A Strategy to Find Suitable Reference Genes for miRNA Quantitative PCR Analysis and Its Application to Cervical Specimens

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Abstract

miRNAs represent an emerging class of promising biomarkers for cancer diagnostics. To perform reliable miRNA expression analysis using quantitative PCR, adequate data normalization is essential to remove nonbiological, technical variations. Ideal reference genes should be biologically stable and reduce technical variability of miRNA expression analysis. Herein is a new strategy for the identification and evaluation of reference genes that can be applied for miRNA-based diagnostic tests without entailing excessive additional experiments. We analyzed the expression of 11 carefully selected candidate reference genes in different types of cervical specimens [ie, tissues, scrapes, and self-collected cervicovaginal specimens (self-samples)]. To identify the biologically most stable reference genes, three commonly used algorithms (GeNorm, NormFinder, and BestKeeper) were combined. Signal-to-noise ratios and P values between control and disease groups were calculated to validate the reduction in technical variability on expression analysis of two marker miRNAs. miR-423 was identified as a suitable reference gene for all sample types, to be used in combination with RNU24 in cervical tissues, RNU43 in scrapes, and miR-30b in self-samples. These findings demonstrate that the choice of reference genes may differ between different types of specimens, even when originating from the same anatomical source. More important, it is shown that adequate normalization increases the signal-to-noise ratio, which is not observed when normalizing to commonly used reference genes.

Original languageEnglish
Pages (from-to)625-637
Number of pages13
JournalThe Journal of molecular diagnostics
Volume19
Issue number5
DOIs
Publication statusPublished - Sep 2017

Cite this

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title = "A Strategy to Find Suitable Reference Genes for miRNA Quantitative PCR Analysis and Its Application to Cervical Specimens",
abstract = "miRNAs represent an emerging class of promising biomarkers for cancer diagnostics. To perform reliable miRNA expression analysis using quantitative PCR, adequate data normalization is essential to remove nonbiological, technical variations. Ideal reference genes should be biologically stable and reduce technical variability of miRNA expression analysis. Herein is a new strategy for the identification and evaluation of reference genes that can be applied for miRNA-based diagnostic tests without entailing excessive additional experiments. We analyzed the expression of 11 carefully selected candidate reference genes in different types of cervical specimens [ie, tissues, scrapes, and self-collected cervicovaginal specimens (self-samples)]. To identify the biologically most stable reference genes, three commonly used algorithms (GeNorm, NormFinder, and BestKeeper) were combined. Signal-to-noise ratios and P values between control and disease groups were calculated to validate the reduction in technical variability on expression analysis of two marker miRNAs. miR-423 was identified as a suitable reference gene for all sample types, to be used in combination with RNU24 in cervical tissues, RNU43 in scrapes, and miR-30b in self-samples. These findings demonstrate that the choice of reference genes may differ between different types of specimens, even when originating from the same anatomical source. More important, it is shown that adequate normalization increases the signal-to-noise ratio, which is not observed when normalizing to commonly used reference genes.",
keywords = "Journal Article",
author = "Iris Babion and Snoek, {Barbara C} and {van de Wiel}, {Mark A} and Wilting, {Saskia M} and Steenbergen, {Renske D M}",
note = "Copyright {\circledC} 2017 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.",
year = "2017",
month = "9",
doi = "10.1016/j.jmoldx.2017.04.010",
language = "English",
volume = "19",
pages = "625--637",
journal = "The Journal of molecular diagnostics",
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publisher = "Association of Molecular Pathology",
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AU - Babion, Iris

AU - Snoek, Barbara C

AU - van de Wiel, Mark A

AU - Wilting, Saskia M

AU - Steenbergen, Renske D M

N1 - Copyright © 2017 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

PY - 2017/9

Y1 - 2017/9

N2 - miRNAs represent an emerging class of promising biomarkers for cancer diagnostics. To perform reliable miRNA expression analysis using quantitative PCR, adequate data normalization is essential to remove nonbiological, technical variations. Ideal reference genes should be biologically stable and reduce technical variability of miRNA expression analysis. Herein is a new strategy for the identification and evaluation of reference genes that can be applied for miRNA-based diagnostic tests without entailing excessive additional experiments. We analyzed the expression of 11 carefully selected candidate reference genes in different types of cervical specimens [ie, tissues, scrapes, and self-collected cervicovaginal specimens (self-samples)]. To identify the biologically most stable reference genes, three commonly used algorithms (GeNorm, NormFinder, and BestKeeper) were combined. Signal-to-noise ratios and P values between control and disease groups were calculated to validate the reduction in technical variability on expression analysis of two marker miRNAs. miR-423 was identified as a suitable reference gene for all sample types, to be used in combination with RNU24 in cervical tissues, RNU43 in scrapes, and miR-30b in self-samples. These findings demonstrate that the choice of reference genes may differ between different types of specimens, even when originating from the same anatomical source. More important, it is shown that adequate normalization increases the signal-to-noise ratio, which is not observed when normalizing to commonly used reference genes.

AB - miRNAs represent an emerging class of promising biomarkers for cancer diagnostics. To perform reliable miRNA expression analysis using quantitative PCR, adequate data normalization is essential to remove nonbiological, technical variations. Ideal reference genes should be biologically stable and reduce technical variability of miRNA expression analysis. Herein is a new strategy for the identification and evaluation of reference genes that can be applied for miRNA-based diagnostic tests without entailing excessive additional experiments. We analyzed the expression of 11 carefully selected candidate reference genes in different types of cervical specimens [ie, tissues, scrapes, and self-collected cervicovaginal specimens (self-samples)]. To identify the biologically most stable reference genes, three commonly used algorithms (GeNorm, NormFinder, and BestKeeper) were combined. Signal-to-noise ratios and P values between control and disease groups were calculated to validate the reduction in technical variability on expression analysis of two marker miRNAs. miR-423 was identified as a suitable reference gene for all sample types, to be used in combination with RNU24 in cervical tissues, RNU43 in scrapes, and miR-30b in self-samples. These findings demonstrate that the choice of reference genes may differ between different types of specimens, even when originating from the same anatomical source. More important, it is shown that adequate normalization increases the signal-to-noise ratio, which is not observed when normalizing to commonly used reference genes.

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DO - 10.1016/j.jmoldx.2017.04.010

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