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Absolute Quantification of Plasma MicroRNA Levels in Cynomolgus Monkeys, Using Quantitative Real-time Reverse Transcription PCR
Published on: February 12, 2018
Plasma-based circulating MicroRNA biomarkers for Parkinson's disease
Sok Kean Khoo1, David Petillo, Un Jung Kang
1Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503, USA. Kean.Khoo@vai.org
Journal of Parkinson'S Disease
|August 14, 2013
Summary
Researchers identified plasma microRNAs (miRNAs) as potential biomarkers for Parkinson's disease (PD). While promising in initial tests, their predictive value varied across different clinical sites, highlighting challenges in multi-site biomarker validation.
Area of Science:
- Neuroscience
- Biomarker Discovery
- Genetics
Background:
- Current Parkinson's disease (PD) diagnosis relies on subjective motor assessments.
- Objective, quantifiable molecular biomarkers are needed for early PD detection.
- Plasma-based circulating microRNAs (miRNAs) show potential as diagnostic tools.
Purpose of the Study:
- To identify, develop, and validate plasma-based circulating miRNAs as PD biomarkers.
- To assess the diagnostic potential of miRNA panels in Parkinson's disease.
Main Methods:
- Microarray analysis of miRNA expression in 32 PD patients and 32 controls.
- Utilized k-Top Scoring Pairs (k-TSP) algorithm and Significance Analysis of Microarrays (SAM).
- Validated findings using real-time PCR in a replication set (42 PD/30 controls) and a validation set (30 PD/5 PSP/4 MSA).
Main Results:
- Identified 9 PD-predictive miRNA pairs (k-TSP) and 13 differentially-expressed miRNAs (SAM).
- A combined panel (k-TSP1, miR-626, miR-505) achieved 91% sensitivity and 100% specificity in the replication set.
- Lower predictive values were observed in the multi-site validation set.
Conclusions:
- Plasma-based circulating miRNAs are feasible biomarkers for neurodegenerative disorders like PD.
- This study highlights the challenges in molecular biomarker research across multiple clinical sites.
- Further research is needed to overcome variability in multi-site biomarker validation.
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