Skip to main content
SpringerLink
Log in

Gene Expression Microarrays in Cancer Research

  • Chapter
  • First Online:
Pharmaceutical Perspectives of Cancer Therapeutics

The advent of microarray technology has enabled scientists to simultaneously investigate the expression of thousands of genes. This technology has been widely used in cancer research to better characterize cancer behaviors at mRNA level and to obtain new insights into various stages of carcinogenesis. A microarray-based experiment generally involves three major components: microarray manufacturing, sample processing, and data analysis, with the goals of identifying differential genes, expression signatures, modules, or networks associated with given pathological changes (Fig. 1). In this chapter, we will introduce the outline of DNA microarray technology and some basic issues related to gene expression microarray-based experiments. The state of the art of cancer gene expression studies regarding tumor development, molecular classification, outcome, and therapeutic response prediction will be addressed. We will also consider the current challenges in data analysis and interpretation of genomics studies. Moreover, we will discuss the emerging concept of cancer systems biology and novel signature-based drug discovery strategies, which are very important for the development of individualized cancer medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lipshutz RJ et al. Using oligonucleotide probe arrays to access genetic diversity. Biotechniques 1995; 19: 442–447.

    PubMed  CAS  Google Scholar 

  2. 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 

  3. Alwine JC, Kemp DJ, Stark GR. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc Natl Acad Sci USA 1977; 74: 5350–5354.

    Article  PubMed  CAS  Google Scholar 

  4. Hoheisel JD. Microarray technology: beyond transcript profiling and genotype analysis. Nat Rev Genet 2006; 7: 200–210.

    Article  PubMed  CAS  Google Scholar 

  5. Hardiman G. Microarray platforms – comparisons and contrasts. Pharmacogenomics 2004; 5: 487–502.

    Article  PubMed  CAS  Google Scholar 

  6. Barrett JC, Kawasaki ES. Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression. Drug Discov Today 2003; 8: 134–141.

    Article  PubMed  CAS  Google Scholar 

  7. Tan PK et al. Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 2003; 31: 5676–5684.

    Article  PubMed  CAS  Google Scholar 

  8. Shi L et al. The MicroArray Quality Control (MAQC) project shows inter- and intra-platform reproducibility of gene expression measurements. Nat Biotechnol 2006; 24: 1151–1161.

    Article  PubMed  CAS  Google Scholar 

  9. Draghici S, Khatri P, Eklund AC, Szallasi Z. Reliability and reproducibility issues in DNA microarray measurements. Trends Genet 2006; 22: 101–109.

    Article  PubMed  CAS  Google Scholar 

  10. Golub TR et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531–537.

    Article  PubMed  CAS  Google Scholar 

  11. Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 2003; 95: 14–18.

    Article  PubMed  CAS  Google Scholar 

  12. Perou CM et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA 1999; 96: 9212–9217.

    Article  PubMed  CAS  Google Scholar 

  13. Allison DB, Cui X, Page GP, Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006; 7: 55–65.

    Article  PubMed  CAS  Google Scholar 

  14. Tinker AV, Boussioutas A, Bowtell DD. The challenges of gene expression microarrays for the study of human cancer. Cancer Cell 2006; 9: 333–339.

    Article  PubMed  CAS  Google Scholar 

  15. Dobbin K, Simon R. Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 2005; 6: 27–38.

    Article  PubMed  Google Scholar 

  16. Garge NR et al. Reproducible clusters from microarray research: whither? BMC Bioinformatics 2005; 6(Suppl 2): S10.

    Article  PubMed  Google Scholar 

  17. Gold D et al. A comparative analysis of data generated using two different target preparation methods for hybridization to high-density oligonucleotide microarrays. BMC Genomics 2004; 5: 2.

    Article  PubMed  Google Scholar 

  18. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002; 32(Suppl): 490–495.

