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In the past servel years, the incidence of Parkinson's disease has continued to rise globally, placing a heavy burden on patients and society. Nevertheless, diagnostic and therapeutic tools for the disease have not been completely optimized. Consequently, the current study concentrated on determining promising biomarkers in the brain tissues of Parkinson's disease individuals and performing functional analyses. In the present work, we aimed to determine the hub genes providing Diagnostic and therapeutic targets for Parkinson's disease. GSE8397, GSE20292, GSE20163, GSE20164, and GSE49036 from the Gene Expression Omnibus (GEO) database were analyzed. Weighted gene co-expression network analysis (WGCNA) and DEGs determined 42 intersecting genes. Subsequently, the genes SV2C and DENR were identified as biomarkers of Parkinson's disease with machine learning. Finally, functional analysis suggested that SV2C may be involved in dopamine vesicle transport. Immune infiltration analysis revealed remarkable differences between SV2C for multiple immune cells. DENR and SV2C expression was demonstrated in PD cell models and clinical human samples.. In conclusion, The SV2C and DENR genes in brain tissue are viable biomarkers and therapeutic targets for Parkinson's disease which can facilitate diagnosis and treatment in clinical practice.
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