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基于加权基因共表达网络鉴定肾移植术后排斥反应中巨噬细胞M1亚型相关基因

董博清, 李杨, 石玉婷, 等. 基于加权基因共表达网络鉴定肾移植术后排斥反应中巨噬细胞M1亚型相关基因[J]. 器官移植, 2023, 14(1): 83-92. doi: 10.3969/j.issn.1674-7445.2023.01.011
引用本文: 董博清, 李杨, 石玉婷, 等. 基于加权基因共表达网络鉴定肾移植术后排斥反应中巨噬细胞M1亚型相关基因[J]. 器官移植, 2023, 14(1): 83-92. doi: 10.3969/j.issn.1674-7445.2023.01.011
Dong Boqing, Li Yang, Shi Yuting, et al. Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis[J]. ORGAN TRANSPLANTATION, 2023, 14(1): 83-92. doi: 10.3969/j.issn.1674-7445.2023.01.011
Citation: Dong Boqing, Li Yang, Shi Yuting, et al. Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis[J]. ORGAN TRANSPLANTATION, 2023, 14(1): 83-92. doi: 10.3969/j.issn.1674-7445.2023.01.011

基于加权基因共表达网络鉴定肾移植术后排斥反应中巨噬细胞M1亚型相关基因

doi: 10.3969/j.issn.1674-7445.2023.01.011
基金项目: 

国家自然科学基金 81970668

陕西省重点研发计划 2022SF-148

西安交通大学基本科研业务基金 xzy012021060

详细信息
    作者简介:
    通讯作者:

    薛武军,博士,主任医师,研究方向为肾移植,Email: xwujun126@xjtu.edu.cn

  • 中图分类号: R617, R392.12

Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis

More Information
  • 摘要:   目的  鉴定肾移植术后排斥反应中巨噬细胞M1亚型表达的相关基因并构建风险模型预测移植肾存活。  方法  在基因表达综合(GEO)数据库下载肾移植术后的GSE36059及GSE21374数据集。GSE36059包括发生排斥反应和稳定移植物的样本,使用该数据集进行加权基因共表达网络分析(WGCNA)和差异分析筛选差异表达的巨噬细胞M1亚型相关差异表达基因(M1-DEG)。随后将GSE21374数据集(包含了移植物丢失的随访数据)按照7∶3拆分为训练集以及验证集,在训练集中使用最小绝对收缩和选择算法(LASSO)筛选变量构建多因素Cox模型,并评估模型预测移植物存活的能力。使用CIBERSORT分析高、低风险组浸润的免疫细胞的差异,并分析两组间人类白细胞抗原(HLA)相关基因的分布,基因集富集分析(GSEA)用于进一步明确高风险组中富集的生物学过程以及通路。最后使用数据库预测与预后基因互作的微小核糖核酸(miRNA)。  结果  在GSE36059数据集中,筛选得到14个M1-DEG。在GSE21374数据集中,使用LASSO-Cox回归筛选出Toll样受体8(TLR8)、Fc γ受体1B(FCGR1B)、BCL2相关蛋白A1(BCL2A1)、组织蛋白酶S(CTSS)、鸟苷酸结合蛋白2(GBP2)及半胱氨酸天冬氨酸蛋白酶招募域家族成员16(CARD16),基于这6个M1-DEG构建多因素Cox模型。风险模型在训练集中预测1年及3年移植物存活的受试者工作特征曲线下面积(AUC)分别为0.918和0.877,在验证集中预测1年及3年移植物存活的AUC分别为0.765及0.736。免疫浸润分析表明,高风险组静息及活化的CD4+记忆T细胞、γδT细胞、巨噬细胞M1亚型浸润增多(均为P < 0.05)。高风险组HLA Ⅰ类基因表达上调。GSEA分析表明,高风险组免疫反应及移植物排斥反应富集。CTSS与8个miRNA相互作用、BCL2A1和GBP2与3个miRNA相互作用、FCGR1B与1个miRNA相互作用。  结论  本研究基于6个M1-DEG构建的预后风险模型对于预测移植肾存活具有良好的表现,可为早期对高风险受者干预提供依据。

     

  • 图  1  加权基因共表达网络分析

    注:A图为样本聚类图; B图为根据R2和平均连通性确定软阈值; C图为基因特征树图; D图为基因聚类树状图; E、F图为基因模块与临床特征相关性热图; G图为模块基因相关性散点图。

    Figure  1.  Analysis of weighted gene co-expression networks

    图  2  差异表达的火山图和韦恩图

    注:A图为火山图; B图为韦恩图。

    Figure  2.  Differentially expressed volcano plots and Wayne diagrams

    图  3  基于M1-DEG构建风险预后模型

    注:A图为在使用LASSO-Cox筛选构建的模型; B图为根据MMSE选择LASSO的惩罚系数(λ); C图为预测肾移植术后移植物存活的列线图模型。

    Figure  3.  Risk prognosis model based on M1-DEG

    图  4  6个基因模型在训练集的表现

    注:A图为根据风险评分将受者分为高风险和低风险组; B图为受者风险评分与移植物存活间的关系; C图为预测移植物存活的受试者工作特征曲线; D图为移植物存活的生存分析。

    Figure  4.  Six gene models performed in the training set

    图  5  6个基因模型在验证集的表现

    注:A图为根据风险评分将受者分为高风险和低风险组; B图为受者的风险评分和移植物存活间的关系; C图为预测移植物存活的受试者工作特征曲线; D图为移植物存活的生存分析。

    Figure  5.  Six gene models performed in the validation set

    图  6  高风险组和低风险组的免疫浸润及相关基因分布分析

    注:A图为免疫浸润分析; B图为预后基因和HLA基因分布。

    Figure  6.  Immune infiltration and related gene distribution analysis between high-risk group and low-risk group

    图  7  基因集富集分析

    注:A图为高风险组中富集的生物学过程; B图为高风险组中富集的KEGG通路。

    Figure  7.  Analysis of gene set enrichment

    图  8  miRNA和预后基因互作网络

    Figure  8.  miRNA and prognostic gene interaction network

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出版历程
  • 收稿日期:  2022-09-14
  • 网络出版日期:  2023-01-17
  • 刊出日期:  2023-01-15

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