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

Abstract

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.

Figure 1.

Statistical analysis and clustering of differentially expressed genes (DEGs) in Parkinson’s disease (PD). (a) Volcano plot of the DEGs. (b) Sample hierarchical clustering based on the expression of the DEGs. (c) Consensus cumulative distribution function (CDF) under different clustering numbers of DEGs. A larger CDF indicates a better gene clustering effect. When k = 2, the clustering result was the best. (d) The area difference value (delta area) between two CDF curves and the horizontal axis. k = 3 was the largest k with an appreciable increase in consensus. (e) Distribution of the clustering results shown by an item tracking plot. When k = 2, the number of mixed elements was the smallest.

Figure 2.

Gene enrichment results of cluster 1 genes (a) and cluster 2 genes (b). The horizontal axis shows the number of genes. The gray solid line represents –lg(P-value).

Figure 3.

The protein–protein interaction (PPI) network and analysis of the 10 hub genes. (a) PPI network of the genes enriched in the significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Pink nodes represent the 10 hub genes. A larger node size indicates a higher degree of interaction. (b) Receiver operating characteristic (ROC) curve analysis of the SVM model for predicting disease status in the GSE100054 dataset. (c) The log₂(fold change) value of the 10 hub genes in the validation dataset (GSE49126) and study dataset (GSE100054). (d) Relative expression levels of the 10 genes in Parkinson’s disease (PD) and controls in the patient cohort.