A methodological procedure for evaluating the impact of hemolysis on circulating microRNAs

Pizzamiglio,Sara; Zanutto,Susanna; Ciniselli,Chiara M.; Belfiore,Antonino; Bottelli,Stefano; Gariboldi,Manuela; Verderio,Paolo

doi:10.3892/ol.2016.5452

A methodological procedure for evaluating the impact of hemolysis on circulating microRNAs

Authors:
- Sara Pizzamiglio
- Susanna Zanutto
- Chiara M. Ciniselli
- Antonino Belfiore
- Stefano Bottelli
- Manuela Gariboldi
- Paolo Verderio
View Affiliations

Affiliations: Unit of Medical Statistics, Biometry and Bioinformatics, Scientific Institutes for Research and Treatment Foundation ʻNational Cancer Instituteʼ, I‑20133 Milan, Italy, Department of Experimental Oncology and Molecular Medicine, Scientific Institutes for Research and Treatment Foundation ʻNational Cancer Instituteʼ, I‑20133 Milan, Italy, Laboratory of Experimental Molecular Pathology, Department of Pathology, Scientific Institutes for Research and Treatment Foundation ʻNational Cancer Instituteʼ, I‑20133 Milan, Italy
Published online on: November 30, 2016 https://doi.org/10.3892/ol.2016.5452
Pages: 315-320

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Circulating microRNAs (miRNAs) are promising non‑invasive biomarkers whose expression may be affected by confounding factors, including hemolysis, that should be considered in studies of miRNA discovery. The present study proposes a methodology for evaluating the impact of hemolysis on the expression of miRNAs. An experiment of in vitro controlled hemolysis was designed for assessing if changes in the expression of eight miRNAs observed to be circulating in plasma may be associated with hemolysis, and also to estimate the level of red blood cell (RBC) contamination in plasma samples where the expression of these miRNAs will be measured. It was confirmed that four miRNAs, miR‑16, miR‑92a, miR‑451 and miR‑486, known to be present in blood cells, were influenced by contamination of RBCs. Furthermore, it was demonstrated that miR‑378 and miR‑30c are hemolysis‑independent and that the expression of miR‑320 and miR‑324‑3p was associated with the level of RBC contamination. This procedure is proposed as a tool for the evaluation of the influence of hemolysis on candidate circulating miRNA biomarkers prior to their analysis in plasma samples.

View Figures

View References

1	Schwarzenbach H, Nishida N, Calin GA and Pantel K: Clinical relevance of circulating cell-free microRNAs in cancer. Nat Rev Clin Oncol. 11:145–156. 2014. View Article : Google Scholar : PubMed/NCBI
2	Sozzi G, Boeri M, Rossi M, Verri C, Suatoni P, Bravi F, Roz L, Conte D, Grassi M, Sverzellati N, et al: Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study. J Clin Oncol. 32:768–773. 2014. View Article : Google Scholar : PubMed/NCBI
3	Zanutto S, Pizzamiglio S, Ghilotti M, Bertan C, Ravagnani F, Perrone F, Leo E, Pilotti S, Verderio P, Gariboldi M and Pierotti MA: Circulating miR-378 in plasma: A reliable, hemolysis-independent biomarker for colorectal cancer. Br J Cancer. 110:1001–1007. 2014. View Article : Google Scholar : PubMed/NCBI
4	Pritchard CC, Kroh E, Wood B, Arroyo JD, Dougherty KJ, Miyaji MM, Tait JF and Tewari M: Blood cell origin of circulating microRNAs: A cautionary note for cancer biomarker studies. Cancer Prev Res (Phila). 5:492–497. 2012. View Article : Google Scholar : PubMed/NCBI
5	Kirschner MB, Kao SC, Edelman JJ, Armstrong NJ, Vallely MP, van Zandwijk N and Reid G: Hemolysis during sample preparation alters microRNA content of plasma. PLoS One. 6:e241452011. View Article : Google Scholar : PubMed/NCBI
6	Kirschner MB, Edelman JJ, Kao SC, Vallely MP, van Zandwijk N and Reid G: The impact of hemolysis on cell-free microRNA biomarkers. Front Genet. 4:942013. View Article : Google Scholar : PubMed/NCBI
7	Appierto V, Callari M, Cavadini E, Morelli D, Daidone MG and Tiberio P: A lipemia-independent NanoDrop(^{^{^{^{^{^®}}}}})-based score to identify hemolysis in plasma and serum samples. Bioanalysis. 6:1215–1226. 2014. View Article : Google Scholar : PubMed/NCBI
8	MacLellan SA, MacAulay C, Lam S and Garnis C: Pre-profiling factors influencing serum microRNA levels. BMC Clin Pathol. 14:272014. View Article : Google Scholar : PubMed/NCBI
9	Verderio P, Bottelli S, Ciniselli CM, Pierotti MA, Gariboldi M and Pizzamiglio S: NqA: An R-based algorithm for the normalization and analysis of microRNA quantitative real-time polymerase chain reaction data. Anal Biochem. 461:7–9. 2014. View Article : Google Scholar : PubMed/NCBI
10	Pizzamiglio S, Bottelli S, Ciniselli CM, Zanutto S, Bertan C, Gariboldi M, Pierotti MA and Verderio P: A normalization strategy for the analysis of plasma microRNA qPCR data in colorectal cancer. Int J Cancer. 134:2016–2018. 2014. View Article : Google Scholar : PubMed/NCBI
11	Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) Method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI
12	Gibbons JD and Chakraborti S: Nonparametric Statistical Inference. 4th. Marcel Dekker; New York: pp. 247–257. 2003
13	Pizzamiglio S, Cossa G, Gatti L, Beretta GL, Corna E, Tinelli S, Verderio P and Perego P: Simultaneous confidence intervals to compare gene expression profiles using ABC transporter TaqMan microfluidic cards. Oncol Rep. 23:853–860. 2010.PubMed/NCBI
14	Pizzamiglio S, Verderio P, Orlando C and Marubini E: Confidence interval for DNA/mRNA concentration by real-time PCR. Int J Biol Markers. 22:232–236. 2007.PubMed/NCBI
15	Yamada A, Cox MA, Gaffney KA, Moreland A, Boland CR and Goel A: Technical factors involved in the measurement of circulating microRNA biomarkers for the detection of colorectal neoplasia. PLoS One. 9:e1124812014. View Article : Google Scholar : PubMed/NCBI