期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 4, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa244
关键词
Parkinson's disease; weighted correlation network analysis; synaptic vesicle trafficking; SYNJ1; PPP2CA; Parkinson-plus syndromes
资金
- National Natural Science Foundation of China [81873776, 81873777]
- National Key R&D Program of China [2017YFC1310200]
- Natural Science Foundations of Guangdong of China [2017A030311010]
- Initiated Foundation of Zhujiang Hospital [02020318005]
- Scientific Research Foundation of Guangzhou [201704030080]
- National Medical Research Council
- Leading Talent in Talents Project Guangdong High-level Personnel of Special Support Program
This study aimed to identify key susceptibility gene targets in postmortem brains and blood of Parkinson's disease (PD) patients and healthy controls (HC). Multitiered analysis integrated gene expression data from 244 human postmortem tissues, resulting in the identification of hub node genes related to PD in a consensus module. Validation in 238 subjects revealed promising markers, especially SYNJ1, for discriminating PD from HCs and Parkinson's Plus Syndrome (PPS), highlighting the potential of SYNJ1 in PD diagnosis and treatment.
Objective: We aimed to identify key susceptibility gene targets in multiple datasets generated from postmortem brains and blood of Parkinson's disease (PD) patients and healthy controls (HC). Methods: We performed a multitiered analysis to integrate the gene expression data using multiple-gene chips from 244 human postmortem tissues. We identified hub node genes in the highly PD-related consensus module by constructing protein-protein interaction (PPI) networks. Next, we validated the top four interacting genes in 238 subjects (90 sporadic PD, 125 HC and 23 Parkinson's Plus Syndrome (PPS)). Utilizing multinomial logistic regression analysis (MLRA) and receiver operating characteristic (ROC), we analyzed the risk factors and diagnostic power for discriminating PD from HC and PPS. Results: We identified 1333 genes that were significantly different between PD and HCs based on seven microarray datasets. The identified MEturquoise module is related to synaptic vesicle trafficking (SVT) dysfunction in PD (P < 0.05), and PPI analysis revealed that SVT genes PPP2CA, SYNJ1, NSF and PPP3CB were the top four hub node genes in MEturquoise (P < 0.001). The levels of these four genes in PD postmortem brains were lower than those in HC brains. We found lower blood levels of PPP2CA, SYNJ1 and NSF in PD compared with HC, and lower SYNJ1 in PD compared with PPS (P < 0.05). SYNJ1, negatively correlated to PD severity, displayed an excellent power to discriminating PD from HC and PPS. Conclusions: This study highlights that SVT genes, especially SYNJ1, may be promising markers in discriminating PD from HCs and PPS.
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