通过整合生物信息学分析鉴定帕金森病的新型免疫相关生物标志物和治疗药物。
Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis.
机构信息
Department of Neurology, Tianjin First Central Hospital, Nankai District, Tianjin, China.
出版信息
Medicine (Baltimore). 2023 Aug 4;102(31):e34456. doi: 10.1097/MD.0000000000034456.
BACKGROUND
The present study was designed to identify immune-related biomarker and candidate drugs for Parkinson disease (PD) by weighted gene co-expression network analysis.
METHODS
Differentially expressed genes were identified in PD and healthy samples in the Gene Expression Omnibus (GEO) database. Besides, immune-related genes were obtained from the immunology database. Then, a co-expression network was constructed by the weighted gene co-expression network analysis package. Diagnostic model for PD was constructed by Lasso and multivariate Cox regression. Furthermore, differentially expressed genes (DEGs) were used to establish PPI and competing endogenous RNA (ceRNA) networks. Functional enrichment and pathway analysis were performed. Drug-hub gene interaction analysis was performed via DGIdb database.
RESULTS
PD samples and normal samples were found to have 220 upregulated genes and 216 downregulated genes in the GSE6613 dataset. The differentially expressed genes contained 50 immune-related genes, with 40 upregulated genes and 10 downregulated genes. We obtained 7 hub genes by intersecting the DEGs and candidate hub genes. As potential diagnostic markers, 2 immune-related DEGs were identified among the 7 hub genes. According to functional enrichment analysis, these DEGs were mainly enriched in immune response, inflammatory response, and cytokine-cytokine receptor interactions. Totally, we obtained 182 drug-gene interaction pairs in Drug-Gene Interaction database (DGIdb).
CONCLUSION
Our results revealed crucial genes and candidate drugs for PD patients and deepen our understanding of the molecular mechanisms involved in PD.
背景
本研究旨在通过加权基因共表达网络分析，鉴定帕金森病（PD）的免疫相关生物标志物和候选药物。
方法
从基因表达综合数据库（GEO）中鉴定 PD 和健康样本中的差异表达基因，同时从免疫学数据库中获取免疫相关基因。然后，使用加权基因共表达网络分析软件包构建共表达网络。通过 Lasso 和多变量 Cox 回归构建 PD 的诊断模型。此外，使用差异表达基因（DEGs）构建 PPI 和竞争性内源 RNA（ceRNA）网络。进行功能富集和通路分析。通过 DGIdb 数据库进行药物-枢纽基因互作分析。
结果
在 GSE6613 数据集，PD 样本和正常样本分别有 220 个上调基因和 216 个下调基因。差异表达基因中包含 50 个免疫相关基因，其中有 40 个上调基因和 10 个下调基因。通过交集 DEGs 和候选枢纽基因，我们获得了 7 个枢纽基因。作为潜在的诊断标志物，在 7 个枢纽基因中鉴定出 2 个免疫相关 DEGs。根据功能富集分析，这些 DEGs 主要富集在免疫反应、炎症反应和细胞因子-细胞因子受体相互作用。在药物-基因相互作用数据库（DGIdb）中，我们总共获得了 182 个药物-基因相互作用对。
结论
本研究结果揭示了 PD 患者的关键基因和候选药物，加深了我们对 PD 涉及的分子机制的理解。
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