Differential gene expression and immune profiling in Parkinson's disease: unveiling potential candidate biomarkers.

IF 2.2 3区 医学 Q3 CLINICAL NEUROLOGY
Xiuping Yao, Peng Wang, Zhenqiang Huang, Lingyun Li
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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

帕金森病的差异基因表达和免疫谱:揭示潜在的候选生物标志物。
背景: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的多方面相互作用。尽管临床诊断明确,但PD的误诊率仍在20%左右。本研究旨在发现新的PD诊断生物标志物,探讨其发病机制,以提高PD患者的早期干预和有效管理。方法:使用5个pd相关的GEO数据集:4个用于训练(GSE7621、GSE8397、GSE20186和GSE20292), 1个用于验证(GSE26927)。基因表达分析包括批量校正和“RobustRankAggreg”(RRA)方法。通过基因本体(GO)和京都基因与基因组百科全书(KEGG)将差异表达基因(DEGs)与功能联系起来。利用CytoHubba在Cytoscape中鉴定枢纽基因,并进行ROC分析验证。实时定量聚合酶链反应(RT-qPCR)证实了PD患者黑质中hub基因的表达。CIBERSORT与Wilcoxon检验和最小绝对收缩和选择算子(LASSO)回归一起,分析了PD患者和健康对照(HC)之间免疫细胞丰度的差异。斯皮尔曼在R中的等级相关性探讨了生物标志物和免疫细胞之间的联系。结果:两种方法交叉鉴定出PD患者的124个DEGs。GO分析显示神经递质转运富集,而KEGG分析发现多巴胺能突触通路参与。使用“UpSet”R包鉴定出三个中心基因(DDC, NEFL和SLC18A2), PD患者中它们的表达明显低于HC组(PD中所有p + T细胞),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。结论:该研究确定SLC18A2是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疾病的发生和进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Neurology
BMC Neurology 医学-临床神经学
CiteScore
4.20
自引率
0.00%
发文量
428
审稿时长
3-8 weeks
期刊介绍: BMC Neurology is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of neurological disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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