2022 年第 9 期 第 17 卷

基于基因表达综合数据库和生物信息学分析筛选小鼠支气管肺发育不良相关的潜在关键基因

Screening potential key genes related to bronchopulmonary dysplasia in mice based on gene expression omnibus database and bioinformatics analysis

作者：成小蓉姜军吴大珍莫连芹黄栋

英文作者：Cheng Xiaorong Jiang Jun Wu Dazhen Mo Lianqin Huang Dong

单位：贵州医科大学附属人民医院贵州省人民医院儿童重症医学科，贵阳550002

英文单位：Department of Pediatric Intensive Care Medicine Affiliated People′s Hospital of Guizhou Medical University Guizhou Provincial People′s Hospital Guiyang 550002 China

关键词：支气管肺发育不良；生物信息学；差异基因；基因表达综合数据库

英文关键词：Bronchopulmonarydysplasia;Bioinformatics;Differentialgenes;Geneexpressionomnibusdatabase

• 摘要：

• 目的 筛选小鼠支气管肺发育不良（BPD）相关的潜在关键基因。方法  从基因表达综合（GEO）数据库中获取BPD小鼠GSE25286和GSE29632数据集，筛选出2个数据集中共同的差异基因。通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路分析上述差异基因的生物学作用。利用STRING数据库构建蛋白质-蛋白质相互作用网络（PPI），利用Cytoscape3.7.2的CytoHubba插件筛选PPI网络中关键基因，再利用clusterProfiler包的关键基因进行GO和KEGG分析。结果  GSE25286和GSE29632数据集中共筛选出45个共同差异基因。GO富集分析表明差异基因主要富集于受体配体活性、细胞因子活性、生长因子活性、趋化因子活性、受体复合物、质膜受体复合体等，KEGG结果显示差异基因主要富集于细胞因子受体相互作用、磷脂酰肌醇-3-激酶/蛋白激酶B信号通路、肿瘤坏死因子信号通路、p53信号通路、缺氧诱导因子1信号通路等。在PPI网络中筛选出7个关键基因：分别为早期生长反应1、细胞周期蛋白依赖性激酶抑制因子1A、丝氨酸蛋白酶抑制因子1、生长分化因子15、细胞因子信号传导抑制因子3、血小板反应蛋白1、抑瘤素M受体基因。结论 BPD的生物信息学分析可发现相关潜在关键基因，可能为探索BPD潜在发病机制和治疗靶点提供理论依据。

• Objective  To screen potential key genes related to bronchopulmonary dysplasia (BPD) in mice. Methods  GSE25286 and GSE29632 data sets of BPD mice were obtained from gene expression omnibus (GEO) database to screen the differential genes common to both data sets. The biological effects of these genes were analyzed by gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways. Protein-protein interaction (PPI) network  was established by STRING database.  The key genes of PPI network were screened by Cytoscape3.7.2 CytoHubba plug-in, and then GO and KEGG analysis were performed through key genes of clusterProfiler package. Results  There were 45 common differential genes in GSE25286 and GSE29632 data sets. GO enrichment analysis showed that the differential genes were mainly concentrated in receptor ligand activity, cytokine activity, growth factor activity, chemokine activity, receptor complex, plasma membrane receptor complex, etc. KEGG showed that differential genes were mainly concentrated in cytokine receptors, phosphoinositide-3-kinase/protein kinase B signaling pathway, tumor necrosis factor signaling pathway, p53 signaling pathway, and hypoxia-inducible factor 1 signaling pathway, etc. Seven key genes were screened from PPI network: early growth response 1, cell cycle cyclin-dependent kinase inhibitor 1A, serpin peptidase inhibitoe 1, growth differentiation factor 15, suppressor of cytokine signaling-3, thrombospondin 1, and oncostatin M receptor. Conclusion  Bioinformatics analysis of BPD can found relevant potential key genes, may provide supporting evidence for exploring the underlying pathogenesis and therapeutic targets of BPD.