Correlation analysis and clinical validation of Parkinson's disease and epigenetic factor-related genes based on transcriptome data

Background Parkinson's disease (PD) is a neurodegenerative disorder, and its early diagnosis and treatment remain significant challenges. Identifying molecular biomarkers associated with PD pathogenesis is critical for disease intervention. Methods The GSE7621 and GSE49036 datasets were integrated to identify differentially expressed genes (DEGs) between PD patients' substantia nigra tissues and normal samples. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to screen PD-related module genes. DEGs were cross-analyzed with epigenetic factor-related genes to identify key genes. The predictive efficacy of these key genes was evaluated using 90 machine learning models, and their expression was validated via independent GEO datasets (GSE20141, GSE42966) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). CIBERSORT was utilized to analyze immune cell infiltration, and Gene Set Enrichment Analysis (GSEA) was performed to explore the biological functions and molecular mechanisms of key genes. Results A total of 78 DEGs were identified, and 550 PD-related genes were screened through WGCNA, yielding 12 key genes. Machine learning models revealed ACTL6B, PRMT8, NAP1L2, BABAM1, and EID2 as core predictive genes for PD. Independent validation and RT-qPCR confirmed significant downregulation of ACTL6B, PRMT8, NAP1L2, BABAM1, and EID2 in PD patients. Immune infiltration analysis demonstrated altered infiltration abundance of CD8 + T cells, macrophage subtypes, and other immune cells in PD patients, with key genes linked to immune microenvironment regulation. GSEA indicated that these genes participate in pathways such as cellular metabolic reprogramming and synaptic transmission. Conclusion This study systematically identified PD-associated epigenetic regulatory genes and revealed their connections to immune microenvironment dynamics and molecular pathways, offering novel insights for early PD diagnosis and therapeutic target development.