    Article  PubMed  CAS  Google Scholar 

  19. Shih JH et al. Effects of pooling mRNA in microarray class comparisons. Bioinformatics 2004; 20: 3318–3325.

    Article  PubMed  CAS  Google Scholar 

  20. Potter JD. Epidemiology, cancer genetics and microarrays: making correct inferences, using appropriate designs. Trends Genet 2003; 19: 690–695.

    Article  PubMed  CAS  Google Scholar 

  21. Bogaerts J et al. Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial. Nat Clin Pract Oncol 2006; 3: 540–551.

    Article  PubMed  CAS  Google Scholar 

  22. Paik S. Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with tamoxifen. Oncologist 2007; 12: 631–635.

    Article  PubMed  CAS  Google Scholar 

  23. Grant GR, Manduchi E, Stoeckert CJ, Jr. Analysis and management of microarray gene expression data. Curr Protoc Mol Biol 2007; Chapter 19: Units 19, 16.

    Google Scholar 

  24. Steibel JP, Rosa GJ. On reference designs for microarray experiments. Stat Appl Genet Mol Biol 2005; 4: Article36.

    PubMed  Google Scholar 

  25. Lee JK, Williams PD, Cheon S. Data mining in genomics. Clin Lab Med 2008; 28: 145–166, viii.

    Article  PubMed  Google Scholar 

  26. DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997; 278: 680–686.

    Article  PubMed  CAS  Google Scholar 

  27. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics 2001; 17: 509–519.

    Article  PubMed  CAS  Google Scholar 

  28. Wright GW, Simon RM. A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 2003; 19: 2448–2455.

    Article  PubMed  CAS  Google Scholar 

  29. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001; 98: 5116–5121.

    Article  PubMed  CAS  Google Scholar 

  30. Simon R. Microarray-based expression profiling and informatics. Curr Opin Biotechnol 2008; 19: 26–29.

    Article  PubMed  CAS  Google Scholar 

  31. Jeffery IB, Higgins DG, Culhane AC. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformatics 2006; 7: 359.

    Article  PubMed  Google Scholar 

  32. Slonim DK. From patterns to pathways: gene expression data analysis comes of age. Nat Genet 2002; 32(Suppl): 502–508.

    Article  PubMed  CAS  Google Scholar 

  33. Rhodes DR, Chinnaiyan AM. Integrative analysis of the cancer transcriptome. Nat Genet 2005; 37(Suppl): S31–37.

    Article  PubMed  CAS  Google Scholar 

  34. Rhodes DR et al. Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 2002; 62: 4427–4433.

    PubMed  CAS  Google Scholar 

  35. Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 2008; 5: e184.

    Article  PubMed  Google Scholar 

  36. Han JD. Understanding biological functions through molecular networks. Cell Res 2008; 18: 224–237.

    Article  PubMed  CAS  Google Scholar 

  37. Segal E, Friedman N, Koller D, Regev A. A module map showing conditional activity of expression modules in cancer. Nat Genet 2004; 36: 1090–1098.

    Article  PubMed  CAS  Google Scholar 

  38. Yang HH, Hu Y, Buetow KH, Lee MP. A computational approach to measuring coherence of gene expression in pathways. Genomics 2004; 84: 211–217.

    Article  PubMed  CAS  Google Scholar 

  39. Harris MA et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 2004; 32: D258–D261.

    Article  PubMed  CAS  Google Scholar 

  40. Kanehisa M et al. The KEGG resource for deciphering the genome. Nucleic Acids Res 2004; 32: D277–D280.

    Article  PubMed  CAS  Google Scholar 

  41. Salomonis N et al. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics 2007; 8: 217.

    Article  PubMed  Google Scholar 

  42. Mootha VK et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003; 34: 267–273.

    Article  PubMed  CAS  Google Scholar 

  43. Yu K, Ganesan K, Miller LD, Tan P. A modular analysis of breast cancer reveals a novel low-grade molecular signature in estrogen receptor-positive tumors. Clin Cancer Res 2006; 12: 3288–3296.

