Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
22 10 2021
22 10 2021
Historique:
received:
26
03
2021
accepted:
12
10
2021
entrez:
23
10
2021
pubmed:
24
10
2021
medline:
22
1
2022
Statut:
epublish
Résumé
There are many subtypes of dementia, and identification of diagnostic biomarkers that are minimally-invasive, low-cost, and efficient is desired. Circulating microRNAs (miRNAs) have recently gained attention as easily accessible and non-invasive biomarkers. We conducted a comprehensive miRNA expression analysis of serum samples from 1348 Japanese dementia patients, composed of four subtypes-Alzheimer's disease (AD), vascular dementia, dementia with Lewy bodies (DLB), and normal pressure hydrocephalus-and 246 control subjects. We used this data to construct dementia subtype prediction models based on penalized regression models with the multiclass classification. We constructed a final prediction model using 46 miRNAs, which classified dementia patients from an independent validation set into four subtypes of dementia. Network analysis of miRNA target genes revealed important hub genes, SRC and CHD3, associated with the AD pathogenesis. Moreover, MCU and CASP3, which are known to be associated with DLB pathogenesis, were identified from our DLB-specific target genes. Our study demonstrates the potential of blood-based biomarkers for use in dementia-subtype prediction models. We believe that further investigation using larger sample sizes will contribute to the accurate classification of subtypes of dementia.
Identifiants
pubmed: 34686734
doi: 10.1038/s41598-021-00424-1
pii: 10.1038/s41598-021-00424-1
pmc: PMC8536697
doi:
Substances chimiques
Amyloid beta-Peptides
0
Biomarkers
0
MicroRNAs
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
20947Informations de copyright
© 2021. The Author(s).
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