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

机译:逆转录定量聚合酶链反应:基于RIN的算法的描述,用于精确数据标准化

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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.
机译:背景逆转录定量聚合酶链反应(RT-qPCR)是用于mRNA定量的金标准技术,但是需要适当的标准化才能获得可靠的数据。已经提出了对准确定量的RNA进行标准化的方法,是体内活检最可靠的方法。但是,这种方法不能纠正RNA完整性的差异。结果在本研究中,我们评估了RNA降解对9种基因(18S,ACTB,ATUB,B2M,GAPDH,HPRT,POLR2L,PSMB6)的相对表达定量的影响和RPLP0)涵盖了广泛的表达范围。我们的结果表明,将RT-qPCR数据标准化为总RNA时,RNA降解可能会在基因表达测量中引入高达100%的误差。为了获得更高的分辨率,以解决降解样品中转录水平的细微差异,我们通过开发一种补偿RNA完整性损失的校正算法,改进了此归一化方法。这种方法使我们可以实现更高的精度,因为定量测量的平均误差降低到了8%。最后,我们将归一化策略应用于104例直肠癌活检中EGFR,HER2和HER3的定量。综上所述,我们的数据表明通过考虑到RNA降解还可以对基因表达进行归一化,从而使样品比较更加可靠。结论我们开发了一种新的RT-qPCR数据归一化方法,可以补偿RNA完整性的损失,因此可以得到准确的基因人活组织检查中的表达定量。

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