Richardson M: Three abundance classes in HeLa cell messenger RNA. Nature 1974, 250:199–204.

    Article  PubMed  CAS  Google Scholar 

  6. Chaib H, Cockrell EK, Rubin MA, Macoska JA: Profiling and verification of gene expression patterns in normal and malignant human prostate tissues by cDNA microarray analysis. Neoplasia 2001, 3:43–52.

    Article  PubMed  CAS  Google Scholar 

  7. Emmert-Buck MR, Strausberg RL, Krizman DB, et al.: Molecular profiling of clinical tissues specimens: feasibility and applications. J Mol Diagn 2000, 2:60–66. An excellent review of the resources available for DNA microarray and complementary technologies for molecular studies of the biological basis of prostate cancer.

    PubMed  CAS  Google Scholar 

  8. Svaren J, Ehrig T, Abdulkadir SA, et al.: EGR1 target genes in prostate carcinoma cells identified by microarray analysis. J Biol Chem 2000, 275:38524–38531.

    Article  PubMed  CAS  Google Scholar 

  9. Calaluce R, Kunkel MW, Watts GS, et al.: Laminin-5-mediated gene expression in human prostate carcinoma cells. Mol Carcinog 2001, 30:119–129.

    Article  PubMed  CAS  Google Scholar 

  10. Ouyang XS, Wang X, Lee DT, et al.: Up-regulation of TRPM-2, MMP-7 and ID-1 during sex hormone-induced prostate carcinogenesis in the Noble rat. Carcinogenesis 2001, 22:965–973.

    Article  PubMed  CAS  Google Scholar 

  11. Asamoto M, Hokaiwado N, Cho YM, et al.: Prostate carcinomas developing in transgenic rats with SV40 T antigen expression under probasin promoter control are strictly androgen dependent. Cancer Res 2001, 61:4693–4700.

    PubMed  CAS  Google Scholar 

  12. Amler LC, Agus DB, LeDuc C, et al.: Dysregulated expression of androgen-responsive and nonresponsive genes in the androgen-independent prostate cancer xenograft model CWR22-R1. Cancer Res 2000, 60:6134–6141.

    PubMed  CAS  Google Scholar 

  13. Lin B, White JT, Ferguson C, et al.: Prostate short-chain dehydrogenase reductase 1 (PSDR1): a new member of the short-chain steroid dehydrogenase/reductase family highly expressed in normal and neoplastic prostate epithelium. Cancer Res 2001, 61:1611–1618.

    PubMed  CAS  Google Scholar 

  14. Hawkins V, Doll D, Bumgarner R, et al.: PEDB: the Prostate Expression Database. Nucleic Acids Res 1999, 27:204–208.

    Article  PubMed  CAS  Google Scholar 

  15. Cole KA, Krizman DB, Emmert-Buck MR: The genetics of cancer—a 3D model. Nat Genet 1999, 21(suppl 1):38–41.

    Article  PubMed  CAS  Google Scholar 

  16. Elek J, Park KH, Narayanan R: Microarray-based expression profiling in prostate tumors. In Vivo 2000, 14:173–182.

    PubMed  CAS  Google Scholar 

  17. Chaib H, Rubin MA, Mucci NR, et al.: Activated in prostate cancer: a PDZ domain-containing protein highly expressed in human primary prostate tumors. Cancer Res 2001, 61:2390–2394.

    PubMed  CAS  Google Scholar 

  18. Chetcuti A, Margan S, Mann S, et al.: Identification of differentially expressed genes in organ-confined prostate cancer by gene expression array. Prostate 2001, 47:132–140.

    Article  PubMed  CAS  Google Scholar 

  19. Bull JH, Ellison G, Patel A, et al.: Identification of potential diagnostic markers of prostate cancer and prostatic intraepithelial neoplasia using cDNA microarray. Br J Cancer 2001, 84:1512–1519. A comprehensive study involving DNA microarray analysis on clinical samples and successful identification of potential prostate/ PIN-specific markers.

    Article  PubMed  CAS  Google Scholar 

  20. Nagle RB: New molecular approaches to tissue analysis. J Histochem Cytochem 2001, 49:1063–1064. This paper presents some laser capture microdissection and RNA amplification as complementary technologies to DNA microarray.

