Multi-omics identification of novel biomarkers and therapeutic targets for Parkinson's disease: from transcriptome to drug interaction

Introduction Parkinson's disease (PD) is a progressive neurodegenerative disorder with limited therapeutic options. Acupuncture may serve as a complementary intervention, but its molecular mechanisms remain unclear. This study integrated bulk and single-cell transcriptomic datasets to identify PD-associated diagnostic candidate genes and explore their relationship with acupuncture-related temporal expression patterns. Methods PD-associated differentially expressed genes were identified from GSE68719. Time-series analysis of GSE178470 characterized ascending or descending expression trajectories in longitudinal blood transcriptomes from one patient with PD sampled at baseline and after 5 and 8 acupuncture sessions. Genes showing directionally opposite patterns between PD-associated dysregulation and post-acupuncture temporal changes were intersected, followed by feature selection using LASSO, Random Forest, Boruta, and XGBoost. Diagnostic performance was evaluated in GSE68719. Single-cell transcriptomic analysis, molecular docking, and MPP+-treated SH-SY5Y cells were used for further exploratory characterization and validation. Results Eleven candidate genes were identified, of which four were prioritized by machine-learning approaches. The four-gene panel discriminated patients with PD from healthy controls in GSE68719, with an apparent AUC of 0.873, sensitivity of 0.690, specificity of 0.864, accuracy of 0.795, precision of 0.769, F1 score of 0.727, and Brier score of 0.134. These metrics reflect PD-versus-control classification rather than prediction of acupuncture response. Single-cell analysis mapped cell type-specific expression across human midbrain populations. Molecular docking provided exploratory predicted binding poses. BAG3 and HSPB1 were upregulated in MPP+-treated SH-SY5Y cells. Discussion These findings provide an integrative framework for prioritizing PD-associated candidate genes with exploratory acupuncture-related temporal patterns. Further validation in larger longitudinal cohorts and functional studies is required.