Abstract
BackgroundBlood circulating microRNAs that are specific for Alzheimer’s disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method.MethodsStudies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA–AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions.ResultsA systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected.ConclusionThis study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.
Highlights
Blood circulating microRNAs that are specific for Alzheimer’s disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs)
adaptive boosting for miRNA disease association (ABMDA) ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs
The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs
Summary
Blood circulating microRNAs that are specific for Alzheimer’s disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). The most reliable DEmiRNAs can be identified by meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method. Alzheimer’s disease (AD) is subcellularly characterized by the presence of extracellular amyloid-beta (Aβ) plaque deposition and intracellular neurofibrillary tangles of hyperphosphorylated tau proteins [1]. Aberrant levels of Aβ and tau proteins in cerebrospinal fluid (CSF) and blood have been evaluated as biomarkers for AD diagnosis [7, 8], increased levels of total and hyperphosphorylated tau proteins, and decreased levels of Aβ in CSF. An ideal AD biomarker would allow for mass screening to identify patients at high risk of developing AD in the presymptomatic stage with adequate reliability
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