Abstract
INTRODUCTION: Parkinson's disease (PD) is characterized by progressive degeneration of dopaminergic neurons in the substantia nigra and pathological aggregation of α-synuclein. Although existing therapies alleviate clinical symptoms, however, due to the unclear etiology, it remains impossible to completely halt this process through currently available approaches. This study aims to elucidate molecular mechanisms underlying PD pathogenesis and identify novel candidate biomarkers. METHODS: We integrated bioinformatics analysis of GEO datasets to pinpoint pivotal genes in PD progression from metabolic and stem cell perspectives. Hub genes were empirically validated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting in animal specimens. A combinatorial predictive model was constructed and evaluated via nomogram. Single-cell RNA sequencing (scRNA-seq) data from PD cohorts were interrogated to localize cell-type-specific expression patterns of signature genes and delineate subtype-specific mechanisms. Our analytical workflow entailed: differential expression screening, functional enrichment, protein-protein interaction (PPI) network construction, and machine learning (ML) algorithms. RESULTS: Our study reveals BMX and CA4 as key hub genes. Experimental confirmation of their dysregulation in in vivo PD models. Development of a high-accuracy PD prediction model (AUC >0.6). scRNA-seq analysis identified an NK cell subtype (NK1) enriched with CA4 expression. KEGG pathway analysis of NK1 marker genes implicated their role in neuroimmune crosstalk during PD progression. DISCUSSION: This work establishes a novel CA4-NK1-PD axis, providing a potential therapeutic entry point for future interventions.