Oct 06 2020

Identification of Parkinson's disease-related pathways and potential risk factors.

Jun Shen, Xiao-Chang Chen, Wang-Jun Li, Qiu Han, Chun Chen
& 3 others
Author Affiliations
The Journal of international medical research(England)

OBJECTIVE: To identify Parkinson's disease (PD)-associated deregulated pathways and genes, to further elucidate the pathogenesis of PD.

METHODS: Dataset GSE100054 was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) in PD samples were identified. Functional enrichment analyses were conducted for the DEGs. The top 10 hub genes in the protein-protein interaction (PPI) network were screened out and used to construct a support vector machine (SVM) model. The expression of the top 10 genes was then validated in another dataset, GSE46129, and a clinical patient cohort.

RESULTS: A total of 333 DEGs were identified. The DEGs were clustered into two gene sets that were significantly enriched in 12 pathways, of which 8 were significantly deregulated in PD, including cytokine-cytokine receptor interaction, gap junction, and actin cytoskeleton regulation. The signature of the top 10 hub genes in the PPI network was used to construct the SVM model, which had high performance for predicting PD. Of the 10 genes, GP1BA, GP6, ITGB5, and P2RY12 were independent risk factors of PD.

CONCLUSION: Genes such as GP1BA, GP6, P2RY12, and ITGB5 play critical roles in PD pathology through pathways including cytokine-cytokine receptor interaction, gap junctions, and actin cytoskeleton regulation.

Keywords: Parkinson’s disease, clustering analysis, deregulated pathway, differentially expressed gene, genetic risk factors, protein–protein interactions, support vector machine model
Journal Article
J. Int. Med. Res. ;48(10):300060520957197

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