Study of molecular patterns associated with ferroptosis in Parkinson’s disease and its immune signature

Abstract

Parkinson’s disease is the second most common neurodegenerative disease in the world. We downloaded data on Parkinson’s disease and Ferroptosis-related genes from the GEO and FerrDb databases. We used WCGAN and Random Forest algorithm to screen out five Parkinson’s disease ferroptosis-related hub genes. Two genes were identified for the first time as possibly playing a role in Braak staging progression. Unsupervised clustering analysis based on hub genes yielded ferroptosis isoforms, and immune infiltration analysis indicated that these isoforms are associated with immune cells and may represent different immune patterns. FRHGs scores were obtained to quantify the level of ferroptosis modifications in each individual. In addition, differences in interleukin expression were found between the two ferroptosis subtypes. The biological functions involved in the hub gene are analyzed. The ceRNA regulatory network of hub genes was mapped. The disease classification diagnosis model and risk prediction model were also constructed by applying hub genes based on logistic regression. Multiple external datasets validated the hub gene and classification diagnostic model with some accuracy. This study explored hub genes associated with ferroptosis in Parkinson’s disease and their molecular patterns and immune signatures to provide new ideas for finding new targets for intervention and predictive biomarkers.