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肺缺血-再灌注核心基因介导的竞争性内源性RNA网络的构建

李晓凤, 唐明政, 刘禧禧, 等. 肺缺血-再灌注核心基因介导的竞争性内源性RNA网络的构建[J]. 器官移植, 2024, 15(1): 70-81. doi: 10.3969/j.issn.1674-7445.2023166
引用本文: 李晓凤, 唐明政, 刘禧禧, 等. 肺缺血-再灌注核心基因介导的竞争性内源性RNA网络的构建[J]. 器官移植, 2024, 15(1): 70-81. doi: 10.3969/j.issn.1674-7445.2023166
Li Xiaofeng, Tang Mingzheng, Liu Xixi, et al. Construction of competitive endogenous RNA network mediated by lung ischemia-reperfusion core genes[J]. ORGAN TRANSPLANTATION, 2024, 15(1): 70-81. doi: 10.3969/j.issn.1674-7445.2023166
Citation: Li Xiaofeng, Tang Mingzheng, Liu Xixi, et al. Construction of competitive endogenous RNA network mediated by lung ischemia-reperfusion core genes[J]. ORGAN TRANSPLANTATION, 2024, 15(1): 70-81. doi: 10.3969/j.issn.1674-7445.2023166

肺缺血-再灌注核心基因介导的竞争性内源性RNA网络的构建

doi: 10.3969/j.issn.1674-7445.2023166
基金项目: 甘肃省自然科学基金(20JR5RA159);甘肃中医药大学2023年度研究生创新创业基金项目32号
详细信息
    作者简介:
    通讯作者:

    张凌云(ORCID 0009-0007-8881-8374),副主任医师,硕士研究生导师,研究方向为围手术期器官保护,Email:zhangly8848@126.com

  • 中图分类号: R617, R563

Construction of competitive endogenous RNA network mediated by lung ischemia-reperfusion core genes

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  • 摘要:   目的  分析肺缺血-再灌注损伤发生的核心基因并构建竞争性内源性RNA(ceRNA)网络。  方法  从基因表达综合(GEO)数据库下载GSE145989的原始数据当作训练集,GSE172222和GSE9634数据集作为验证集,并鉴定差异表达基因(DEG)。进行基因本体(GO)、京都基因与基因组百科全书(KEGG)富集分析。构建蛋白质-蛋白质相互作用(PPI)网络,筛选核心基因并分析核心基因的诊断价值、免疫细胞的免疫浸润水平。构建并验证ceRNA网络。分析基于ceRNA网络的靶向药物。  结果  共检测到179个DEG,其中61个下调基因,118个上调基因。GO分析结果显示,DEG与细胞迁移、分化和调节等生物学过程有关;与内吞囊泡膜、浆膜和膜筏等细胞成分有关;与趋化因子受体、G蛋白偶联受体、免疫受体活性和抗原结合等分子功能有关。KEGG分析显示DEG参与肿瘤坏死因子(TNF)、Wnt、白细胞介素(IL)-17和核因子(NF)-κB等信号通路。PPI网络提示CD8A、IL2RG、STAT1、CD3G和SYK是肺缺血-再灌注损伤的核心基因。ceRNA网络提示miR-146a-3p、miR-28-5p和miR-593-3p与CD3G的表达相关;miR-149-3p、miR-342-5p、miR-873-5p和miR-491-5p与IL2RG表达相关;miR-194-3p、miR-512-3p、miR-377-3p和miR-590-3p与SYK表达相关;miR-590-3p和miR-875-3p与CD8A表达相关;miR-143-5p、miR-1231、miR-590-3p、miR-875-3p与STAT1表达相关。CD3G有13种靶向药物,IL2RG有4种靶向药物,SYK有28种靶向药物,lncRNA MUC2有3种靶向药物,未发现CD8A、STAT1和其他ceRNA网络基因的靶向药物。  结论   CD8A、IL2RG、STAT1、CD3G和SYK是肺缺血-再灌注损伤的核心基因,对核心基因进行研究分析可能有助于肺缺血-再灌注损伤的诊断,并提供新的研究思路和治疗靶点。

     

  • 图  1  DEG的GO、KEGG和免疫细胞浸润分析

    注:A图为前100个DEG;B图和C图为GO分析;D图为KEGG分析;E图为免疫细胞浸润分析。

    Figure  1.  GO, KEGG and immune cell infiltration analysis of DEG

    图  2  核心基因的筛选

    注:A图为DEG的PPI网络,绿色为下调基因,红色为上调基因;B图为基因打分,红色为得分;C图为核心基因;D图为核心基因的相关性分析。

    Figure  2.  Screening of the core genes

    图  3  核心基因的验证

    注:A图为GSE145989数据集核心基因的表达;B图为GSE172222数据集核心基因的表达;C图为GSE9634数据集核心基因的表达;D图为核心基因在GSE172222数据集中的诊断效力;E图为核心基因在GSE9634数据集中的诊断效力。

    Figure  3.  Validation of core genes

    图  4  核心基因和免疫细胞相关性分析

    Figure  4.  Correlation analysis of core genes and immune cell

    图  5  ceRNA网络构建与表达验证

    注:A图为基于核心基因mRNA-miRNA-lncRNA的ceRNA网络;B图为ceRNA网络成员表达情况。

    Figure  5.  ceRNA network construction and expression validation

    图  6  标记基因靶向药物的预测

    注:A图为可能靶向SYK的药物;B图为可能靶向CD3G的药物;C图为可能靶向IL2RG的药物;D图为可能靶向lncRNA MUC2的药物。

    Figure  6.  Prediction of marker gene-targeted drugs

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  • 收稿日期:  2023-08-26
  • 网络出版日期:  2023-11-29
  • 刊出日期:  2024-01-11

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