Integrative analysis of DNA methylation and gene expression data for the diagnosis and underlying mechanism of Parkinson’s disease

BACKGROUND: Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder and the leading cause of disability in the daily activities. In the management of PD, accurate and specific biomarkers in blood for the early diagnosis of PD are urgently needed. DNA methylation is one of the main epigenetic mechanisms and associated with the gene expression and disease initiation of PD. We aimed to construct a methylation signature for the diagnosis of PD patients, and explore the potential value of DNA methylation in therapeutic options. MATERIALS AND METHODS: Whole blood DNA methylation and gene expression data of PD patients as well as healthy controls were extracted from Gene Expression Omnibus database. Next, differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between PD patients and healthy controls were identified. Least absolute shrinkage and selection operator cox regression analysis was carried out to construct a diagnostic signature based on the overlapped genes. And, the receiver operating characteristic (ROC) curves were drawn and the area under the curve (AUC) was used to assess the diagnostic performance of the signature in both the training and testing datasets. Finally, gene ontology and gene set enrichment analysis were subsequently carried out to explore the underlying mechanisms. RESULTS: We obtained a total of 9,596 DMGs, 1,058 DEGs, and 237 overlapped genes in the whole blood between PD patients and healthy controls. Eight methylation-driven genes (HIST1H4L, CDC42EP3, KIT, GNLY, SLC22A1, GCM1, INO80B, and ARHGAP26) were identified to construct the gene expression signature. The AUCs in predicting PD patients were 0.84 and 0.76 in training dataset and testing dataset, respectively. Additionally, eight methylation-altered CpGs were also identified to construct the CpGs signature which showed a similarly robust diagnostic capability, with AUCs of 0.8 and 0.73 in training dataset and testing dataset, respectively. CONCLUSION: We conducted an integrated analysis of the gene expression and DNA methylation data, and constructed a methylation-driven genes signature and a methylation-altered CpGs signature to distinguish the patients with PD from healthy controls. Both of them had a robust prediction power and provide a new insight into personalized diagnostic and therapeutic strategies for PD.