Abstract
MicroRNAs (miRNAs) are emerging as key regulators of complex biological processes in several cardiovascular diseases, including atrial fibrillation (AF). Reverse transcription-quantitative polymerase chain reaction is a powerful technique to quantitatively assess miRNA expression profile, but reliable results depend on proper data normalization by suitable reference genes. Despite the increasing number of studies assessing miRNAs in cardiac disease, no consensus on the best reference genes has been reached. This work aims to assess reference genes stability in human cardiac tissue with a focus on AF investigation. We evaluated the stability of five reference genes (U6, SNORD48, SNORD44, miR-16, and 5S) in atrial tissue samples from eighteen cardiac-surgery patients in sinus rhythm and AF. Stability was quantified by combining BestKeeper, delta-Cq, GeNorm, and NormFinder statistical tools. All methods assessed SNORD48 as the best and U6 as the worst reference gene. Applications of different normalization strategies significantly impacted miRNA expression profiles in the study population. Our results point out the necessity of a consensus on data normalization in AF studies to avoid the emergence of divergent biological conclusions.
Highlights
The regulatory function of miRNAs in cardiac disease and atrial fibrillation (AF) supports their utilization as prognostic and predictive biomarkers as well as therapeutic targets
Correlation analysis was performed between each pair of reference genes and between each reference gene and the BestKeeper Index (BKI)
In the representative example of miR-499a-5p, we showed that normalization by the best reference gene pointed out differences in the expression levels between AF and sinus rhythm (SR) patients, which were lost by applying the wrong normalization strategy
Summary
The regulatory function of miRNAs in cardiac disease and AF supports their utilization as prognostic and predictive biomarkers as well as therapeutic targets. This requires, a reliable and quantitative assessment of miRNA expression. Normalization is aimed to differentiate true biological variations, explaining the investigated phenotype, from non-specific experimentally-induced alterations. Factors, such as sample collection and preservation, amount of raw material, enzyme efficiency, RNA integrity, can artefactually alter expression levels. To date normalization by one or a set of internal reference genes is generally accepted to Department of Physics, University of Trento, Trento, Italy. Healthcare Research and Innovation Program (IRCS-PAT), Bruno Kessler Foundation, Trento, Italy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
