Identification of SV2C and DENR as Key Biomarkers for Parkinson ’s Disease Based on Bioinformatics, Machine Learning, and Experimental Verification

The objective of this study is to investigate the potential biomarkers and therapeutic target genes for Parkinson ’s disease (PD). We analyzed four datasets (GSE8397, GSE20292, GSE20163, GSE20164) from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and differential expression analysis to select genes and perform functional analysis. We applied three algorithms, namely, random forest, support vector machine recursive feature elimination, and least absolute shrinkage and selection operator, to identify hub genes, perform functional analysis, and assess their clinical diagnostic potential using receiver operating characteristic (ROC) curve analysis. We employ ed the xCell website to evaluate differences in the composition patterns of immune cells in the GEO datasets. We also collected serum samples from PD patients and established PD cell model to validate the expression of hub genes using enzyme-linked immunosorbent assay and quantitative real-time poly merase chain reaction. Our findings identifiedSV2C andDENR as two hub genes for PD and decreased in PD brain tissue compared with controls. ROC analysis showed effectively value ofSV2C andDENR to diagnose PD, and they were downregulated in the serum of PD patients and cell model. Functional analysis revealed that dopamine vesicle transport and synaptic vesicle recycling are crucial pathways in PD. Besides, the differences in the composition of immune cells, especially basophils a...
Source: Journal of Molecular Neuroscience - Category: Neuroscience Source Type: research