Bioinformatics-based Analysis to Screen Key Genes for Ischemia and Hypoxia after Spinal Cord Injury and Analysis of Immune Infiltration Patterns
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维普
万方数据
目的 通过生物信息学方法筛选脊髓损伤(spina cord injury,SCI)后缺血缺氧相关基因(ischemic and hypoxia related genes,IAHRGs),并分析其免疫浸润模式.方法 从Gene Expression Omnibus(GEO)下载脊髓损伤相关GSE5296,GSE47681和GSE217797的基因表达谱,其中GSE5296,GSE47681样本作为测试集,GSE217797样本作为验证集,获取脊髓损伤与健康样本之间的差异表达基因(differentia1y expressed genes DEGs).在GeneCards数据库和MSigDB数据库筛选(IAHRGs).DEGs和IAHRGs两部分取交集得到缺血缺氧相关的差异表达基因(ischemic and hypoxia related differentially expressed genes,IAHRDEGs).基于 IAHRDEGs 通过 LASSO 模型和 SVM 分析共同筛选得到的关键基因.将关键基因进行Logistics回归分析并构建诊断模型.通过Nomogram分析诊断模型的诊断能力并绘制Logistic预测值的列线图.使用受试者工作特征(ROC)曲线评估诊断模型和关键基因对脊髓损伤的诊断价值.利用CIBERSORT工具分析疾病的免疫细胞浸润模式.结果 共筛选IAHRGs 388个,脊髓损伤与健康样本间差异表达基因313个,其中表达上调312个,下调1个.取两者交集得到27个上调的IAHRDEGs.基于IAHRDEGs经LASSO模型及SVM分析共筛选5个脊髓损伤后缺血缺氧相关关键基因(Abca1,Casp1,Lpl,Procr,Tnfrsf1a).Nomogram分析明确Logistics诊断模型效果良好.ROC曲线分析显示Casp1,Lp1,Tnfrsf1a的诊断效果较高(AUC＞0.9),Abca1,Procr 诊断效果次之(AUC:0.7～0.9),而 Logistics Linear Predictors 的诊断效果最佳(AUC=0.964).CIBERSORT分析显示5个关键基因与8种免疫细胞(中性粒细胞、B淋巴细胞、浆细胞、M0巨噬细胞、CD4T细胞、CD4滤泡细胞、Th17细胞、静止NK细胞)浸润相关.结论 Abca1,Casp1,Lpl,Procr和Tnfrsf1a 5个关键基因可能与脊髓损伤后缺血缺氧发病密切相关,可以作为脊髓损伤后诊断、治疗的候选分子标志物.
Objective To screen ischemia and hypoxiarelated genes(IAHRGs)after spinal cord injury(SCI)and analyze their immune infiltration patterns by bioinformatics methods.Methods The expression profiles of SCI-related GSE5296,GSE47681 and GSE217797 were downloaded from the Gene Expression Omnibus(GEO)database,where GSE5296,GSE47681 samples were used as the test set and GSE217797 samples as the validation set,and the differentially expressed genes(DEGs)between SCI and healthy samples were obtained.IAHRGs were screened in GeneCards and MSigDB databases.The intersection of DEGs and IAHRGs yielded ischemic and hypoxia related differentially expressed genes(IAHRDEGs).Based on the IAHRDEGs,the key genes were jointly screened by LASSO model and SVM analysis.The key genes were subjected to logistic regression analysis and a diagnostic model was constructed.The diagnostic ability of the diagnostic model was analyzed by Nomogram and the column line graph of Logistic predictive values was plotted.The diagnostic value of the diagnostic model and key genes for SCI was evaluated using the receiver operating characteristics(ROC).Immune cell infiltration patterns of the disease were analyzed using the CIBERSORT tool.Results A total of 388 IAHRGs were screened,313 differentially expressed genes were detected between SCI and healthy samples,among which 312 were up-regulated and 1 was down-regulated.A sum of 27 up-regulated IAHRDEGs genes were obtained.Five key genes related to ischemia and hypoxia after SCI(Abca1,Caspl,Lpl,Procr,Tnfrsf1a)were screened by LASSO model and SVM analysis based on IAHRDEGs.Nomogram analysis confirms the effect of logistics diagnosis model.ROC curve analysis showed that Casp1,Lpl and Tnfrsf1a had higher diagnostic efficacy(AUC＞0.9),followed by Abca1 and Procr(AUC:0.7～0.9),and the logistics linear predictors had the best diagnostic effect(AUC=0.964).CIBERSORT analysis showed that five key genes were associated with the infiltration of eight types of immune cells(neutrophil cells,B cells naive,plasma cells,M0 macrophage,T cells CD4 naive,T cells CD4 follicular,Th17 cells,and NK resting).Conclusion The five key genes of Abcal,Caspl,Lpl,Procr,and Tnfrsfla,may be closely related to ischemic-hypoxic pathogenesis after SCI,and can be used as candidate molecular markers for the diagnosis and treatment after SCI.
spinal cord injurykey gene for ischemia and hypoxiaimmune infiltrationcomprehensive bioinformatics
维普
万方数据
