Journal of Inflammation Research · 2023 · 12 citations · 34 references
Life SciencesBioinformaticsMolecular BiologyGene Expression AnalysisInflammationMolecular DiagnosticsExpression Signature DiscoveryGene Set Enrichment AnalysisHost-pathogen RecognitionOsteomyelitisBiological CharacterizationDiagnostic Model AnalysisDiagnostic Model ValidationEarly DiagnosisEnrichment AnalysisImmune Infiltration BiomarkersImmune Infiltration Prognostic SignaturesSupport Vector Machine Classification
Objective: (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics. Methods: Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes. Results: A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated. Conclusion: We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM.
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