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Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning
Joint Authors
Ren, Jianting
Zhang, Bo
Wei, Dongfeng
Zhang, Zhanjun
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-27
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Background.
Alzheimer’s disease (AD) is a neurodegenerative disorder and characterized by the cognitive impairments.
It is essential to identify potential gene biomarkers for AD pathology.
Methods.
DNA methylation expression data of patients with AD were downloaded from the Gene Expression Omnibus (GEO) database.
Differentially methylated sites were identified.
The functional annotation analysis of corresponding genes in the differentially methylated sites was performed.
The optimal diagnostic gene biomarkers for AD were identified by using random forest feature selection procedure.
In addition, receiver operating characteristic (ROC) diagnostic analysis of differentially methylated genes was performed.
Results.
A total of 10 differentially methylated sites including 5 hypermethylated sites and 5 hypomethylated sites were identified in AD.
There were a total of 8 genes including thioredoxin interacting protein (TXNIP), noggin (NOG), regulator of microtubule dynamics 2 (FAM82A1), myoneurin (MYNN), ankyrin repeat domain 34B (ANKRD34B), STAM-binding protein like 1, ALMalpha (STAMBPL1), cyclin-dependent kinase inhibitor 1C (CDKN1C), and coronin 2B (CORO2B) that correspond to 10 differentially methylated sites.
The cell cycle (FDR=0.0284087) and TGF-beta signaling pathway (FDR=0.0380372) were the only two significantly enriched pathways of these genes.
MYNN was selected as optimal diagnostic biomarker with great diagnostic value.
The random forests model could effectively predict AD.
Conclusion.
Our study suggested that MYNN could be served as optimal diagnostic biomarker of AD.
Cell cycle and TGF-beta signaling pathway may be associated with AD.
American Psychological Association (APA)
Ren, Jianting& Zhang, Bo& Wei, Dongfeng& Zhang, Zhanjun. 2020. Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning. BioMed Research International،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1137409
Modern Language Association (MLA)
Ren, Jianting…[et al.]. Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning. BioMed Research International No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1137409
American Medical Association (AMA)
Ren, Jianting& Zhang, Bo& Wei, Dongfeng& Zhang, Zhanjun. Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1137409
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1137409