Li Yatan, Jia Wei, Chen Chen, Chen Cheng, Chen Jinchao, Yang Xinling, Liu Pei
Department of Pharmacy, the Second Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China.
PLoS One. 2025 May 28;20(5):e0320257. doi: 10.1371/journal.pone.0320257. eCollection 2025.
Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder. The inflammatory response is essential in the pathogenesis and progression of PD. The goal of this study is to combine bioinformatics and machine learning to screen for biomarker genes related to the inflammatory response in PD. First, differentially expressed genes associated with inflammatory response were screened, PPI networks were constructed and enriched for analysis. LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. Finally, drug sensitivity analysis, mRNA-miRNA network construction and single-cell transcriptome data analysis were performed. The experimental results showed that we screened 31 differentially expressed genes related to inflammatory response. Signaling pathways such as cytokine activity were associated with these genes. Three biomarkers were identified using machine learning algorithms: IL18R1, NMUR1 and RELA. Seventeen co-associated miRNAs were identified by the mRNA-miRNA network as possible regulatory nodes in PD. Finally, these three biomarkers were found to be closely associated with T cells, Endothelial cells, excitatory neurons, inhibitory neurons, and other cells in single-cell transcriptomic analysis. In conclusion, IL18R1, NMUR1 and RELA could be potential therapeutic targets for PD in inflammatory response and new biomarkers for PD diagnosis.
帕金森病（PD）是一种常见且使人衰弱的神经退行性疾病。炎症反应在PD的发病机制和进展中至关重要。本研究的目的是结合生物信息学和机器学习来筛选与PD炎症反应相关的生物标志物基因。首先，筛选与炎症反应相关的差异表达基因，构建蛋白质-蛋白质相互作用（PPI）网络并进行富集分析。使用套索（LASSO）、支持向量机递归特征消除（SVM-RFE）和随机森林算法筛选生物标志物基因。然后，绘制ROC曲线，并基于生物标志物基因构建PD风险预测模型。最后，进行药物敏感性分析、mRNA- miRNA网络构建和单细胞转录组数据分析。实验结果表明，我们筛选出31个与炎症反应相关的差异表达基因。细胞因子活性等信号通路与这些基因相关。使用机器学习算法鉴定出三个生物标志物：白细胞介素18受体1（IL18R1）、神经降压素受体1（NMUR1）和信号转导及转录激活因子3（RELA）。通过mRNA-miRNA网络鉴定出17个共同相关的miRNA作为PD中可能的调控节点。最后，在单细胞转录组分析中发现这三个生物标志物与T细胞、内皮细胞、兴奋性神经元、抑制性神经元和其他细胞密切相关。总之，IL18R1、NMUR1和RELA可能是PD炎症反应中的潜在治疗靶点和PD诊断的新生物标志物。
