基于生物信息学方法筛选重度抑郁症关键基因及潜在治疗药物预测
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山东省医药卫生科技发展计划(2017WS748)


Bioinformatics-based approach for screening hub genes and predicting potential therapeutic agents for major depressive disorder
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    目的 通过生物信息学方法筛选重度抑郁症关键基因及潜在治疗药物。方法 收集 GEO 公共数据库中 GSE98793、GSE76826 数据集作为数据来源,采用在线工具及 R 语言进行数据分析、差异 表达基因筛选和富集分析。蛋白质 - 蛋白质互作网络分析采用 STRING 数据库进行。关键基因的筛选 采用 Cytoscape 软件。采用受试者工作特征(ROC)曲线评价诊断价值,采用 Connectivity Map 数据库预 测治疗重度抑郁症的小分子药物。结果 筛选出 TLR2、CD28、IL7R、IRF4、MAPK14 共 5 个关键基因。 ROC 曲线结果显示,5 个关键基因在 GSE98793、GSE76826 数据集中的 ROC 曲线下面积为 0.616~0.804。 预测得到 UBP-302、ketanserin、CS-1657、androstenol、taurocholic-acid 共 5 种小分子药物可能对改善重度 抑郁症患者症状有潜在价值。结论 TLR2、CD28、IL7R、IRF4、MAPK14 可能是重度抑郁症患者发病的 关键基因,UBP-302、ketanserin、CS-1657、androstenol、taurocholic-acid 5 个小分子化合物可能对于抑郁症 状改善有一定效果,可为后续药物研究提供思路。

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    Objective To screen hub genes and potential therapeutic agents for major depressive disorder (MDD) by bioinformatics methods. Methods The GSE98793 and GSE76826 datasets in the GEO database were used as the data source. Data analysis, differential expression gene (DEG) screening and enrichment analysis were carried out using online tools and R soft. STRING database was used to analyze proteinprotein interaction network, and Cytoscape was used to screen hub genes. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value, and Connectivity Map database was used to predict small molecule drugs for severe MDD. Results A total of 5 hub genes (TLR2, CD28, IL7R, IRF4, MAPK14) were screened out. The ROC curve results showed that the area under the ROC curve of the 5 hub genes in the GSE98793 and GSE76826 datasets ranges from 0.616 to 0.804. It was predicted that UBP-302, ketanserin, CS- 1657, androstenol, taurocholic acid, a total of 5 small molecule drugs, may have potential value in improving the symptoms of patients with major depressive disorder. Conclusions TLR2, CD28, IL7R, IRF4, MAPK14 may be key genes in the pathogenesis of MDD patients. The 5 small molecule compounds UBP-302, ketanserin, CS-1657, androstenol, and taurocholic-acid may have a certain effect on improving depressive symptoms and can provide ideas for subsequent drug research.

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王相文,王圣海,孙辰辉,曲春晖,孙平.基于生物信息学方法筛选重度抑郁症关键基因及潜在治疗药物预测[J].神经疾病与精神卫生,2023,23(6):
DOI :10.3969/j. issn.1009-6574.2023.06.004.

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  • 在线发布日期: 2023-07-14