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Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disorder (NDS) with unclear pathophysiology and few therapeutic options. Mutations in SOD1 and C9orf72 are the most common in Asian and Caucasian patients with ALS, respectively. Aberrant (microRNAs) miRNAs fou...

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Autores principales: Cheng, Yang-Fan, Gu, Xiao-Jing, Yang, Tian-Mi, Wei, Qian-Qian, Cao, Bei, Zhang, Yang, Shang, Hui-Fang, Chen, Yong-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951117/
https://www.ncbi.nlm.nih.gov/pubmed/36845651
http://dx.doi.org/10.3389/fnagi.2023.1106497
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author Cheng, Yang-Fan
Gu, Xiao-Jing
Yang, Tian-Mi
Wei, Qian-Qian
Cao, Bei
Zhang, Yang
Shang, Hui-Fang
Chen, Yong-Ping
author_facet Cheng, Yang-Fan
Gu, Xiao-Jing
Yang, Tian-Mi
Wei, Qian-Qian
Cao, Bei
Zhang, Yang
Shang, Hui-Fang
Chen, Yong-Ping
author_sort Cheng, Yang-Fan
collection PubMed
description BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disorder (NDS) with unclear pathophysiology and few therapeutic options. Mutations in SOD1 and C9orf72 are the most common in Asian and Caucasian patients with ALS, respectively. Aberrant (microRNAs) miRNAs found in patients with gene-mutated ALS may be involved in the pathogenesis of gene-specific ALS and sporadic ALS (SALS). The aim of this study was to screen for differentially expressed miRNAs from exosomes in patients with ALS and healthy controls (HCs) and to construct a miRNA-based diagnostic model to classify patients and HCs. METHODS: We compared circulating exosome-derived miRNAs of patients with ALS and HCs using the following two cohorts: a discovery cohort (three patients with SOD1-mutated ALS, three patients with C9orf72-mutated ALS, and three HCs) analyzed by microarray and a validation cohort (16 patients with gene-mutated ALS, 65 patients with SALS, and 61 HCs) confirmed by RT-qPCR. The support vector machine (SVM) model was used to help diagnose ALS using five differentially expressed miRNAs between SALS and HCs. RESULTS: A total of 64 differentially expressed miRNAs in patients with SOD1-mutated ALS and 128 differentially expressed miRNAs in patients with C9orf72-mutated ALS were obtained by microarray compared to HCs. Of these, 11 overlapping dysregulated miRNAs were identified in both groups. Among the 14 top-hit candidate miRNAs validated by RT-qPCR, hsa-miR-34a-3p was specifically downregulated in patients with SOD1-mutated ALS, while hsa-miR-1306-3p was downregulated in ALS patients with both SOD1 and C9orf72 mutations. In addition, hsa-miR-199a-3p and hsa-miR-30b-5p were upregulated significantly in patients with SALS, while hsa-miR-501-3p, hsa-miR-103a-2-5p, and hsa-miR-181d-5p had a trend to be upregulated. The SVM diagnostic model used five miRNAs as features to distinguish ALS from HCs in our cohort with an area under receiver operating characteristic curve (AUC) of 0.80. CONCLUSION: Our study identified aberrant miRNAs from exosomes of SALS and ALS patients with SOD1/C9orf72 mutations and provided additional evidence that aberrant miRNAs were involved in the pathogenesis of ALS regardless of the presence or absence of the gene mutation. The machine learning algorithm had high accuracy in predicting the diagnosis of ALS, shedding light on the foundation for the clinical application of blood tests in the diagnosis of ALS, and revealing the pathological mechanisms of the disease.
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spelling pubmed-99511172023-02-25 Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis Cheng, Yang-Fan Gu, Xiao-Jing Yang, Tian-Mi Wei, Qian-Qian Cao, Bei Zhang, Yang Shang, Hui-Fang Chen, Yong-Ping Front Aging Neurosci Neuroscience BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disorder (NDS) with unclear pathophysiology and few therapeutic options. Mutations in SOD1 and C9orf72 are the most common in Asian and Caucasian patients with ALS, respectively. Aberrant (microRNAs) miRNAs found in patients with gene-mutated ALS may be involved in the pathogenesis of gene-specific ALS and sporadic ALS (SALS). The aim of this study was to screen for differentially expressed miRNAs from exosomes in patients with ALS and healthy controls (HCs) and to construct a miRNA-based diagnostic model to classify patients and HCs. METHODS: We compared circulating exosome-derived miRNAs of patients with ALS and HCs using the following two cohorts: a discovery cohort (three patients with SOD1-mutated ALS, three patients with C9orf72-mutated ALS, and three HCs) analyzed by microarray and a validation cohort (16 patients with gene-mutated ALS, 65 patients with SALS, and 61 HCs) confirmed by RT-qPCR. The support vector machine (SVM) model was used to help diagnose ALS using five differentially expressed miRNAs between SALS and HCs. RESULTS: A total of 64 differentially expressed miRNAs in patients with SOD1-mutated ALS and 128 differentially expressed miRNAs in patients with C9orf72-mutated ALS were obtained by microarray compared to HCs. Of these, 11 overlapping dysregulated miRNAs were identified in both groups. Among the 14 top-hit candidate miRNAs validated by RT-qPCR, hsa-miR-34a-3p was specifically downregulated in patients with SOD1-mutated ALS, while hsa-miR-1306-3p was downregulated in ALS patients with both SOD1 and C9orf72 mutations. In addition, hsa-miR-199a-3p and hsa-miR-30b-5p were upregulated significantly in patients with SALS, while hsa-miR-501-3p, hsa-miR-103a-2-5p, and hsa-miR-181d-5p had a trend to be upregulated. The SVM diagnostic model used five miRNAs as features to distinguish ALS from HCs in our cohort with an area under receiver operating characteristic curve (AUC) of 0.80. CONCLUSION: Our study identified aberrant miRNAs from exosomes of SALS and ALS patients with SOD1/C9orf72 mutations and provided additional evidence that aberrant miRNAs were involved in the pathogenesis of ALS regardless of the presence or absence of the gene mutation. The machine learning algorithm had high accuracy in predicting the diagnosis of ALS, shedding light on the foundation for the clinical application of blood tests in the diagnosis of ALS, and revealing the pathological mechanisms of the disease. Frontiers Media S.A. 2023-02-10 /pmc/articles/PMC9951117/ /pubmed/36845651 http://dx.doi.org/10.3389/fnagi.2023.1106497 Text en Copyright © 2023 Cheng, Gu, Yang, Wei, Cao, Zhang, Shang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Cheng, Yang-Fan
Gu, Xiao-Jing
Yang, Tian-Mi
Wei, Qian-Qian
Cao, Bei
Zhang, Yang
Shang, Hui-Fang
Chen, Yong-Ping
Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis
title Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis
title_full Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis
title_fullStr Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis
title_full_unstemmed Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis
title_short Signature of miRNAs derived from the circulating exosomes of patients with amyotrophic lateral sclerosis
title_sort signature of mirnas derived from the circulating exosomes of patients with amyotrophic lateral sclerosis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951117/
https://www.ncbi.nlm.nih.gov/pubmed/36845651
http://dx.doi.org/10.3389/fnagi.2023.1106497
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