Cao Yun, Kong Ling-Bo, Huang Xing, Li Xiao-Lin, Chang Jing-Ling, Gao Ying
Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine Beijing 100700, China.
Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine Beijing 100700, China Institute for Brain Disorders, Beijing University of Chinese Medicine Bejing 100700, China.
Zhongguo Zhong Yao Za Zhi. 2021 Apr;46(7):1803-1812. doi: 10.19540/j.cnki.cjcmm.20210218.401.
The aim of this paper was to explore the key genes and pathogenesis of ischemic stroke(IS) by bioinformatics, and predict the potential traditional Chinese medicines for IS. Based on the gene-chip raw data set of GSE22255 from National Center of Biotechnology Information(NCBI), the article enrolled in 20 patients with ischemic stroke and 20 sex-and age-matched controls, and differentially expressed genes(DEGs) were screened based on R language software. The DAVID tool and R language software were used to perform gene ontology(GO) biological process enrichment analysis and Kyoto encyclopedia of genes and gnomes(KEGG) pathway enrichment analysis. The DEGs were imported into STRING to construct a protein-protein interaction network, and the Molecular Complexity Module(MCODE) plug-in of Cytoscape software was used to visualize and analyze the key functional modules. Moreover, the core genes and the medical ontology information retrieval platform(Coremine Medical) were mapped to each other to screen the traditional Chinese medicines and construct drug-active ingredient-target network. Compared with healthy controls, 14 DEGs were obtained, of which 12 genes were up-regulated and 2 genes were down-regulated. DEGs were mainly involved in immune response, inflammatory process, signal transduction, and cell proliferation regulation. The interleukin-17(IL-17), nuclear factor kappaB(NF-κB), tumor necrosis factor(TNF), nucleotide binding oligomerization domain(NOD)-like receptor and other signaling pathways were involved in KEGG pathway enrichment analysis. The key modules of the DEGs-encoding protein interaction network mainly focused on 7 genes of TNF, JUN, recombinant immediate early response 3(IER3), recombinant early growth response protein 1(EGR1), prostaglandin-endoperoxide synthase 2(PTGS2), C-X-C motif chemokine ligand 8(CXCL8) and C-X-C motif chemokine ligand 2(CXCL2), which were involved in biological processes widely such as neuroinflammation and immunity. TNF and JUN were the key nodes in this module, which might become potential biological markers for diagnosis and prognosis evaluation of IS. The potential traditional Chinese medicines for the treatment of IS includes Salviae Miltiorrhizae Radix et Rhizoma, Croci Stigma, Scutellariae Radix, and Cannabis Fructus. The occurrence of stroke was the result of multiple factors. Dysregulation of genes and pathways related to immune regulation and inflammation may be the key link for the development of IS. This study provided research direction and theoretical basis for further exploring the mechanism of action of traditional Chinese medicine in the treatment of IS and searching for potential drug targets.
本文旨在通过生物信息学方法探索缺血性脑卒中(IS)的关键基因和发病机制,并预测治疗IS的潜在中药。基于美国国立生物技术信息中心(NCBI)的基因芯片原始数据集GSE22255,纳入20例缺血性脑卒中患者和20例性别及年龄匹配的对照,基于R语言软件筛选差异表达基因(DEG)。使用DAVID工具和R语言软件进行基因本体论(GO)生物过程富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。将DEG导入STRING构建蛋白质-蛋白质相互作用网络,使用Cytoscape软件的分子复杂性模块(MCODE)插件对关键功能模块进行可视化和分析。此外,将核心基因与医学本体信息检索平台(Coremine Medical)相互映射以筛选中药并构建药物-活性成分-靶点网络。与健康对照相比,共获得14个DEG,其中12个基因上调,2个基因下调。DEG主要参与免疫反应、炎症过程、信号转导和细胞增殖调控。KEGG通路富集分析涉及白细胞介素-17(IL-17)、核因子κB(NF-κB)、肿瘤坏死因子(TNF)、核苷酸结合寡聚化结构域(NOD)样受体等信号通路。DEG编码的蛋白质相互作用网络的关键模块主要集中在TNF、JUN、重组即刻早期反应3(IER3)、重组早期生长反应蛋白1(EGR1)、前列腺素内过氧化物合酶2(PTGS2)、C-X-C基序趋化因子配体8(CXCL8)和C-X-C基序趋化因子配体2(CXCL2)这7个基因,它们广泛参与神经炎症和免疫等生物学过程。TNF和JUN是该模块中的关键节点,可能成为IS诊断和预后评估的潜在生物学标志物。治疗IS的潜在中药包括丹参、西红花、黄芩和火麻仁。脑卒中的发生是多种因素作用的结果。与免疫调节和炎症相关的基因和通路失调可能是IS发病的关键环节。本研究为进一步探索中药治疗IS的作用机制及寻找潜在药物靶点提供了研究方向和理论依据。