Identification of Key Genes and Pathways in Parkinson’s Disease Through Bioinformatics Analysis

Abstract

Parkinson’s disease (PD), the second most common neurodegenerative disorder, involves nigral dopaminergic neuron loss. Integrative transcriptomics of central and peripheral tissues may uncover conserved molecular signatures, biomarkers, and therapeutic targets. Gene expression datasets from substantia nigra (GSE8397) and peripheral blood mononuclear cells (GSE22491) were retrieved from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using GEO2R with an adjusted P < 0.05 and |log2 fold change| ≥1. Shared DEGs underwent Gene Ontology, KEGG, and Reactome enrichment analyses. Protein–protein interaction networks were constructed using STRING and Cytoscape, followed by hub gene identification through multiple topological algorithms. Cell-type composition was estimated with xCell, and therapeutic relevance was evaluated with DGIdb and the Open Targets Platform. We identified 18 common DEGs (14 concordant, 4 discordant), enriched in dopaminergic/synaptic pathways. Five hub genes (SNCA, TH, DDC, SLC18A2, COMT) were consistently identified. Cell deconvolution revealed neuronal loss and oligodendrocyte increase. Drug analysis confirmed approved therapeutics and clinical relevance, including SNCA-targeted agents. Integrative transcriptomics identified conserved central-peripheral signatures in PD. These hub genes are candidate biomarkers/therapeutic targets, offering a systems-level framework for disease mechanisms and independent cohort validation.