Bioinformatics-driven identification and validation of diagnostic biomarkers for cerebral ischemia reperfusion injury

• DOI: 10.1016/j.heliyon.2024.e28565
• Publication Date: 2024-03-31T21:50:53Z
• Authors: Yuan Yang; Yushan Duan; Huan Jiang; Junjie Li; Wenya Bai; Qi Zhang; Junming Li; Jianlin Shao
• Keywords: Identification

This article aims to identify genetic features associated with immune cell infiltration in cerebral ischemia-reperfusion injury (CIRI) development through bioinformatics, the goal of discovering diagnostic biomarkers and potential therapeutic targets. We obtained two datasets from Gene Expression Omnibus (GEO) database immune-related differentially expressed genes (IRDEGs). These genes' functions were analyzed via Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG). Tools such as CIBERSORT ssGSEA assessed infiltration. The Starbase miRDB databases predicted miRNAs interacting hub genes, Cytoscape software mapped mRNA-miRNA interaction networks. ENCORI was employed predict RNA binding proteins genes. Key identified using a random forest algorithm constructing Support Vector Machine (SVM) model. LASSO regression analysis constructed model for determine their value, PCR validated expression ischemia-reperfusion. 10 IRDEGs (C1qa, Ccl4, Cd74, Cd8a, Cxcl10, Gmfg, Grp, Lgals3bp, Timp1, Vim). algorithm, SVM intersection revealed three key (Ccl4, C1qa) CIRI. analysis, further refined this (Ccl4 C1qa), With ROC curve, confirming efficacy (C1qa AUC = 0.75, Ccl4 0.939). corroborated these findings. Our study elucidates metabolic response mechanisms CIRI, identifying targets injury.