Yao Xiuping, Wang Peng, Huang Zhenqiang, Li Lingyun
Department of Clinical Laboratory, Lishui City People's Hospital, Lishui, 323000, Zhejiang Province, China.
BMC Neurol. 2025 Aug 27;25(1):354. doi: 10.1186/s12883-025-04388-x.
Parkinson's disease (PD) represents a common neurodegenerative disorder characterized by a multifaceted interaction with immune infiltration. Despite a well-defined clinical diagnosis, the misdiagnosis rate of PD remains around 20%. The aim of this study is to discover new diagnostic biomarkers for PD and investigate their pathogenesis to improve early intervention and effective management of patients with PD.
Five PD-related GEO datasets were used: four for training (GSE7621, GSE8397, GSE20186, and GSE20292) and one for validation (GSE26927). Gene expression analysis included batch correction and "RobustRankAggreg" (RRA) methods. Differentially expressed genes (DEGs) were linked to functions via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were identified using CytoHubba in Cytoscape and validated with ROC analysis. Real-time quantitative polymerase chain reaction (RT-qPCR) confirmed hub gene expression in PD patients' substantia nigra. CIBERSORT, along with the Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression, analyzed differences in immune cell abundance between PD patients and healthy controls (HC). Spearman's rank correlation in R explored the link between biomarkers and immune cells.
The intersection of two methods identified 124 DEGs in PD. GO analysis revealed enrichment in neurotransmitter transport, while KEGG analysis identified involvement in the dopaminergic synapse pathway. Three hub genes (DDC, NEFL, and SLC18A2) were identified using the "UpSet" R package, and their expression was significantly lower in PD patients than in the HC group (all p < 0.05), as confirmed by RT-qPCR. LASSO regression and ROC analysis demonstrated that SLC18A2 could diagnose PD with high specificity and sensitivity in both training (0.85 and 0.84) and validation sets (1.00 and 0.75). CIBERSORT analysis showed increased memory B cells, activated mast cells, NK cells, and CD8 T cells in PD, with notable differences in the abundance of memory B cells and activated mast cells between PD and HC.
The study identifies SLC18A2 as a potential candidate biomarker for PD and emphasizes the involvement of memory B cells and activated mast cells in the onset and progression of the disease.
帕金森病（PD）是一种常见的神经退行性疾病，其特征是与免疫浸润存在多方面的相互作用。尽管临床诊断明确，但PD的误诊率仍约为20%。本研究的目的是发现PD的新诊断生物标志物，并研究其发病机制，以改善PD患者的早期干预和有效管理。
使用了五个与PD相关的GEO数据集：四个用于训练（GSE7621、GSE8397、GSE20186和GSE20292），一个用于验证（GSE26927）。基因表达分析包括批次校正和“稳健秩聚合”（RRA）方法。差异表达基因（DEG）通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）与功能相关联。使用Cytoscape中的CytoHubba识别枢纽基因，并通过ROC分析进行验证。实时定量聚合酶链反应（RT-qPCR）证实了PD患者黑质中枢纽基因的表达。CIBERSORT结合Wilcoxon检验和最小绝对收缩和选择算子（LASSO）回归，分析了PD患者和健康对照（HC）之间免疫细胞丰度的差异。R中的Spearman秩相关分析了生物标志物与免疫细胞之间的联系。
两种方法的交集在PD中鉴定出124个DEG。GO分析显示在神经递质转运方面富集，而KEGG分析确定其参与多巴胺能突触途径。使用“UpSet”R包鉴定出三个枢纽基因（DDC、NEFL和SLC18A2），RT-qPCR证实，它们在PD患者中的表达明显低于HC组（所有p<0.05）。LASSO回归和ROC分析表明，SLC18A2在训练集（0.85和0.84）和验证集（1.00和0.75）中均能以高特异性和敏感性诊断PD。CIBERSORT分析显示，PD中记忆B细胞、活化肥大细胞、NK细胞和CD8 T细胞增加，PD和HC之间记忆B细胞和活化肥大细胞的丰度存在显著差异。
该研究确定SLC18A2为PD的潜在候选生物标志物，并强调记忆B细胞和活化肥大细胞参与了该疾病的发生和发展。
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