Huang Junming, Li Bowen, Wei Huangwei, Li Chengxin, Liu Chao, Mi Hua, Chen Shaohua
Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, 530000, Guangxi, China.
Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, Guangxi, China.
Sci Rep. 2024 Jan 25;14(1):2167. doi: 10.1038/s41598-024-52276-0.
Parkinson's disease (PD) is a progressive neurodegenerative disease whose etiology is attributed to development of Lewy bodies and degeneration of dopaminergic neurons in the substantia nigra (SN). Currently, there are no definitive diagnostic indicators for PD. In this study, we aimed to identify potential diagnostic biomarkers for PD and analyzed the impact of immune cell infiltrations on disease pathogenesis. The PD expression profile data for human SN tissue, GSE7621, GSE20141, GSE20159, GSE20163 and GSE20164 were downloaded from the Gene Expression Omnibus (GEO) database for use in the training model. After normalization and merging, we identified differentially expressed genes (DEGs) using the Robust rank aggregation (RRA) analysis. Simultaneously, DEGs after batch correction were identified. Gene interactions were determined through venn Diagram analysis. Functional analyses and protein-protein interaction (PPI) networks were used to the identify hub genes, which were visualized through Cytoscape. A Lasso Cox regression model was employed to identify the potential diagnostic genes. The GSE20292 dataset was used for validation. The proportion of infiltrating immune cells in the samples were determined via the CIBERSORT method. Sixty-two DEGs were screened in this study. They were found to be enriched in nerve conduction, dopamine (DA) metabolism, and DA biosynthesis Gene Ontology (GO) terms. The PPI network and Lasso Cox regression analysis revealed seven potential diagnostic genes, namely SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1, were subsequently validated in peripheral blood samples obtained from healthy control (HC) and PD patients, as well as in the GSE20292 dataset. The results revealed the exceptional sensitivity and specificity of these genes in PD diagnosis and monitoring. Moreover, PD patients exhibited a higher number of plasma cells, compared to HC individuals. The SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1 are potential diagnostic biomarkers for PD. Our findings also reveal the essential roles of immune cell infiltration in both disease onset and trajectory.
帕金森病(PD)是一种进行性神经退行性疾病,其病因归因于路易小体的形成和黑质(SN)中多巴胺能神经元的退化。目前,尚无明确的帕金森病诊断指标。在本研究中,我们旨在识别帕金森病的潜在诊断生物标志物,并分析免疫细胞浸润对疾病发病机制的影响。从基因表达综合数据库(GEO)下载了人类黑质组织的帕金森病表达谱数据GSE7621、GSE20141、GSE20159、GSE20163和GSE20164,用于训练模型。经过标准化和合并后,我们使用稳健秩聚合(RRA)分析识别差异表达基因(DEG)。同时,识别批次校正后的DEG。通过维恩图分析确定基因相互作用。使用功能分析和蛋白质-蛋白质相互作用(PPI)网络来识别枢纽基因,并通过Cytoscape进行可视化。采用套索Cox回归模型识别潜在的诊断基因。使用GSE20292数据集进行验证。通过CIBERSORT方法确定样本中浸润免疫细胞的比例。本研究筛选出62个DEG。发现它们在神经传导、多巴胺(DA)代谢和DA生物合成基因本体(GO)术语中富集。PPI网络和套索Cox回归分析揭示了7个潜在的诊断基因,即SLC18A2、TAC1、PCDH8、KIAA0319、PDE6H、AXIN1和AGTR1,随后在从健康对照(HC)和帕金森病患者获得的外周血样本以及GSE20292数据集中得到验证。结果显示这些基因在帕金森病诊断和监测中具有极高的敏感性和特异性。此外,与HC个体相比,帕金森病患者的浆细胞数量更多。SLC18A2、TAC1、PCDH8、KIAA0319、PDE6H、AXIN1和AGTR1是帕金森病的潜在诊断生物标志物。我们的研究结果还揭示了免疫细胞浸润在疾病发生和发展过程中的重要作用。