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来自肌萎缩侧索硬化症患者循环外泌体的微小RNA特征

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

作者信息

Cheng Yang-Fan, Gu Xiao-Jing, Yang Tian-Mi, Wei Qian-Qian, Cao Bei, Zhang Yang, Shang Hui-Fang, Chen Yong-Ping

机构信息

Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Disease Center, West China Hospital, Sichuan University, Chengdu, China.

Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China.

出版信息

Front Aging Neurosci. 2023 Feb 10;15:1106497. doi: 10.3389/fnagi.2023.1106497. eCollection 2023.

DOI:10.3389/fnagi.2023.1106497
PMID:36845651
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9951117/
Abstract

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disorder (NDS) with unclear pathophysiology and few therapeutic options. Mutations in and 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 -mutated ALS, three patients with -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 -mutated ALS and 128 differentially expressed miRNAs in patients with -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 -mutated ALS, while hsa-miR-1306-3p was downregulated in ALS patients with both and 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 / 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.

摘要

背景

肌萎缩侧索硬化症(ALS)是一种进行性、致命的神经退行性疾病(NDS),其病理生理学尚不清楚,治疗选择有限。在亚洲和白种人ALS患者中, 和 基因的突变分别最为常见。在基因发生突变的ALS患者中发现的异常微小RNA(miRNA)可能参与了基因特异性ALS和散发性ALS(SALS)的发病机制。本研究的目的是筛选ALS患者和健康对照(HCs)外泌体中差异表达的miRNA,并构建基于miRNA的诊断模型以区分患者和HCs。

方法

我们使用以下两个队列比较了ALS患者和HCs循环外泌体来源的miRNA:一个发现队列(3例 基因突变的ALS患者、3例 基因突变的ALS患者和3例HCs)通过微阵列分析,一个验证队列(16例基因发生突变的ALS患者、65例SALS患者和61例HCs)通过RT-qPCR进行确认。支持向量机(SVM)模型用于利用SALS和HCs之间五个差异表达的miRNA帮助诊断ALS。

结果

与HCs相比,通过微阵列分析在 基因突变的ALS患者中总共获得了64个差异表达的miRNA,在 基因突变的ALS患者中获得了128个差异表达的miRNA。其中,在两组中鉴定出11个重叠的失调miRNA。在通过RT-qPCR验证的14个最显著的候选miRNA中,hsa-miR-34a-3p在 基因突变的ALS患者中特异性下调,而hsa-miR-1306-3p在 和 基因均发生突变的ALS患者中下调。此外,hsa-miR-199a-3p和hsa-miR-30b-5p在SALS患者中显著上调,而hsa-miR-501-3p、hsa-miR-103a-2-5p和hsa-miR-181d-5p有上调趋势。SVM诊断模型使用五个miRNA作为特征在我们的队列中区分ALS和HCs,受试者操作特征曲线(AUC)下面积为0.80。

结论

我们的研究从SALS和 / 基因突变的ALS患者的外泌体中鉴定出异常miRNA,并提供了额外证据表明异常miRNA无论是否存在基因突变均参与ALS的发病机制。机器学习算法在预测ALS诊断方面具有较高准确性,为血液检测在ALS诊断中的临床应用奠定了基础,并揭示了该疾病的病理机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2521/9951117/59cf7df57b35/fnagi-15-1106497-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2521/9951117/7e19eb33c0a4/fnagi-15-1106497-g002.jpg
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