Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis
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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。 Abstract:Objective To identify M1 macrophage-related genes in rejection after kidney transplantation and construct a risk prediction model for renal allograft survival. Methods GSE36059 and GSE21374 datasets after kidney transplantation were downloaded from Gene Expression Omnibus (GEO) database. GSE36059 dataset included the samples from the recipients with rejection and stable allografts. Using this dataset, weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted to screen the M1 macrophage-related differentially expressed gene (M1-DEG). Then, GSE21374 dataset (including the follow-up data of graft loss) was divided into the training set and validation set according to a ratio of 7∶3. In the training set, a multivariate Cox's model was constructed using the variables screened by least absolute shrinkage and selection operator (LASSO), and the ability of this model to predict allograft survival was evaluated. CIBERSORT was employed to analyze the differences of infiltrated immune cells between the high-risk group and low-risk group, and the distribution of human leukocyte antigen (HLA)-related genes was analyzed between two groups. Gene set enrichment analysis (GSEA) was used to further clarify the biological process and pathway enrichment in the high-risk group. Finally, the database was employed to predict the microRNA (miRNA) interacting with the prognostic genes. Results In the GSE36059 dataset, 14 M1-DEG were screened. In the GSE21374 dataset, Toll-like receptor 8 (TLR8), Fc gamma receptor 1B (FCGR1B), BCL2 related protein A1 (BCL2A1), cathepsin S (CTSS), guanylate binding protein 2(GBP2) and caspase recruitment domain family member 16 (CARD16) were screened by LASSO-Cox regression analysis, and a multivariate Cox's model was constructed based on these 6 M1-DEG. The area under curve (AUC) of receiver operating characteristic of this model for predicting the 1- and 3-year graft survival was 0.918 and 0.877 in the training set, and 0.765 and 0.736 in the validation set, respectively. Immune cell infiltration analysis showed that the infiltration of rest and activated CD4+ memory T cells, γδT cells and M1 macrophages were increased in the high-risk group (all P < 0.05). The expression level of HLA I gene was up-regulated in the high-risk group. GSEA analysis suggested that immune response and graft rejection were enriched in the high-risk group. CTSS interacted with 8 miRNA, BCL2A1 and GBP2 interacted with 3 miRNA, and FCGR1B interacted with 1 miRNA. Conclusions The prognostic risk model based on 6 M1-DEG has high performance in predicting graft survival, which may provide evidence for early interventions for high-risk recipients. -
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
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