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帕金森病中与铁死亡相关的分子模式及其免疫特征的研究。

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

机构信息

Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China.

Department of Neurology, The 962 Hospital of the Chinese People's Liberation Army Joint Logistic Support Force, City Harbin, Province Heilongjiang, China.

出版信息

PLoS One. 2023 Dec 21;18(12):e0295699. doi: 10.1371/journal.pone.0295699. eCollection 2023.

DOI:10.1371/journal.pone.0295699
PMID:38127902
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10734959/
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 数据库中下载了帕金森病和铁死亡相关基因的数据。我们使用 WCGAN 和随机森林算法筛选出五个与帕金森病铁死亡相关的枢纽基因。其中两个基因首次被确定为可能在 Braak 分期进展中发挥作用。基于枢纽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有关，可能代表不同的免疫模式。通过 FRHGs 评分来量化每个个体中铁死亡修饰的水平。此外，还发现两种铁死亡亚型之间的白细胞介素表达存在差异。分析了枢纽基因涉及的生物学功能。绘制了枢纽基因的 ceRNA 调控网络。还通过逻辑回归应用枢纽基因构建了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枢纽基因和分类诊断模型的准确性。本研究探讨了帕金森病中与铁死亡相关的枢纽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了新的思路。

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