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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/12/12/1628 |
| _version_ | 1827641572665065472 |
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| author | Alice Giotta Lucifero Sabino Luzzi |
| author_facet | Alice Giotta Lucifero Sabino Luzzi |
| author_sort | Alice Giotta Lucifero |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T17:16:45Z |
| format | Article |
| id | doaj.art-ecf38abafbb840b38052ed23419a4bda |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-09T17:16:45Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-ecf38abafbb840b38052ed23419a4bda2023-11-24T13:39:00ZengMDPI AGBrain Sciences2076-34252022-11-011212162810.3390/brainsci12121628Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target GenesAlice Giotta Lucifero0Sabino Luzzi1Neurosurgery Unit, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, ItalyNeurosurgery Unit, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, ItalyIntroduction: 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.https://www.mdpi.com/2076-3425/12/12/1628artificial intelligencebrain arteriovenous malformations (AVM)hemorrhagic strokemachine learningmicroRNAnon-coding RNA |
| spellingShingle | Alice Giotta Lucifero Sabino Luzzi Brain AVMs-Related microRNAs: Machine Learning Algorithm for Expression Profiles of Target Genes Brain Sciences artificial intelligence brain arteriovenous malformations (AVM) hemorrhagic stroke machine learning microRNA non-coding RNA |
| 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 | artificial intelligence brain arteriovenous malformations (AVM) hemorrhagic stroke machine learning microRNA non-coding RNA |
| url | https://www.mdpi.com/2076-3425/12/12/1628 |
| work_keys_str_mv | AT alicegiottalucifero brainavmsrelatedmicrornasmachinelearningalgorithmforexpressionprofilesoftargetgenes AT sabinoluzzi brainavmsrelatedmicrornasmachinelearningalgorithmforexpressionprofilesoftargetgenes |