Identification of immune-related key genes in the peripheral blood of ischaemic stroke patients using a weighted gene coexpression network analysis and machine learning

Abstract

Abstract Background The immune system plays a vital role in the pathological process of ischaemic stroke. However, the exact immune-related mechanism remains unclear. The current research aimed to identify immune-related key genes associated with ischaemic stroke. Methods CIBERSORT was utilized to reveal the immune cell infiltration pattern in ischaemic stroke patients. Meanwhile, a weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules significantly correlated with ischaemic stroke. The characteristic genes correlated with ischaemic stroke were identified by the following two machine learning methods: the support vector machine-recursive feature elimination (SVM-RFE) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression. Results The CIBERSORT results suggested that there was a decreased infiltration of naive CD4 T cells, CD8 T cells, resting mast cells and eosinophils and an increased infiltration of neutrophils, M0 macrophages and activated memory CD4 T cells in ischaemic stroke patients. Then, three significant modules (pink, brown and cyan) were identified to be significantly associated with ischaemic stroke. The gene enrichment analysis indicated that 519 genes in the above three modules were mainly involved in several inflammatory or immune-related signalling pathways and biological processes. Eight hub genes (ADM, ANXA3, CARD6, CPQ, SLC22A4, UBE2S, VIM and ZFP36) were revealed to be significantly correlated with ischaemic stroke by the LASSO logistic regression and SVM-RFE algorithm. The external validation combined with a RT‒qPCR analysis revealed that the expression levels of ADM, ANXA3, SLC22A4 and VIM were significantly increased in ischaemic stroke patients and that these key genes were positively associated with neutrophils and M0 macrophages and negatively correlated with CD8 T cells. The mean AUC value of ADM, ANXA3, SLC22A4 and VIM was 0.80, 0.87, 0.91 and 0.88 in the training set, 0.85, 0.77, 0.86 and 0.72 in the testing set and 0.87, 0.83, 0.88 and 0.91 in the validation samples, respectively. Conclusions These results suggest that the ADM, ANXA3, SLC22A4 and VIM genes are reliable serum markers for the diagnosis of ischaemic stroke and that immune cell infiltration plays a crucial role in the occurrence and development of ischaemic stroke.

Keywords

• Weighted gene coexpression network analysis
• Ischaemic stroke
• Immune cell subtype distribution pattern
• Significant modules
• Hub genes