Identification of Key Genes and Immunological Features Associated with Copper Metabolism in Parkinson's Disease by Bioinformatics Analysis.

To explore diagnostic genes associated with cuproptosis in Parkinson's disease (PD) and to characterize immune cell infiltration by comprehensive bioinformatics analysis, three PD datasets were downloaded from the GEO database, two of which were merged and preprocessed as the internal training set and the remaining one as the external validation set. Based on the internal training set, differential analysis was performed to obtain differentially expressed genes (DEGs), and weighted gene co-expression network analysis (WGCNA) was conducted to obtain significant module genes. The genes obtained here were intersected to form the intersecting genes. The intersecting genes obtained from DEGs and WGCNA were intersected with cuproptosis-related genes (CRGs) to generate cuproptosis-related disease signature genes, and functional enrichment analysis was performed on Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, LASSO analysis of the cuproptosis-related disease signature genes was performed to identify key genes and construct a diagnostic and predictive model. Then, single sample gene set enrichment analysis (ssGSEA) was performed on the internal training set to further analyze the correlation between key genes and immune cells. Lastly, the results were validated using an external validation set. A total of 405 DEGs were obtained by differential analysis, and 6 gene modules were identified by WGCNA analysis. The genes in the most significant modules were intersected with the DEGs to obtain 21 intersecting genes. The functions of the intersecting genes were mainly enriched in neurotransmitter transport, GABA-ergic synapse, synaptic vesicle cycle, serotonergic synapse, phenylalanine metabolism, tyrosine metabolism, tryptophan metabolism, etc. Subsequently, the intersecting genes were intersected with CRGs, and LASSO regression analysis was performed to screen 3 key cuproptosis-related disease signature genes, namely, SLC18A2, SLC6A3, and SV2C. The calibration curve of the nomogram model constructed based on these 3 key genes to predict PD showed good agreement, with a C-index of 0.944 and an area under the ROC (AUC) of 0.944 (0.833-1.000). It was also validated by the external dataset that the model constructed with these 3 key genes had good diagnostic and predictive power for PD. The ssGSEA analysis revealed that neutrophils might be the potential core immune cells and that SLC18A2, SLC6A3, and SV2C were significantly negatively correlated with neutrophils, which was also verified in the validation set. PD diagnosis and prediction model based on CRGs (SLC18A2, SLC6A3, and SV2C) has good diagnostic and predictive performance and could be a useful tool in the diagnosis of PD.