miRNAs in cerebrospinal fluid associated with Alzheimer's disease: A systematic review and pathway analysis using a data mining and machine learning approach.
J Neurochem
; 168(6): 977-994, 2024 06.
Article
in En
| MEDLINE
| ID: mdl-38390627
ABSTRACT
Alzheimer's disease (AD) is the most common type and accounts for 60%-70% of the reported cases of dementia. MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in gene expression regulation. Although the diagnosis of AD is primarily clinical, several miRNAs have been associated with AD and considered as potential markers for diagnosis and progression of AD. We sought to match AD-related miRNAs in cerebrospinal fluid (CSF) found in the GeoDataSets, evaluated by machine learning, with miRNAs listed in a systematic review, and a pathway analysis. Using machine learning approaches, we identified most differentially expressed miRNAs in Gene Expression Omnibus (GEO), which were validated by the systematic review, using the acronym PECO-Population (P) Patients with AD, Exposure (E) expression of miRNAs, Comparison (C) Healthy individuals, and Objective (O) miRNAs differentially expressed in CSF. Additionally, pathway enrichment analysis was performed to identify the main pathways involving at least four miRNAs selected. Four miRNAs were identified for differentiating between patients with and without AD in machine learning combined to systematic review, and followed the pathways analysis:
miRNA-30a-3p, miRNA-193a-5p, miRNA-143-3p, miRNA-145-5p. The pathways epidermal growth factor, MAPK, TGF-beta and ATM-dependent DNA damage response, were regulated by these miRNAs, but only the MAPK pathway presented higher relevance after a randomic pathway analysis. These findings have the potential to assist in the development of diagnostic tests for AD using miRNAs as biomarkers, as well as provide understanding of the relationship between different pathophysiological mechanisms of AD.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
MicroRNAs
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Data Mining
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Alzheimer Disease
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Machine Learning
Limits:
Humans
Language:
En
Journal:
J Neurochem
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J. neurochem
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Journal of neurochemistry
Year:
2024
Document type:
Article
Affiliation country:
Brazil
Country of publication:
United kingdom