KEGG analysis results of the green module.

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.

帕金森病（Parkinson’s disease）是全球第二大常见神经退行性疾病。本研究从GEO数据库与FerrDb数据库下载了帕金森病及铁死亡相关基因数据，采用WCGAN与随机森林算法筛选出5个帕金森病铁死亡相关核心基因（hub genes），其中2个基因首次被证实可能参与Braak分期（Braak staging）的疾病进展过程。基于核心基因的无监督聚类分析可划分出铁死亡亚型，免疫浸润分析结果显示，此类亚型与免疫细胞密切相关，或代表不同的免疫模式。本研究构建了铁死亡核心基因评分（FRHGs scores），以量化每个个体的铁死亡修饰水平。此外，两类铁死亡亚型间的白细胞介素表达存在显著差异。研究还对核心基因所涉及的生物学功能进行了富集分析，并绘制了核心基因的内源竞争RNA（ceRNA）调控网络。基于核心基因，通过逻辑回归算法构建了疾病分类诊断模型与风险预测模型。借助多组外部数据集对核心基因及分类诊断模型进行验证，结果显示二者均具备一定的预测准确性。本研究探明了帕金森病中铁死亡相关核心基因及其分子模式与免疫特征，为发掘新型干预靶点及预测性生物标志物提供了全新思路。