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使用综合生物信息学方法鉴定与脊髓损伤相关的基因共表达模块、枢纽基因和通路。

Identification of gene coexpression modules, hub genes, and pathways related to spinal cord injury using integrated bioinformatics methods.

作者信息

Wang Tienan, Wu Baolin, Zhang Xiuzhi, Zhang Meng, Zhang Shuo, Huang Wei, Liu Tao, Yu Weiting, Li Junlei, Yu Xiaobing

机构信息

Department of Orthopaedics, Zhongshan Hospital of Dalian University, Dalian, China.

出版信息

J Cell Biochem. 2019 May;120(5):6988-6997. doi: 10.1002/jcb.27908. Epub 2019 Jan 17.

Abstract

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway "natural killer cell-mediated cytotoxicity" were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the "natural killer cell-mediated cytotoxicity" pathway.

摘要

脊髓损伤(SCI)的特征是神经元大量丧失和轴突再生受到抑制。与SCI诱导的免疫抑制相关的潜在机制仍不清楚。加权基因共表达网络分析(WGCNA)目前广泛应用于识别与疾病临床特征相关的共表达模块、枢纽基因和通路。我们进行这项研究以识别与SCI发展相关的枢纽基因。下载了基因表达综合数据库(GEO)数据集GSE45006和GSE20907,并使用WGCNA检测它们之间的显著相关性和连通性。在满足WGCNA近似无标度拓扑的前提下,从主要的GSE45006数据中识别出三个显著的共表达模块,包括567个特征基因。进一步的生物信息学分析表明,这些特征基因参与了SCI中的炎症和免疫反应。通过短时间序列表达挖掘、蛋白质-蛋白质相互作用网络和功能富集分析,确定了三个枢纽基因Rac2、Itgb2和Tyrobp以及一条“自然杀伤细胞介导的细胞毒性”通路。基于GSE45006和GSE20907数据集,证实了SCI后0、3、7和14天Rac2、Itgb2和Tyrobp基因的表达模式逐渐上调。最后,我们发现Rac2、Itgb2和Tyrobp基因可能通过“自然杀伤细胞介导的细胞毒性”通路在SCI发展中发挥关键作用。

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