目的: 通过生物信息学的方法挖掘帕金森病(Parkinson’s disease, PD)的关键基因,为PD提供新的诊断标志物及免疫细胞浸润特征。方法: 从高通量基因表达(gene expression omnibus, GEO)数据库下载合并GSE20163和GSE20164数据集并筛选差异表达基因(differential expression genes, DEGs)。采用基因本体(gene ontology, GO)和京都基因和基因组百科全书(kyoto encyclopedia of genes and genomes, KEGG)数据库分析预测DEGs的生物学功能,然后进行加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)识别PD相关模块基因,并对DEGs和PD相关模块基因取交集,使用最小绝对收缩和选择算法(least absolute shrinkage and selection operator, LASSO)回归分析缩小交集基因并确定PD关键基因,最后对关键基因进行免疫浸润分析,并用数据集GSE49036对上述4个基因进行验证。结果: 共筛选出34个DEGs,主要与神经递质转运、学习记忆和认知等生物学过程相关。共获得WGCNA关键模块基因41个,与DEGs取交集获得19个交集基因,最终通过LASSO回归分析确定SLC18A2、SV2C、CUX2和CALB1共4个PD关键基因,根据受试者工作特征(receiver operating characteristic,ROC)曲线显示4个关键基因诊断PD的准确度较高。与对照组相比,PD中未成熟树突状细胞以及γδT细胞表达相对较低,嗜中性粒细胞细胞表达较高。数据集GSE49036验证发现,SLC18A2、SV2C和CUX2在PD和对照组中基因表达差异有显著性意义,且ROC曲线显示诊断PD的准确性较高,而CALB1基因表达差异不显著。结论: 利用生物信息学方法筛选出4个PD关键致病基因,首次报道CUX2基因与PD相关性,为PD的诊断和疾病的发生发展机制提供新的线索。
Objective: To provide new diagnostic markers and immune infiltration pattern for Parkinson’s disease (PD) by identifying key genes of PD using bioinformatics analysis. Methods: The microarray expression data were downloaded from GSE20163 and GSE20164 in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the healthy control samples and PD samples were identified and the biological functions of DEGs were predicted using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases. Meanwhile,key module genes were screened via weighted gene co-expression network analysis (WGCNA), and the intersection of key module genes and DEGs was taken. Least absolute shrinkage and selection operator (LASSO) analysis was used to further screen candidate genes for PD. Finally, the immune infiltration was analyzed by a single-sample gene set enrichment analysis (ssGSEA) algorithm, and the above four genes were validated in GSE49036. Results: Thirty-four DEGs were identified, which were mainly related to biological processes such as neurotransmitter transport, learning and memory, and cognition. A unique gene module related to PD was identified and 19 PD-related genes were obtained by intersection of key module genes and DEGs. Four candidate genes (SLC18A2, SV2C, CUX2, and CALB1) were further identified through LASSO analysis. The receiver operating characteristic (ROC) curve indicated that the 4 candidate genes had a good performance in distinguishing the PD samples from healthy control samples. Comparing with the control group, the proportion of immature dendritic cells and γδ T cells in PD was relatively lower, and the proportion of neutrophils cells was higher. It was validated in GSE49036 that SLC18A2, SV2C, and CUX2 had significant differences in gene expression between the PD group and the control group. The ROC curve indicated 3 genes have a high degree of accuracy in diagnosing the PD, while the difference in CALB1 gene expression was not significant. Conclusion: Four candidate genes (SLC18A2, SV2C, CUX2, and CALB1) were identified by bioinformatics analysis and the correlation between the CUX2 gene and PD was reported for the first time, which provides a reference for exploring the diagnosis and development mechanism of PD.
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