Reverse transcription-quantitative polymerase chain reaction: description of a RIN-based algorithm for accurate data normalization.

Abstract : BACKGROUND: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the gold standard technique for mRNA quantification, but appropriate normalization is required to obtain reliable data. Normalization to accurately quantitated RNA has been proposed as the most reliable method for in vivo biopsies. However, this approach does not correct differences in RNA integrity. RESULTS: In this study, we evaluated the effect of RNA degradation on the quantification of the relative expression of nine genes (18S, ACTB, ATUB, B2M, GAPDH, HPRT, POLR2L, PSMB6 and RPLP0) that cover a wide expression spectrum. Our results show that RNA degradation could introduce up to 100% error in gene expression measurements when RT-qPCR data were normalized to total RNA. To achieve greater resolution of small differences in transcript levels in degraded samples, we improved this normalization method by developing a corrective algorithm that compensates for the loss of RNA integrity. This approach allowed us to achieve higher accuracy, since the average error for quantitative measurements was reduced to 8%. Finally, we applied our normalization strategy to the quantification of EGFR, HER2 and HER3 in 104 rectal cancer biopsies. Taken together, our data show that normalization of gene expression measurements by taking into account also RNA degradation allows much more reliable sample comparison. CONCLUSION: We developed a new normalization method of RT-qPCR data that compensates for loss of RNA integrity and therefore allows accurate gene expression quantification in human biopsies.
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Soumis le : mercredi 17 juin 2009 - 11:24:39
Dernière modification le : mercredi 3 octobre 2018 - 16:02:18
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Alexandre Ho-Pun-Cheung, Caroline Bascoul-Mollevi, Eric Assénat, Florence Boissière-Michot, Frédéric Bibeau, et al.. Reverse transcription-quantitative polymerase chain reaction: description of a RIN-based algorithm for accurate data normalization.. BMC Molecular Biology, BioMed Central, 2009, 10, pp.31. 〈10.1186/1471-2199-10-31〉. 〈inserm-00396220〉



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