Screening of immune diagnostic biomarkers for depression based on bioinformatics methods
Objective:To screen and analyze potential immune cell infiltration biomarkers related to depres-sion through bioinformatics methods,and further explore their biological roles in the occurrence and development of depression.Methods:The depression-related datasets GSE-98793 and GSE-52790 were selected from the GEO database.Firstly,the obtained data underwent background correction,normalization and dimensionality re-duction,and then the differentially expressed genes related to depression within the dataset were analyzed.Subse-quently,immune-related analysis was performed on the differentially expressed genes using SVM-RFE,LASSO and RF algorithms to construct an immune-related depression diagnostic prediction model.GO function and KEGG enrichment analyses were performed.The CIBERSORT algorithm for immune infiltration was applied to further screen the DEGs,resulting in a total of 22 differentially expressed genes.STRING and Cytoscape software were used to construct a PPI molecular network and subjected to internal validation.Results:The PPI network was con-structed using Cytoscape software,and 11 immune-related differentially expressed genes were screened out,inclu-ding TLR2,CD1C,S100A12,MARCO,CD48,XCL1,LTB,RETN,PTX3,IFNK and ERAP2.Among them,TLR2,S100A12,XCL1 and LTB genes are involved in the regulation of immune inflammatory responses and the infiltration of immune cells,suggesting their potential as biomarkers for immune cell infiltration.Conclusion:The genes identified in this study,which are related to depression and involved in immune cell infiltration,have the po-tential to serve as biomarkers for predicting the onset of depression,and they offer possibilities for the development of targets for immune-targeted drug therapies.
万方数据
维普
