Bioinformatics analysis identifies a novel 4-gene signature associated with immune cell infiltration in...

Osteoarthritis is the most common degenerative joint condition, yet its pathogenesis remains incompletely understood. This study aims to identify key differentially expressed genes (DEGs) in osteoarthritis-affected synovial tissues and investigate potential underlying immune mechanisms using bioinformatics approaches. Gene expression datasets GSE12021, GSE55235, and GSE55457 from the Gene Expression Omnibus (GEO) were analyzed, comprising 30 osteoarthritis and 28 normal synovial tissue samples. Differentially expressed genes (DEGs) were identified using the limma R package. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were screened via protein-protein interaction (PPI) networks and multiple topological network algorithms. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration was assessed with CIBERSORT. In situ hybridization validated hub gene expression in an independent cohort of 9 osteoarthritis and 8 control synovial tissue samples. A total of 388 DEGs were identified, including 167 upregulated and 221 downregulated genes. KEGG pathway analysis associated these genes with immune-related pathways such as TNF, IL-17, and NF-κB signaling. Network-based topological analysis using ten cytoHubba algorithms identified CXCL2, JUNB, CX3CR1, and CCL19 as crucial diagnostic genes for osteoarthritis, which were further validated using an expanded testing set (GSE1919, GSE55457, GSE206848; 22 OA vs. 22 controls). The combined 4-gene signature demonstrated high diagnostic accuracy (AUC = 0.988) in the training set and an AUC of 0.938 in the independent testing set. In situ hybridization demonstrated elevated CX3CR1 mRNA levels and reduced CXCL2 and JUNB levels in osteoarthritis synovial tissues; the directional change of CCL19 mRNA was consistent with bioinformatic predictions, but did not reach statistical significance in the clinical validation cohort. Immune cell infiltration analysis revealed significant differences in eight immune cell types between patients and healthy individuals. An associative negative correlation was observed between JUNB and resting mast cells (R = - 0.54, p = 0.0021). Furthermore, computational analyses, presented as a hypothesis-generating resource, predicted potential regulatory networks involving small-molecule drugs, competing endogenous RNAs (ceRNAs), and transcription factors (TFs) targeting the hub genes. CXCL2, JUNB, and CX3CR1 represent potential diagnostic biomarkers for osteoarthritis, with CCL19 identified bioinformatically as a contributing component of the multi-gene signature, though its clinical validation requires further study. Memory B cells, resting memory CD4⁺ T cells, plasma cells, activated NK cells, and especially resting mast cells, may be associated with the disease's onset and progression.