    PubMed  CAS  Google Scholar 

  21. Xu J, Stolk JA, Zhang X, et al.: Identification of differentially expressed genes in human prostate cancer using subtraction and microarray. Cancer Res 2000, 60:1677–1682. An innovative combination of technologies to allow for identification of prostate- and prostate cancer-specific biomarkers.

    PubMed  CAS  Google Scholar 

  22. Carlisle AJ, Prabhu VV, Elkahloun A, et al.: Development of a prostate cDNA microarray and statistical gene expression analysis package. Mol Carcinog 2000, 28:12–22.

    Article  PubMed  CAS  Google Scholar 

  23. Verma RS, Manikal M, Conte RA, Godec CJ: Chromosomal basis of adenocarcinoma of the prostate. Cancer Invest 1999, 17:441–447.

    Article  PubMed  CAS  Google Scholar 

  24. Ross RK: The role of molecular genetics in chemoprevention studies of prostate cancer. IARC Sci Publ 2001, 154:207–213.

    PubMed  CAS  Google Scholar 

  25. Wang DG, Fan JB, Siao CJ, et al.: Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 1998, 280:1077–1082.

    Article  PubMed  CAS  Google Scholar 

  26. Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ: High density synthetic oligonucleotide arrays. Nat Genet 1999, 21:20–24. The authors review the technology and applications of in situ synthetic oligonucleotide microarray. Applications of DNA microarrays for large-scale genotyping and detection of gene mutation are well described and discussed in this article.

    Article  PubMed  CAS  Google Scholar 

  27. Ozen M, Pathak S: Genetic alterations in human prostate cancer: a review of current literature. Anticancer Res 2000, 20:1905–1912.

    PubMed  CAS  Google Scholar 

  28. Hacia JG: Resequencing and mutational analysis using oligonucleotide microarrays. Nat Genet 1999, 21:42–47. This paper describes the use of DNA microarray for DNA sequencing studies and its principle.

    Article  PubMed  CAS  Google Scholar 

  29. Hui AB, Lo KW, Yin XL, et al.: Detection of multiple gene amplifications in glioblastoma multiforme using array-based comparative genomic hybridization. Lab Invest 2001, 81:717–723.

    Article  PubMed  CAS  Google Scholar 

  30. Hegde P, Qi R, Abernathy K, et al.: A concise guide to cDNA microarray analysis. Biotechniques 2000, 29:548–556. This paper outlines the steps of cDNA microarray analysis and discusses each step in detail.

    PubMed  CAS  Google Scholar 

  31. Yang YH, Dudoit S, Luu P, Speed TP: Normalization for cDNA microarray data. Proceedings of the International Society for Optical Engineering International Biomedical Optics Symposium 2001. San Jose, CA; January 2001. Different ways to normalize the DNA microarray data are presented and compared. The mathematical computations and principles of data normalization are described.

  32. Vaarala MH, Porvari K, Kyllonen A, Vihko P: Differentially expressed genes in two LNCaP prostate cancer cell lines reflecting changes during prostate cancer progression. Lab Invest 2000, 80:1259–1268.

    Article  PubMed  CAS  Google Scholar 

  33. Raychaudhuri S, Stuart JM, Altman RB: Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 2000, 455–466.

  34. Hilsenbeck SG, Friedrichs WE, Schiff R, et al.: Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J Natl Cancer Inst 1999, 91:453–459. This is the first paper using principal component analysis on DNA microarray data to identify the differentially expressed genes related to tamoxifen-resistant breast cancer.

    Article  PubMed  CAS  Google Scholar 

  35. Khan J, Simon R, Bittner M, et al.: Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res 1998, 58:5009–5013.

    PubMed  CAS  Google Scholar 

  36. Luo J, Duggan DJ, Chen Y, et al.: Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res 2001, 61:4683–4688.

    PubMed  CAS  Google Scholar 

  37. Lu J, Liu Z, Xiong M, et al.: Gene expression profile changes in initiation and progression of squamous cell carcinoma of esophagus. Int J Cancer 2001, 91:288–294.

    Article  PubMed  CAS  Google Scholar 

  38. Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998, 95:14863–14868. The hierarchical clustering was, for the first time, used to analyze DNA microarray data for gene clustering. The authors wrote CLUSTER and TREEVIEW programs that are available to the public.

    Article  PubMed  CAS  Google Scholar 

  39. Bertucci F, Houlgatte R, Benziane A, et al.: Gene expression profiling of primary breast carcinomas using arrays of candidate genes. Hum Mol Genet 2000, 9:2981–2991.

