Expectations, validity, and reality in gene expression profiling

J Clin Epidemiol. 2010 Sep;63(9):950-9. doi: 10.1016/j.jclinepi.2010.02.018. Epub 2010 Jun 25.

Abstract

Objective: To provide a critical overview of gene expression profiling methodology and discuss areas of future development.

Results: Gene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders. However, translating this technology into genomic medicine for use in diagnosis and prognosis faces many challenges. In addition, gene expression profile analysis is frequently controversial, because its conclusions often lack reproducibility and claims of effective dissemination into translational medicine have, in some cases, been remarkably unjustified. In the last decade, a large number of methodological and technical solutions have been offered to overcome the challenges.

Study design and setting: We consider the strengths, limitations, and appropriate applications of gene expression profiling techniques, with particular reference to the clinical relevance.

Conclusion: Some studies have demonstrated the ability and clinical utility of gene expression profiling for use as diagnostic, prognostic, and predictive molecular markers. The challenges of gene expression profiling lie with the standardization of analytic approaches and the evaluation of the clinical merit in broader heterogeneous populations by prospective clinical trials.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Biomarkers
  • Clinical Trials as Topic
  • Databases, Genetic
  • Gene Expression Profiling / instrumentation
  • Gene Expression Profiling / methods*
  • Humans
  • Information Storage and Retrieval / methods
  • Microarray Analysis / methods*
  • Neoplasms / diagnosis*
  • Neoplasms / genetics
  • Reproducibility of Results
  • Transcription, Genetic / genetics*

Substances

  • Biomarkers