Identification of key biomarkers and immune infiltration in the peripheral blood of osteoarthritis by bioinformatics analysis

Abstract Background Osteoarthritis (OA) is a common chronic arthritis disease characterized by cartilage degeneration, sub-cartilage bone hyperplasia, osteophyte formation and joint space stenosis. Traditionally, OA treatment consists of pain management with joint replacement for end-stage disease. Patients diagnosed with OA have developed irreversible organic changes, so the focus should be shifted to prevention, early diagnosis and intervention of OA.Methods The differentially expressed genes (DEGs) were identified from the gene expression profile GSE48556 from the GEO database, and functional enrichment analyses were performed. Boruta algorithm and the least absolute shrinkage and selection operator (LASSO) analysis were used to select hub genes, then we used the LightGBM to evaluate the accuracy of hub genes prediction for OA. The CIBERSORT algorithm was used to analyze the immune infiltration of peripheral blood mononuclear cells (PBMCs) between OA and normal controls.Results A total of 35 DEGs including 29 downregulated genes and 6 upregulated genes were mainly involved in response to tumor necrosis factor (TNF) and TNF-mediated signaling pathway through GO analysis and were mainly enriched in cAMP signaling pathway, NF-κB signaling pathway through KEGG. The LightGBM prediction of the hub gene model showed that area under curve (AUC) of ROC curve and PR curve reached 0.988 and 0.996 respectively in verification set, indicating that hub genes have a high diagnostic accuracy for OA. Gene ADRB2 was the only up-regulated gene in OA group. H3F3B showed the largest AUC which means a high diagnostic power of OA. From the immune infiltration analysis, the content of CD8+ T cells and resting mast cells showed significant differences between the two groups and hub genes were closely related to immune cells.Conclusion The hub genes in PBMCs between OA and normal controls may potentially serve as biomarkers in both prevention and diagnosis of OA and indicate for the exploration of the mechanism of OA.