生信分析肺纤维化TDP-43相关基因表达及免疫浸润

Expression and immunological infiltration of TDP-43-related genes in idiopathic pulmonary fibrosis based on bioinformatics analysis

[目的]基于生信分析特发性肺纤维化(IPF)TDP-43相关基因的表达及免疫浸润的关系。[方法]GEO数据库获取转录组数据,与TDP-43的相关性分析(|相关性系数|>0.75)获得TDP-43相关基因,并通过差异分析得到TDP-43相关的差异基因。使用4种机器学习算法建立模型筛选候选靶点用于预测IPF的风险并进行验证。单样本基因集富集分析(ssGSEA)对候选靶点进行免疫细胞浸润分析。基于候选靶点的表达,训练集被分为3个亚组。通过基因集变异分析评估3组之间代谢通路的富集情况,此外,比较3个亚组之间免疫细胞浸润的差异。[结果]共鉴定出了58个与TDP-43相关的差异基因,4种机器学习算法中,SVM最优算法构建模型,TIMM17A、RCOR1、HTATSF1、SENP5和GNS被鉴定为潜在的诊断生物标志物,内外部验证的ROC曲线下面积分别为0.955和0.888,并建立预测风险的列线图。5个潜在标志物的高表达和低表达之间的免疫细胞浸润程度存在差异。在3个亚组中,代谢通路和免疫浸润情况均具有差异。[结论]TIMM17A、RCOR1、HTATSF1、SENP5、GNS可能作为IPF诊断标志物,其在IPF中与多个免疫细胞浸润显著相关。

[Objective] The relationship between expression of TDP-43 related gene and immune infiltration in idiopathic pulmonary fibrosis(IPF)was analyzed based on bioinformatics.[Method]Obtain the transcriptome data in database from Gene Expression Omnibus(GEO),through the correlation analysis with TDP-43(|correlation coefficient|>0.75)for TDP-43 related genes,and the analysis of the differences between the TDP-genetic differences associated with 43.Four machine learning algorithms were used to establish models to screen candidate targets for predicting the risk of IPF.Single sample gene set enrichment analysis(ssGSEA)was used to analyze the immune cell infiltration of the candidate targets.Based on the expression of candidate targets,the training set was divided into 3 subgroups.Gene set variation analysis was used to evaluate the enrichment of metabolic pathways among the three groups.In addition,the differences in immune cell infiltration among the three subgroups were compared.[Result]A total of 58 differential genes related to TDP-43 were identified.Among the four machine learning algorithms,the SVM optimal algorithm was used to construct the model.TIMM17A,RCORI,HTATSFI,SENP5 and GNS were identified as potential diagnostic biomarkers,and the area under the ROC curve of internal and external validation was 0.955 and 0.888,respectively.In addition,a nomogram was developed to predict the risk.There was a difference in the degree of immune cell infiltration between high and low expression of the five potential markers.Metabolic pathways and immune infiltration were different in the three subgroups.[Conclusion]TIMM17A,RCORI,HTATSFI,SENP5,and GNS may be used as markers for the diagnosis of IPF,and TDP-43 related genes may affect the immune infiltration of IPF.