Imagine that patients with amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disorder, are like treasure chests waiting to be unlocked. In this study, scientists delved into the circulating exosomes, which act as communication vessels in the body, of ALS patients and healthy controls. By analyzing the microRNAs (miRNAs) within these exosomes, they aimed to identify markers that could aid in diagnosing ALS. The researchers discovered a total of 64 differentially expressed miRNAs in ALS patients with SOD1 gene mutations and 128 in those with C9orf72 gene mutations. Importantly, some miRNAs were common to both groups, hinting at shared mechanisms underlying ALS development. The team also developed a machine learning model using five specific miRNAs as features to predict ALS diagnosis with high accuracy. These findings not only shed light on the potential pathological mechanisms of ALS but also pave the way for future clinical applications of blood tests in diagnosing this devastating disease. Excitingly, this research opens up new possibilities for early detection and personalized therapies for ALS patients. To dive deeper into the fascinating world of exosomes and their role in ALS, check out the full research article.

BackgroundAmyotrophic 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.MethodsWe 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.ResultsA 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.ConclusionOur 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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