Highlights
- •Non-invasive miRNA-based tests could be used for differential diagnosis.
- •Exosomal microRNAs (miRNAs) in serum are promising biomarker candidates for PSP.
- •Serum samples were analyzed with RT-qPCR to measure levels of selected miRNAs.
- •Differential miRNAs were dysregulated between patients groups and healthy controls.
Abstract
Introduction
Parkinson's disease (PD), a progressive neurodegenerative disease, can be misdiagnosed
with atypical conditions such as Progressive Supranuclear Paralysis (PSP) due to overlapping
clinical features. MicroRNAs (miRNAs) are small non-coding RNAs with a key role in
post-transcriptional gene regulation. The aim was to identify a set of differential
exosomal miRNAs biomarkers, which may aid in diagnosis.
Methods
We analyzed the serum level of 188 miRNAs in a discovery set, by using RTqPCR based
TaqMan assay, in a small cohort of healthy controls, PD and PSP patients. Subsequently,
the differentially expressed miRNAs, between PSP and PD patients, were further tested
in a larger and independent cohort of 33 healthy controls, 40 PD and 20 PSP patients.
The most accurate diagnostic exosomal miRNAs classifiers were identified in a logistic
regression model.
Results
A statistically significant set of three exosomal miRNAs: miR-21-3p, miR-22-3p and
miR-223-5p, discriminated PD from HC (area under the curve of 0.75), and a set of
three exosomal miRNAs, miR-425-5p, miR-21-3p, and miR-199a-5p, discriminated PSP from
PD with good diagnostic accuracy (area under the curve of 0.86). Finally, the classifier
that best discriminated PSP from PD consisted of six exosomal miRNAs (area under the
curve = 0.91), with diagnostic sensitivity and specificity of 0.89 and 0.90, respectively.
Conclusions
Based on our analysis, these data showed that exosomal miRNAs could act as biomarkers
to differentiate between PSP and PD.
Keywords
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Article info
Publication history
Published online: November 23, 2021
Accepted:
November 18,
2021
Received in revised form:
November 16,
2021
Received:
June 21,
2021
Identification
Copyright
© 2021 Elsevier Ltd. All rights reserved.
