Exploring the mechanism of metabolic cell death-related genes AKR1C2 and MAP1LC3A as biomarkers in Parkinson's disease.

There is a strong relationship between metabolic cell death (MCD) and neurodegenerative diseases. However, the involvement of metabolic cell death (MCD)-related genes (MCDRGs) in Parkinson's disease (PD) pathogenesis remains poorly analyzed. Integrating PD-associated differentially expressed genes (DEGs) from GSE7621 with MCDRGs, we identified key biomarkers through protein-protein interaction networks and machine learning. Diagnostic performance was validated through nomogram analysis. Subsequent analyses included functional enrichment, immune profiling, drug prediction, and single-cell RNA sequencing. AKR1C2 and MAP1LC3A were identified as potential biomarkers. A nomogram with superior diagnostic performance was constructed. Gene set enrichment analysis indicated that both biomarkers were linked to the "Parkinson's disease". Further, immune infiltration revealed that AKR1C2 had the remarkably strongest positive correlation with M2 macrophages. Moreover, benzo[a]pyrene-1,6-dione, mestranol, and paraoxon-methyl might be potential therapeutic agents for PD. Single-cell RNA-seq analysis demonstrated endothelial-specific expression, with MAP1LC3A and AKR1C2 exhibiting distinct temporal regulation during differentiation. AKR1C2 and MAP1LC3A were identified as potential biomarkers associated with MCD in PD. These results offer fresh concepts for PD prevention and diagnosis.