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…

By Claus L. Andersen, J. L. Jensen, Torben F. Ørntoft. Cancer Research, 2004

6,774 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a7e1ddf11a5cfcbccebfabd6f67e88268b3138a7

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Citations in the corpus

Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues. (2008, methodology): “Following the modified z-score filtering, each normalization candidate was transformed to a quantity as outlined by the authors of geNorm and NormFinder (Vandesompele et al. 2002; Andersen et al. 2004), creating a list of targets ranked accordingly relative to a stability value (Supplemental Table 1”
Validation of Candidate Reference Genes for the Accurate Normalization of Real-Time Quantitative RT-PCR Data in Rice During Seed Development (2010, methodology): “Meanwhile, several algorithms, such as geNorm and Norm Finder, were recently developed to identify the stablest reference gene(s) from a panel of potential genes or candidate genes under a given set of experimental conditions (Andersen et al. 2004; Vandesompele et al. 2002).”
Reference Gene Selection for Insect Expression Studies Using Quantitative Real-Time PCR: The Head of the Honeybee, Apis mellifera, After a Bacterial Challenge (2008, methodology): “…compare the transcription level of the selected genes across different samples and experimental manipulation, the average Ct-value of each duplicate reaction was converted to raw data for subsequent analysis with the geNorm (Vandesompele et al. 2002) and Normfinder (Andersen et al. 2004) programs.”
Genome-Wide Identification and Testing of Superior Reference Genes for Transcript Normalization in Arabidopsis1[w] (2005, background): “…dehydrogenase (GAPDH), elongation factor-1a (EF-1a), polyubiquitin (UBQ), actin (ACT), and a-tubulin and b-tubulin (TUA and TUB, respectively) genes (Goidin et al., 2001; Bustin, 2002; Kim et al., 2003; Andersen et al., 2004; Brunner et al., 2004; Dheda et al., 2004; Radonić et al., 2004).”
Quantitative real-time RT-PCR--a perspective. (2005, background): “This drawback is addressed by another model that takes into consideration not just overall expression variation, but also systematic variation across sample subgroups (Andersen et al. 2004).”
Differential network biology (2012, background): “Although dividing genes into just two categories—housekeeping and other—is an over-simpliﬁcation (certain housekeeping genes have signiﬁcant variance in expression; Andersen et al , 2004), housekeeping genes have nonetheless been a useful concept for several reasons.”
A novel and universal method for microRNA RT-qPCR data normalization (2009, methodology): “All results obtained with geNorm were independently confirmed with the Normfinder algorithm [15] (data not shown).”
Reference genes in real-time PCR (2013, methodology): “First, structure and sequence of candidate genes should be recognized so the designed primers would always amplify the product that will reflect the actual amount of mRNA, both in the samples and in each individual (Andersen et al. 2004).”
Reference genes identified in the silkworm Bombyx mori during metamorphism based on oligonucleotide microarray and confirmed by qRT‐PCR (2008, methodology): “…cycle threshold (CT) values from qRT-PCR were first transformed into quantities by the comparative CT method (Livak & Schmittgen, 2001) and then used in gene expression stability analysis with geNorm 3.4 software (Vandesompele et al., 2002) and the program NormFinder (Andersen et al., 2004).”
RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization (2011, methodology): “We also observed that most of the candidate genes had a slight response to various tumors and to oncolytic viruses (see Additional file 8).”