Uncovering Potential Biomarkers and Constructing a Prediction Model Associated with Iron Metabolism in Parkinson's Disease

Background: Parkinson's disease (PD) is a common neurodegenerative disorder. Iron metabolism abnormalities have been reported in PD patients and may contribute to disease pathogenesis. Our study aimed to explore key genes associated with iron metabolism in Methods: Three datasets and iron metabolism-related genes (IMRGs) were collected from the public database, and the datasets were merged into a combined dataset. PD-related differentially expressed genes (DEGs) were obtained and intersected with IMRGs to acquire iron metabolism-related DEGs (IMRDEGs). Subsequently, the IMRDEGs were subjected to functional enrichment and ROC analyses. Finally, key genes were identified, followed by the construction and evaluation of a risk score model, drug prediction, and Results: A total of 24 IMRDEGs were obtained. The AUC values of the 24 IMRDEGs ranged from 0.599 to 0.781. After logistic regression and the SVM analyses, a total of 10 key genes were identified, followed by the construction of the risk score model. The AUC value of the risk score model was 0.953, demonstrating good diagnostic value. The calibration curve and decision curve analysis showed that the risk score model has good predictive performance and clinical benefit for PD patients. Additionally, a total of 49 drugs were predicted. Conclusion: A total of 10 key genes were identified as potential biomarkers, and the risk score model was constructed for PD patients, exhibiting good diagnostic. This study may provide potential biomarkers for PD patients, promoting an understanding of the pathogenesis of PD.