PDF(Pubmed)
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.
摘要：
帕金森病是世界上第二常见的神经退行性疾病。我们从GEO和FerrDb数据库下载了有关帕金森氏病和铁凋亡相关基因的数据。我们使用WCGAN和随机森林算法筛选出五个与帕金森氏病铁凋亡相关的hub基因。首次发现两个基因可能在Braak分期进展中起作用。基于集线器基因的无监督聚类分析产生了铁死亡亚型，免疫浸润分析表明，这些亚型与免疫细胞相关，可能代表不同的免疫模式。获得FRHG评分以量化每个个体中铁凋亡修饰的水平。此外,白细胞介素的表达在两种铁死亡亚型之间存在差异。分析了hub基因中涉及的生物学功能。绘制了hub基因的ceRNA调控网络。应用基于logistic回归的hub基因构建疾病分类诊断模型和风险预测模型。多个外部数据集验证了hub基因和分类诊断模型的准确性。本研究探索帕金森病中与铁凋亡相关的hub基因及其分子模式和免疫特征,为寻找新的干预靶点和预测生物标志物提供新思路。
