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Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation
Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possibl...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924183/ https://www.ncbi.nlm.nih.gov/pubmed/33672625 http://dx.doi.org/10.3390/ijms22042102 |
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author | Songia, Paola Chiesa, Mattia Alfieri, Valentina Massaiu, Ilaria Moschetta, Donato Myasoedova, Veronika Valerio, Vincenza Fusini, Laura Gripari, Paola Zanobini, Marco Poggio, Paolo |
author_facet | Songia, Paola Chiesa, Mattia Alfieri, Valentina Massaiu, Ilaria Moschetta, Donato Myasoedova, Veronika Valerio, Vincenza Fusini, Laura Gripari, Paola Zanobini, Marco Poggio, Paolo |
author_sort | Songia, Paola |
collection | PubMed |
description | Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (p ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification. |
format | Online Article Text |
id | pubmed-7924183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79241832021-03-03 Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation Songia, Paola Chiesa, Mattia Alfieri, Valentina Massaiu, Ilaria Moschetta, Donato Myasoedova, Veronika Valerio, Vincenza Fusini, Laura Gripari, Paola Zanobini, Marco Poggio, Paolo Int J Mol Sci Article Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (p ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification. MDPI 2021-02-20 /pmc/articles/PMC7924183/ /pubmed/33672625 http://dx.doi.org/10.3390/ijms22042102 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Songia, Paola Chiesa, Mattia Alfieri, Valentina Massaiu, Ilaria Moschetta, Donato Myasoedova, Veronika Valerio, Vincenza Fusini, Laura Gripari, Paola Zanobini, Marco Poggio, Paolo Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation |
title | Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation |
title_full | Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation |
title_fullStr | Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation |
title_full_unstemmed | Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation |
title_short | Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation |
title_sort | putative circulating micrornas are able to identify patients with mitral valve prolapse and severe regurgitation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924183/ https://www.ncbi.nlm.nih.gov/pubmed/33672625 http://dx.doi.org/10.3390/ijms22042102 |
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