Abstract
MicroRNAs (miRNAs), a class of small non-coding RNAs that modulate gene expression at the post-transcriptional level, are attractive targets in many academic and diagnostic applications. Among them, assessing miRNA biomarkers in minimally invasive liquid biopsies was shown to be a promising tool for managing diseases, particularly cancer. The initial screening of disease-relevant transcripts is often performed by high-throughput next-generation sequencing (NGS), in here RNA sequencing (RNA-Seq). After complex processing of small RNA-Seq data, differential gene expression analysis is performed to evaluate miRNA biomarker signatures. To ensure experimental validity, biomarker candidates are commonly validated by an orthogonal technology such as reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). This chapter outlines in detail the material and methods one can apply to reproducibly identify miRNA biomarker signatures from blood total RNA. After screening miRNA profiles by small RNA-Seq, resulting data is validated in compliance with the “Minimum Information for Publication of Quantitative Real-Time PCR Experiments” (MIQE) guidelines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buschmann D, Haberberger A, Kirchner B et al (2016) Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow. Nucleic Acids Res 44:5995–6018. https://doi.org/10.1093/nar/gkw545
Pfaffl MW (2013) Transcriptional biomarkers. Methods 59:1–2. https://doi.org/10.1016/j.ymeth.2012.12.011
Strimbu K, Tavel JA (2010) What are biomarkers? Curr Opin HIV AIDS 5(6):463–466. https://doi.org/10.1097/COH.0b013e32833ed177
Wang J, Chen J, Sen S (2016) MicroRNA as biomarkers and diagnostics. J Cell Physiol 231(1):25–30. https://doi.org/10.1002/jcp.25056
Ghai V, Wang K (2016) Recent progress toward the use of circulating microRNAs as clinical biomarkers. Arch Toxicol 90(12):2959–2978. https://doi.org/10.1007/s00204-016-1828-2
Arneth B (2018) Update on the types and usage of liquid biopsies in the clinical setting: a systematic review. BMC Cancer 18(1):527. https://doi.org/10.1186/s12885-018-4433-3
Reithmair M, Buschmann D, Marte M et al (2017) Cellular and extracellular miRNAs are blood-compartment-specific diagnostic targets in sepsis. J Cell Mol Med 21:2403. https://doi.org/10.1111/jcmm.13162
Gibson U, Heid A, Williams P (1996) A novel method for real time quantitative RT-PCR. Genome Res 6:995–1001
Morrison T, Weiss J, Wittwer C (1998) Quantification of low-copy transcripts by continuous SYBR Green I monitoring during amplification. BioTechniques 24:954–962
Arya M, Shergill IS, Williamson M et al (2005) Basic principles of real-time quantitative PCR. Expert Rev Mol Diagn 5:209–219. https://doi.org/10.1586/14737159.5.2.209
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25(4):402–408. https://doi.org/10.1006/meth.2001.1262
Bustin SA, Benes V, Garson JA et al (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611–622. https://doi.org/10.1373/clinchem.2008.112797
R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
Schroeder A, Mueller O, Stocker S et al (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7:3. https://doi.org/10.1186/1471-2199-7-3
Kong Y (2011) Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics 98(2):152–153. https://doi.org/10.1016/j.ygeno.2011.05.009
Andrews S (2010) FastQC: a quality control tool for high throughput sequence data
Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42(Database issue):D68–D73. https://doi.org/10.1093/nar/gkt1181
Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25. https://doi.org/10.1186/gb-2009-10-3-r25
Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079. https://doi.org/10.1093/bioinformatics/btp352
Love MI, Huber W et al (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8
Vandesompele J, De Preter K, Pattyn F et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:RESEARCH0034
Andersen CL, Jensen JL, Orntoft TF (2004) 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. Cancer Res 64(15):5245–5250. https://doi.org/10.1158/0008-5472.CAN-04-0496
Pfaffl MW, Tichopad A, Prgomet C et al (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—excel-based tool using pair-wise correlations. Biotechnol Lett 26(6):509–515
Lu J, Getz G, Miska EA et al (2005) MicroRNA expression profiles classify human cancers. Nature 435:834–838. https://doi.org/10.1038/nature03702
Volinia S, Calin GA, Liu CG et al (2006) A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A 103:2257–2261. https://doi.org/10.1073/pnas.0510565103
Burns MJ, Nixon GJ, Foy CA (2005) Standardisation of data from real-time quantitative PCR methods - evaluation of outliers and comparison of calibration curves. BMC Biotechnol 5:31. https://doi.org/10.1186/1472-6750-5-31
Komsta L (2011) Outliers: tests for outliers, R package version 0.14. https://CRAN.R-project.org/package=outliers
Millard SP (2013) EnvStats: an R package for environmental statistics. Springer, New York
Kaur H, Arora A, Wengel J (2006) Thermodynamic, counterion, and hydration effects for the incorporation of locked nucleic acid nucleotides into DNA duplexes. Biochemistry 45:7347–7355. https://doi.org/10.1021/bi060307w
Schrader C, Schielke A, Ellerbroek L et al (2012) PCR inhibitors - occurrence, properties and removal. J Appl Microbiol 113:1014–1026. https://doi.org/10.1111/j.1365-2672.2012.05384.x
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Mussack, V., Hermann, S., Buschmann, D., Kirchner, B., Pfaffl, M.W. (2020). MIQE-Compliant Validation of MicroRNA Biomarker Signatures Established by Small RNA Sequencing. In: Biassoni, R., Raso, A. (eds) Quantitative Real-Time PCR. Methods in Molecular Biology, vol 2065. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9833-3_3
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9833-3_3
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-9832-6
Online ISBN: 978-1-4939-9833-3
eBook Packages: Springer Protocols