    Article  PubMed  CAS  Google Scholar 

  44. Ihmels J et al. Revealing modular organization in the yeast transcriptional network. Nat Genet 2002; 31: 370–377.

    PubMed  CAS  Google Scholar 

  45. Chuaqui RF et al. Post-analysis follow-up and validation of microarray experiments. Nat Genet 2002; 32(Suppl): 509–514.

    Article  PubMed  CAS  Google Scholar 

  46. Quackenbush J. Microarray analysis and tumor classification. N Engl J Med 2006; 354: 2463–2472.

    Article  PubMed  CAS  Google Scholar 

  47. Nevins JR, Potti A. Mining gene expression profiles: expression signatures as cancer phenotypes. Nat Rev Genet 2007; 8: 601–609.

    Article  PubMed  CAS  Google Scholar 

  48. Sotiriou C, Piccart MJ. Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer 2007; 7: 545–553.

    Article  PubMed  CAS  Google Scholar 

  49. Perou CM et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747–752.

    Article  PubMed  CAS  Google Scholar 

  50. Chen X et al. Gene expression patterns in human liver cancers. Mol Biol Cell 2002; 13: 1929–1939.

    Article  PubMed  CAS  Google Scholar 

  51. Hortobagyi GN. Trastuzumab in the treatment of breast cancer. N Engl J Med 2005; 353: 1734–1736.

    Article  PubMed  CAS  Google Scholar 

  52. Paez JG et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004; 304: 1497–1500.

    Article  PubMed  CAS  Google Scholar 

  53. Chung CH, Bernard PS, Perou CM. Molecular portraits and the family tree of cancer. Nat Genet 2002; 32(Suppl): 533–540.

    Article  PubMed  CAS  Google Scholar 

  54. Hayes DN et al. Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. J Clin Oncol 2006; 24: 5079–5090.

    Article  PubMed  CAS  Google Scholar 

  55. Hillen HF. Unknown primary tumours. Postgrad Med J 2000; 76: 690–693.

    Article  PubMed  CAS  Google Scholar 

  56. Su AI et al. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 2001; 61: 7388–7393.

    PubMed  CAS  Google Scholar 

  57. Rhodes DR et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 2004; 101: 9309–9314.

    Article  PubMed  CAS  Google Scholar 

  58. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet 2003; 33: 49–54.

    Article  PubMed  CAS  Google Scholar 

  59. Wirapati P et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 2008; 10: R65.

    Article  PubMed  Google Scholar 

  60. Shedden K et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 2008; 14: 822–827.

    Article  PubMed  CAS  Google Scholar 

  61. Staunton JE et al. Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci USA 2001; 98: 10787–10792.

    Article  PubMed  CAS  Google Scholar 

  62. Potti A et al. Genomic signatures to guide the use of chemotherapeutics. Nat Med 2006; 12: 1294–1300.

    Article  PubMed  CAS  Google Scholar 

  63. Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 2004; 3: 179–196.

    Article  PubMed  CAS  Google Scholar 

  64. Kitano H. Biological robustness. Nat Rev Genet 2004; 5: 826–837.

    Article  PubMed  CAS  Google Scholar 

  65. Reymann S, Borlak J. Transcription profiling of lung adenocarcinomas of c-myc-transgenic mice: identification of the c-myc regulatory gene network. BMC Syst Biol 2008; 2: 46.

    Article  PubMed  Google Scholar 

  66. Whitfield ML, George LK, Grant GD, Perou CM. Common markers of proliferation. Nat Rev Cancer 2006; 6: 99–106.

    Article  PubMed  CAS  Google Scholar 

  67. Ioannidis JP. Microarrays and molecular research: noise discovery? Lancet 2005; 365: 454–455.

    PubMed  Google Scholar 

  68. Futreal PA et al. A census of human cancer genes. Nat Rev Cancer 2004; 4: 177–183.

    Article  PubMed  CAS  Google Scholar 

  69. Vogelstein B, Kinzler KW. Cancer genes and the pathways they control. Nat Med 2004; 10: 789–799.

    Article  PubMed  CAS  Google Scholar 

  70. Lahav G et al. Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat Genet 2004; 36: 147–150.

    Article  PubMed  CAS  Google Scholar 

  71. Kim E, Giese A, Deppert W. Wild-type p53 in cancer cells: When a guardian turns into a blackguard. Biochem Pharmacol 2009; 77 (1): 11–20.