    Article  PubMed  CAS  Google Scholar 

  40. Ross DT, Scherf U, Eisen MB, et al.: Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 2000, 24:227–235.

    Article  PubMed  CAS  Google Scholar 

  41. Getz G, Levine E, Domany E: Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci U S A 2000, 97:12079–12084.

    Article  PubMed  CAS  Google Scholar 

  42. Welsh JB, Zarrinkar PP, Sapinoso LM, et al.: Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl Acad Sci U S A 2001, 98:1176–1181.

    Article  PubMed  CAS  Google Scholar 

  43. Dhanasekaran SM, Barrette TR, Ghosh D, et al.: Delineation of prognostic biomarkers in prostate cancer. Nature 2001, 412:822–826. This paper presents the accurate classification of prostate tissue samples based on the patterns of gene expression profiles by DNA microarray analysis. In addition, the differentially expressed genes related to the phenotypes of the tissue samples were identified by t-statistics.

    Article  PubMed  CAS  Google Scholar 

  44. Ward JH: Hierarchical grouping to optimize an objective function. J Am Stat Assoc 1963, 58:236–262.

    Article  Google Scholar 

  45. Hartigan JA: Clustering Algorithms. New York: John Wiley and Sons; 1975. This book presents different clustering algorithms and the mathematical computations for measurement of similarity or dissimilarity of multidimensional data.

    Google Scholar 

  46. Callow MJ, Dudoit S, Gong EL, et al.: Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res 2000, 10:2022–2029. This paper presents t-statistics for DNA microarray data analysis to identify the statistically significant, differentially expressed genes. Using the Q-Q plot, the genes with differential expression are clearly visualized.

    Article  PubMed  CAS  Google Scholar 

  47. Lau KM, Thompson C, Tian W, Ho SM: Identification of differentially expressed genes in the ventral prostates of young and old rats using t-statistic analysis on cDNA microarray data, submitted.

  48. Tamayo P, Slonim D, Mesirov J, et al.: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A 1999, 96:2907–2912. The principal advantages over hierarchical clustering of selforganizing maps were well presented. The authors are one of the first groups using self-organizing maps to analyze DNA microarray data.

    Article  PubMed  CAS  Google Scholar 

  49. Tavazoie S, Hughes JD, Campbell MJ, et al.: Systematic determination of genetic network architecture. Nat Genet 1999, 22:281–285.

    Article  PubMed  CAS  Google Scholar 

  50. Toronen P, Kolehmainen M, Wong G, Castren E: Analysis of gene expression data using self-organizing maps. FEBS Lett 1999, 451:142–146. The principle of self-organizing maps was well presented. The authors are one of the first groups using self-organizing maps to analyze DNA microarray data.

    Article  PubMed  CAS  Google Scholar 

  51. Khan J, Wei JS, Ringner M, et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001, 7:673–679. This paper presents the successful use of artificial neural networks to train the machine to recognize the patterns of gene expression profiles of different types of small, round blue cell tumors of childhood and to accurately predict the cancer types of the blinded samples.

    Article  PubMed  CAS  Google Scholar 

  52. Mukherjee S, Tamayo P, Mesirov JP, et al.: Support vector machine classification of microarray data [technical report CBCL paper 182/AI memo 1676]. Cambridge, MA: Massachusetts Institute of Technology; 1999.

    Google Scholar 

  53. Furey TS, Cristianini N, Duffy N, et al.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16:906–914. This paper presents the use of support vector machines to recognize the patterns of gene expression profiles of cancer tissue samples. The principles and mathematic computations of support vector machines are well described.

    Article  PubMed  CAS  Google Scholar 

  54. Brown MP, Grundy WN, Lin D, et al.: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 2000, 97:262–267.

    Article  PubMed  CAS  Google Scholar 

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  1. Department of Surgery, University of Massachusetts Medical School, Room S4-746, 55 Lake Avenue North, 01655, Worcester, MA, USA

    Shuk-Mei Ho PhD & Kin-Mang Lau PhD

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  1. Shuk-Mei Ho PhD
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Ho, SM., Lau, KM. DNA microarrays in prostate cancer. Curr Urol Rep 3, 53–60 (2002). https://doi.org/10.1007/s11934-002-0011-x

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