Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets
Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets
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DOI：
10.1158/0008-5472.can-04-0496
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发表时间：
2004-08-01
期刊：
影响因子：
11.2
通讯作者：
Orntoft, TF
中科院分区：
文献类型：
--
作者：
Andersen, CL;Jensen, JL;Orntoft, TF
准确的标准化是正确测量基因表达的绝对先决条件。对于定量实时逆转录-PCR（RT-PCR），最常用的标准化策略涉及标准化到单个组成型表达的对照基因。然而，近年来，已经清楚的是，没有单一基因在所有细胞类型和所有实验条件下组成型表达，这意味着在每次实验之前必须验证预期对照基因的表达稳定性。我们概述了一种新颖的，创新的，强大的策略，以确定稳定表达的基因之间的一组候选标准化基因。该策略植根于基因表达的数学模型，该模型不仅能够估计候选标准化基因的总体变化，而且能够估计样本集的样本子组之间的变化。值得注意的是，该策略为估计的表达变异提供了直接测量，使用户能够评估使用基因时引入的系统误差。在与先前发表的策略进行的并排比较中，我们基于模型的方法以更稳健的方式进行，并且对候选标准化基因的共调节表现出较低的敏感性。我们使用基于模型的策略来识别适合于标准化来自结肠癌和膀胱癌的定量RT-PCR数据的基因。这些基因是用于结肠的UBC、GAPD和TPT 1，以及用于膀胱的HSPCB、TEGT和ATP 5 B。所提出的策略可以应用于任何类型的实验设计，以评估任何归一化基因候选的适用性，并应允许更可靠的RT-PCR数据的归一化。
Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data.