Home > Technical section > Biology > Molecular biology > RNA study > gene expression profiling
gene expression profiling
Thursday 20 November 2003
Gene expression profiling allow comparison of gene expression between normal and diseased (e.g., cancerous) cells.
Many biologists interpret changes in gene expression levels based on the fold ratio by which it has gone up or down between treatments. However, this is not a statistically valid approach, since it does not take into account the variability of that gene between replicates assigned the same treatment.
A fourfold change in the measured expression level of a gene that varies greatly between samples given the same treatment is probably not significant, whereas a 1.4-fold change in the measured expression of a tightly regulated gene could be very significant.
Biologists embarking on expression studies are strongly advised to consult with biostatisticians before starting work, in order to estimate how much replication is needed to obtain sufficient statistical power.
Features
expression profiling in tumors
Variants
comparative expressed sequence hybridization (CESH)(145066940
References
Nevins JR, Potti A. Mining gene expression profiles: expression signatures as cancer phenotypes. Nat Rev Genet. 2007 Jul 3; PMID: 17607306
Draghici S, Khatri P, Eklund AC, Szallasi Z. Reliability and reproducibility issues in DNA microarray measurements. Trends Genet. 2006 Feb;22(2):101-9. PMID: 16380191
Wadlow R, Ramaswamy S. DNA microarrays in clinical cancer research. Curr Mol Med. 2005 Feb;5(1):111-20. PMID: 15720274
Dracopoli NC. Development of oncology drug response markers using transcription profiling. Curr Mol Med. 2005 Feb;5(1):103-10. PMID: 15720273
Burczynski ME, Oestreicher JL, Cahilly MJ, Mounts DP, Whitley MZ, Speicher LA, Trepicchio WL. Clinical pharmacogenomics and transcriptional profiling in early phase oncology clinical trials. Curr Mol Med. 2005 Feb;5(1):83-102. PMID: 15720272
Hsu B, Cass L, Williams WV. Application of transcriptome analysis to clinical pharmacology studies. Curr Mol Med. 2005 Feb;5(1):65-82. PMID: 15720271
Searfoss GH, Ryan TP, Jolly RA. The role of transcriptome analysis in pre-clinical toxicology. Curr Mol Med. 2005 Feb;5(1):53-64. PMID: 15720270
Bilello JA. The agony and ecstasy of "OMIC" technologies in drug development. Curr Mol Med. 2005 Feb;5(1):39-52. PMID: 15720269
Hu YF, Kaplow J, He Y. From traditional biomarkers to transcriptome analysis in drug development. Curr Mol Med. 2005 Feb;5(1):29-38. PMID: 15720268
Tumor Analysis Best Practices Working Group. Expression profiling—best practices for data generation and interpretation in clinical trials. Nat Rev Genet. 2004 Mar;5(3):229-37. PMID: 14970825
Leung YF, Cavalieri D. Fundamentals of cDNA microarray data analysis. Trends Genet. 2003 Nov;19(11):649-59. PMID: 14585617
Moreau Y, Aerts S, De Moor B, De Strooper B, Dabrowski M. Comparison and meta-analysis of microarray data: from the bench to the computer desk.
Trends Genet. 2003 Oct;19(10):570-7. PMID: 14550631
Smith L, Greenfield A. DNA microarrays and development. Hum Mol Genet. 2003 Apr 2;12(Suppl 1):R1-8. PMID: 12668591
Bustin SA, Dorudi S. The value of microarray techniques for quantitative gene profiling in molecular diagnostics. Trends Mol Med. 2002 Jun;8(6):269-72. PMID: 12067612
Mills JC, Roth KA, Cagan RL, Gordon JI. DNA microarrays and beyond: completing the journey from tissue to cell. Nat Cell Biol. 2001 Aug;3(8):E175-8. Review. Erratum in: Nat Cell Biol 2001 Oct;3(10):943.
PMID: 11483971
Liotta L, Petricoin E. Molecular profiling of human cancer. Nat Rev Genet. 2000 Oct;1(1):48-56. PMID: 11262874
Wooster R. Cancer classification with DNA microarrays is less more? Trends Genet. 2000 Aug;16(8):327-9. PMID: 10904257
Claverie JM. Computational methods for the identification of differential and coordinated gene expression. Hum Mol Genet. 1999;8(10):1821-32. PMID: 10469833