    Google Scholar 

  72. Stegmaier K et al. Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nat Genet 2004; 36: 257–263.

    Article  PubMed  CAS  Google Scholar 

  73. Stegmaier K et al. Gefitinib induces myeloid differentiation of acute myeloid leukemia. Blood 2005; 106: 2841–2848.

    Article  PubMed  CAS  Google Scholar 

  74. Stegmaier K et al. Signature-based small molecule screening identifies cytosine arabinoside as an EWS/FLI modulator in Ewing sarcoma. PLoS Med 2007; 4: e122.

    Article  PubMed  Google Scholar 

  75. Lamb J. The Connectivity Map: a new tool for biomedical research. Nat Rev Cancer 2007; 7: 54–60.

    Article  PubMed  CAS  Google Scholar 

  76. Wei G et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell 2006; 10: 331–342.

    Article  PubMed  CAS  Google Scholar 

  77. Hieronymus H et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell 2006; 10: 321–330.

    Article  PubMed  CAS  Google Scholar 

  78. Wong DJ et al. Revealing targeted therapy for human cancer by gene module maps. Cancer Res 2008; 68: 369–378.

    Article  PubMed  CAS  Google Scholar 

  79. Brenner S et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat Biotechnol 2000; 18: 630–634.

    Article  PubMed  CAS  Google Scholar 

  80. Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol 2008; 26: 1135–1145.

    Article  PubMed  CAS  Google Scholar 

  81. Liu F et al. Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates. BMC Genomics 2007; 8: 153.

    Article  PubMed  CAS  Google Scholar 

  82. Coppee JY. Do DNA microarrays have their future behind them? Microbes Infect 2008; 10 (9): 1067–1071.

    Google Scholar 

  83. Koscielny S. Critical review of microarray-based prognostic tests and trials in breast cancer. Curr Opin Obstet Gynecol 2008; 20: 47–50.

    Article  PubMed  Google Scholar 

  84. Abdullah-Sayani A, Bueno-de-Mesquita JM, van de Vijver MJ. Technology Insight: tuning into the genetic orchestra using microarrays--limitations of DNA microarrays in clinical practice. Nat Clin Pract Oncol 2006; 3: 501–516.

    Article  PubMed  CAS  Google Scholar 

  85. Wilson CA, Dering J. Recent translational research: microarray expression profiling of breast cancer--beyond classification and prognostic markers? Breast Cancer Res 2004; 6: 192–200.

    Article  PubMed  Google Scholar 

  86. Gallegos Ruiz MI et al. Integration of gene dosage and gene expression in non-small cell lung cancer, identification of HSP90 as potential target. PLoS ONE 2008; 3: e0001722.

    Article  PubMed  Google Scholar 

  87. Adler AS et al. Genetic regulators of large-scale transcriptional signatures in cancer. Nat Genet 2006; 38: 421–430.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Orthopaedic Surgery, University of Tennessee Health Science Center, Memphis, TN 38163, USA

    Jian Yan & Weikuan Gu

Authors
  1. Jian Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weikuan Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weikuan Gu .

Editor information

Editors and Affiliations

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2009 Springer Science+Business Media, LLC

    About this chapter

    Cite this chapter

    Yan, J., Gu, W. (2009). Gene Expression Microarrays in Cancer Research. In: Lu, Y., Mahato, R. (eds) Pharmaceutical Perspectives of Cancer Therapeutics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0131-6_20

    Download citation

    Publish with us

    Policies and ethics

    Societies and partnerships

    Search

    Navigation