A Novel Schizophrenia Diagnostic Model Based on Statistically Significant Changes in Gene Methylation in Specific Brain Regions. (12th February 2020)

• Record Type: Journal Article
• Authors: Zou, Donghua; Qiu, Yufen; Li, Rongjie; Meng, Youshi; Wu, Yuan
• Other Names: Lazarov Nikolai E. Academic Editor.
• Is Part Of: BioMed research international. Volume 2020(2020)
• Publication Date: 2020-02-12
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2020/8047146
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Objective . The present study identified methylation patterns of schizophrenia- (SCZ-) related genes in different brain regions and used them to construct a novel DNA methylation-based SCZ diagnostic model. Methods . Four DNA methylation datasets representing different brain regions were downloaded from the Gene Expression Omnibus. The common differentially methylated genes (CDMGs) in all datasets were identified to perform functional enrichment analysis. The differential methylation sites of 10 CDMGs involved in the largest numbers of neurological or psychiatric-related biological processes were used to construct a DNA methylation-based diagnostic model for SCZ in the respective datasets. Results . A total of 849 CDMGs were identified in the four datasets, but the methylation sites as well as degree of methylation differed across the brain regions. Functional enrichment analysis showed CDMGs were significantly involved in biological processes associated with neuronal axon development, intercellular adhesion, and cell morphology changes and, specifically, in PI3K-Akt, AMPK, and MAPK signaling pathways. Four DNA methylation-based classifiers for diagnosing SCZ were constructed in the four datasets, respectively. The sample recognition efficiency of the classifiers showed an area under the receiver operating characteristic curve of 1.00 in three datasets and >0.9 in one dataset. Conclusion . DNA methylation patterns in SCZ vary across different brain regions, which may be a useful epigenetic characteristic for diagnosing SCZ. Our novel model based on SCZ-gene methylation shows promising diagnostic power.