The molecular markers of immune cell infiltration in ischemic stroke

Abstract

Abstract Background: Some studies have revealed that immune regulation can delay Ischemic Stroke (IS) progression and improve neurological function and prognosis. Therefore, the molecular markers of immune cell infiltration in stroke deserves further investigation. Methods: The proportion of immune cells in the GSE58294 and GSE16561 datasets were calculated by Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm. Then, Weighted Gene Coexpression Network Analysis (WGCNA) was performed to screen the key module genes related to immune cells. The overlapping differentially expressed genes (DEGs) between IS and healthy control (HC) samples were obtained from the GSE58294 and GSE16561 datasets. Differential immune cell-related DEGs were screened by overlapping DEGs and key module genes of WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to investigate the functions of immune cell-related DEGs. Subsequently, machine learning algorithms were used to identify diagnostic genes. Then, GSE58294, GSE1656 and GSE54992 datasets were used to screen diagnostic genes by the Received Operating Characteristic (ROC) curves. Subsequently, the Pearson correlation between immune cells and diagnostic genes were analyzed. Moreover, Gene Set Enrichment Analysis (GSEA) was used to explore the functions of diagnostic genes, and the Comparative Toxicology Genomics (CTD) database was used to predict potential drugs for diagnostic genes. Finally, the quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) was applied to explore the expression of diagnostic genes. Results: Three common differential immune cells in the GSE58294 and GSE16561 datasets were obtained, and 25 differential immune cell-related DEGs were obtained. Functional enrichment revealed that these genes were mainly associated with immune response activation and immunocytes. Moreover, 3 diagnostic genes (CD79B, ID3 and PLXDC2) with good diagnostic value were obtained. Subsequently, Pearson correlation analysis between immune cells and 3 diagnostic genes showed that the 3 genes were strong correlation with immune cells. Furthermore, GSEA revealed that CD79B, ID3 and PLXDC2 were mainly involved in immune response. Additionally, 20 CD79B-related, 73 ID3-related and 19 PLXDC2-related drugs were predicted. Finally, the mRNA expression of CD79B, ID3 and PLXDC2 were different in IS and HC. Conclusion: CD79B, ID3 and PLXDC2 were identified as biomarkers of IS, which might provide a research basis for further understanding the pathogenesis of IS and contribute to the treatment of IS.