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Updated: Aug 1, 2026
Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR
Published on: January 22, 2014
Micro RNA as a potential blood-based epigenetic biomarker for Alzheimer's disease
Peter D Fransquet1, Joanne Ryan2
1Department of Epidemiology and Preventive Medicine, Monash University, Melbourne 3004, Victoria, Australia; Disease Epigenetics, Murdoch Childrens Research Institute, and The University of Melbourne, Parkville, 3052, Victoria, Australia.
Abstract:
As the prevalence of Alzheimer's disease (AD) increases, the search for a definitive, easy to access diagnostic biomarker has become increasingly important. Micro RNA (miRNA), involved in the epigenetic regulation of protein synthesis, is a biological mark which varies in association with a number of disease states, possibly including AD. Here we comprehensively review methods and findings from 26 studies comparing the measurement of miRNA in blood between AD cases and controls. Thirteen of these studies used receiver operator characteristic (ROC) analysis to determine the diagnostic accuracy of identified miRNA to predict AD, and three studies did this with a machine learning approach. Of 8098 individually measured miRNAs, 23 that were differentially expressed between AD cases and controls were found to be significant in two or more studies. Only six of these were consistent in their direction of expression between studies (miR-107, miR-125b, miR-146a, miR-181c, miR-29b, and miR-342), and they were all shown to be down regulated in individuals with AD compared to controls. Of these directionally concordant miRNAs, the strongest evidence was for miR-107 which has also been shown in previous studies to be involved in the dysregulation of proteins involved in aspects of AD pathology, as well as being consistently downregulated in studies of AD brains. We conclude that imperative to the discovery of reliable and replicable miRNA biomarkers of AD, standardised methods of measurements, appropriate statistical analysis, utilization of large datasets with machine learning approaches, and comprehensive reporting of findings is urgently needed.
Insights
MicroRNAs (miRNAs) show promise as Alzheimer
Area of Science:
- Neuroscience
- Genetics
- Biomarker Discovery
Background:
- Alzheimer's disease (AD) prevalence is rising, increasing the need for accessible diagnostic biomarkers.
- MicroRNAs (miRNAs) are epigenetic regulators of protein synthesis, with expression levels potentially varying in disease states like AD.
- Blood-based miRNA measurements are explored as a less invasive diagnostic approach compared to traditional methods.
Purpose of the Study:
- To comprehensively review existing studies on miRNA measurement in blood for Alzheimer's disease diagnosis.
- To identify specific miRNAs consistently found to be differentially expressed in AD cases versus controls.
- To assess the diagnostic accuracy and reliability of blood-based miRNAs as Alzheimer's biomarkers.
Main Methods:
- Systematic review and meta-analysis of 26 studies comparing blood miRNA levels in AD patients and healthy controls.
- Analysis of diagnostic accuracy using receiver operator characteristic (ROC) analysis and machine learning approaches.
- Identification of differentially expressed miRNAs across studies and assessment of expression consistency and direction.
Main Results:
- Out of 8098 measured miRNAs, 23 were significantly different between AD cases and controls in two or more studies.
- Six miRNAs (miR-107, miR-125b, miR-146a, miR-181c, miR-29b, miR-342) showed consistent downregulation in AD patients across studies.
- miR-107 demonstrated the strongest evidence, with prior links to AD pathology and consistent downregulation in both blood and brain studies.
Conclusions:
- Specific miRNAs, particularly miR-107, show potential as blood-based biomarkers for Alzheimer's disease.
- Standardized measurement methods, robust statistical analysis, and machine learning are crucial for developing reliable miRNA biomarkers.
- Further research with large datasets and comprehensive reporting is needed to validate these findings and translate them into clinical practice.
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