Finding Genetic Contributors to Parkinson’s Disease via Weighted Gene Co- expression Network Analysis

Background: As the second most prevalent progressive neurodegenerative disorder, Parkinson’s disease (PD) involves complex pathological processes and lacks definitive diagnostic biomarkers. This study aimed to explore molecular signatures associated with PD to support improved diagnostic and therapeutic strategies for PD patients. Methods: Gene expression profiles from GSE202667 dataset (platform: GPL20844) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between PD and control samples was assessed using the “limma” package in R software. We used Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks and evaluate their correlation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed to explore biological correlations. Hub genes were validated using an independent dataset (GSE54536). Results: A total of 365 common DEGs were identified. WGCNA revealed 4 co-expression modules, with the eigenvalues of the blue module most significantly with PD stages (r=0.78, P<0.001). Key genes from this module were analyzed using protein-protein interaction (PPI) networks and CytoHubba algorithm. Eight potential hub genes, including LILRB4, LILRB2, FCGR2A, FCGR2B, CCR1, TLR4, ITGAX, and NCAM1 , were identified, of which FCGR2A and FCGR2B were validated as consistently upregulated in PD across datasets. Conclusions: FCGR2A and FCGR2B may serve as immune-related molecular indicators of PD, suggesting a potential role in disease progression and as candidates for further clinical diagnostic and therapeutic development. Further studies with larger and experimentally enriched datasets are recommended.