Integrated multi-omics analysis and experimental validation identify hub genes with diagnostic value in meningitis.

Background: Meningitis remains a serious threat to human health; however, the molecular mechanisms underlying this condition, particularly those related to macrophage involvement, have not been fully elucidated. The aim of this study was to identify macrophage-associated hub genes in meningitis and to assess their diagnostic value and associations with immune cell infiltration. Methods: Multiple datasets from the Gene Expression Omnibus were analyzed to identify differentially expressed genes (DEGs). Immune cell infiltration was estimated using the CIBERSORT algorithm (Cell-type Identification by Estimating Relative Subsets of RNA Transcripts). Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to identify key modules associated with classically activated (M1) macrophages. Hub genes were subsequently screened through integration of the protein-protein interaction (PPI) network with machine learning algorithms, including least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest algorithms. Diagnostic performance was assessed using receiver operating characteristic curves and construction of a nomogram. Experimental validation was subsequently performed using reverse transcription quantitative polymerase chain reaction, western blotting, and flow cytometry in peripheral blood samples obtained from individuals with meningitis. Results: A total of 292 DEGs were identified, including 195 upregulated genes and 97 downregulated genes. Immune infiltration analysis demonstrated significantly increased proportions of M1 macrophages and activated dendritic cells in samples obtained from the meningitis group. Integrated analysis combining WGCNA, the PPI network, and machine learning methods identified Conclusion: