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Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes

Introduction: microRNAs (miRNAs) are a class of non-coding RNAs playing a myriad of important roles in regulating gene expression. Of note, recent work demonstrated a critical role of miRNAs in the genesis and progression of brain arteriovenous malformations (bAVMs). Accordingly, here we examine miR...

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Autores principales: Giotta Lucifero, Alice, Luzzi, Sabino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775264/
https://www.ncbi.nlm.nih.gov/pubmed/36552089
http://dx.doi.org/10.3390/brainsci12121628
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author Giotta Lucifero, Alice
Luzzi, Sabino
author_facet Giotta Lucifero, Alice
Luzzi, Sabino
author_sort Giotta Lucifero, Alice
collection PubMed
description Introduction: microRNAs (miRNAs) are a class of non-coding RNAs playing a myriad of important roles in regulating gene expression. Of note, recent work demonstrated a critical role of miRNAs in the genesis and progression of brain arteriovenous malformations (bAVMs). Accordingly, here we examine miRNA signatures related to bAVMs and associated gene expression. In so doing we expound on the potential prognostic, diagnostic, and therapeutic significance of miRNAs in the clinical management of bAVMs. Methods: A PRISMA-based literature review was performed using PubMed/Medline database with the following search terms: “brain arteriovenous malformations”, “cerebral arteriovenous malformations”, “microRNA”, and “miRNA”. All preclinical and clinical studies written in English, regardless of date, were selected. For our bioinformatic analyses, miRWalk and miRTarBase machine learning algorithms were employed; the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was quired for associated pathways/functions. Results: four studies were ultimately included in the final analyses. Sequencing data consistently revealed the decreased expression of miR-18a in bAVM-endothelial cells, resulting in increased levels of vascular endodermal growth factor (VEGF), Id-1, matrix metalloproteinase, and growth signals. Our analyses also suggest that the downregulation of miR-137 and miR-195* within vascular smooth muscle cells (VSMCs) may foster the activation of inflammation, aberrant angiogenesis, and phenotypic switching. In the peripheral blood, the overexpression of miR-7-5p, miR-629-5p, miR-199a-5p, miR-200b-3p, and let-7b-5p may contribute to endothelial proliferation and nidus development. The machine learning algorithms employed confirmed associations between miRNA-related target networks, vascular rearrangement, and bAVM progression. Conclusion: miRNAs expression appears to be critical in managing bAVMs’ post-transcriptional signals. Targets of microRNAs regulate canonical vascular proliferation and reshaping. Although additional scientific evidence is needed, the identification of bAVM miRNA signatures may facilitate the development of novel prognostic/diagnostic tools and molecular therapies for bAVMs.
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spelling pubmed-97752642022-12-23 Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes Giotta Lucifero, Alice Luzzi, Sabino Brain Sci Review Introduction: microRNAs (miRNAs) are a class of non-coding RNAs playing a myriad of important roles in regulating gene expression. Of note, recent work demonstrated a critical role of miRNAs in the genesis and progression of brain arteriovenous malformations (bAVMs). Accordingly, here we examine miRNA signatures related to bAVMs and associated gene expression. In so doing we expound on the potential prognostic, diagnostic, and therapeutic significance of miRNAs in the clinical management of bAVMs. Methods: A PRISMA-based literature review was performed using PubMed/Medline database with the following search terms: “brain arteriovenous malformations”, “cerebral arteriovenous malformations”, “microRNA”, and “miRNA”. All preclinical and clinical studies written in English, regardless of date, were selected. For our bioinformatic analyses, miRWalk and miRTarBase machine learning algorithms were employed; the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was quired for associated pathways/functions. Results: four studies were ultimately included in the final analyses. Sequencing data consistently revealed the decreased expression of miR-18a in bAVM-endothelial cells, resulting in increased levels of vascular endodermal growth factor (VEGF), Id-1, matrix metalloproteinase, and growth signals. Our analyses also suggest that the downregulation of miR-137 and miR-195* within vascular smooth muscle cells (VSMCs) may foster the activation of inflammation, aberrant angiogenesis, and phenotypic switching. In the peripheral blood, the overexpression of miR-7-5p, miR-629-5p, miR-199a-5p, miR-200b-3p, and let-7b-5p may contribute to endothelial proliferation and nidus development. The machine learning algorithms employed confirmed associations between miRNA-related target networks, vascular rearrangement, and bAVM progression. Conclusion: miRNAs expression appears to be critical in managing bAVMs’ post-transcriptional signals. Targets of microRNAs regulate canonical vascular proliferation and reshaping. Although additional scientific evidence is needed, the identification of bAVM miRNA signatures may facilitate the development of novel prognostic/diagnostic tools and molecular therapies for bAVMs. MDPI 2022-11-28 /pmc/articles/PMC9775264/ /pubmed/36552089 http://dx.doi.org/10.3390/brainsci12121628 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Giotta Lucifero, Alice
Luzzi, Sabino
Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes
title Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes
title_full Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes
title_fullStr Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes
title_full_unstemmed Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes
title_short Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes
title_sort brain avms-related micrornas: machine learning algorithm for expression profiles of target genes
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775264/
https://www.ncbi.nlm.nih.gov/pubmed/36552089
http://dx.doi.org/10.3390/brainsci12121628
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