Identification and experimental validation of candidate biomarkers for Parkinson's Disease through integrated bioinformatics, Mendelian randomization, and machine learning.

BACKGROUND: Identifying reliable biomarkers and therapeutic targets is important for improving the early diagnosis and treatment of Parkinson's disease (PD). This study aimed to identify PD-related core genes, validate their expression, and explore potential therapeutic drugs. METHODS: PD transcriptomic datasets were analyzed using differential expression analysis, Mendelian randomization, and machine learning. Candidate genes were validated using quantitative PCR (qPCR) in an MPTP-induced PD mouse model. Potential drugs were screened using the DSigDB database, followed by molecular docking and 100-ns molecular dynamics simulations to evaluate protein-ligand binding and stability. RESULTS: Seven potential core genes-AMIGO2, ATG7, CDK2AP2, CYP27A1, MT1X, NIPSNAP1, and TTC19-were identified. AMIGO2, ATG7, NIPSNAP1, and TTC19 were downregulated, whereas CDK2AP2, CYP27A1, and MT1X were upregulated. The results of animal experiments validated the abnormal expression of ATG7, NIPSNAP1, TTC19, CYP27A1, and MT1X. Drug screening identified 239 candidate compounds, five of which met the screening criteria. Molecular docking and molecular dynamics simulations provided computational support for relatively stable interactions between the top three drug-target complexes. CONCLUSION: This study identified several PD-related core genes and experimentally validated the expression of selected genes in an MPTP-induced PD mouse model. In addition, several potential therapeutic compounds were identified through computational analyses, providing candidates for further experimental investigation.