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Background. Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells. Methods. We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves. Results. We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta Tcells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set. Conclusion. Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS. [ABSTRACT FROM AUTHOR]

Ischemic stroke (IS) is a high-incidence disease that seriously threatens human life and health. Neuroinflammation and immune responses are key players in the pathophysiological processes of IS. However, the underlying immune mechanisms are not fully understood. In this study, we attempted to identify several immune biomarkers associated with IS. We first retrospectively collected validated human IS immune-related genes (IS-IRGs) as seed genes. Afterward, potential IS-IRGs were discovered by applying random walk with restart on the PPI network and the permutation test as a screening strategy. Doing so, the validated and potential sets of IS-IRGs were merged together as an IS-IRG catalog. Two microarray profiles were subsequently used to explore the expression patterns of the IS-IRG catalog, and only IS-IRGs that were differentially expressed between IS patients and controls in both profiles were retained for biomarker selection by the Random Forest rankings. CLEC4D and CD163 were finally identified as immune biomarkers of IS, and a classification model was constructed and verified based on the weights of two biomarkers obtained from the Neural Network algorithm. Furthermore, the CIBERSORT algorithm helped us determine the proportions of circulating immune cells. Correlation analyses between IS immune biomarkers and immune cell proportions demonstrated that CLEC4D was strongly correlated with the proportion of neutrophils (r = 0.72). These results may provide potential targets for further studies on immuno-neuroprotection therapies against reperfusion injury. [ABSTRACT FROM AUTHOR]

Background: Myasthenia gravis (MG) is an antibody-mediated autoimmune disease. In recent years, accumulating evidence has indicated that long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs), contributing to the progression of various autoimmune diseases. Nevertheless, the regulatory roles of ceRNAs in MG pathogenesis remain unclear. In this study, we aimed to elucidate the role of lncRNA OIP5-AS1 as a ceRNA associated with MG progression. Methods: Real-time PCR was used to detect OIP5-AS1 levels in peripheral blood mononuclear cells (PBMCs) from patients with MG. Luciferase reporter assays were performed to validate the relationship between OIP5-AS1 and miR-181c-5p. CCK-8 and flow cytometry were performed to test the proliferation and apoptotic abilities of OIP5-AS1 in Jurkat cells. Furthermore, real-time PCR and Western blot assays were performed to explore the interactions between OIP5-AS1, miR-181c-5p, and IL-7. Results: The expression of OIP5-AS1 was up-regulated in patients with MG. Luciferase reporter assay indicated that OIP5-AS1 targeted the miR-181c-5p. Functional assays showed that OIP5-AS1 suppressed Jurkat cell apoptosis and promoted cell proliferation by sponging miR-181c-5p. Mechanistically, knockdown of OIP5-AS1 inhibited IL-7 expression at both the mRNA and protein levels in Jurkat cells, whereas the miR-181c-5p inhibitor blocked the reduction of IL-7 expression induced by OIP5-AS1 suppression. Conclusions: We confirmed that OIP5-AS1 serves as an endogenous sponge for miR-181c-5p to regulate the expression of IL-7. Our findings provide novel insights into MG processes and suggests potential therapeutic targets for patients with MG. [ABSTRACT FROM AUTHOR]