Comprehensive Bioinformatics Analysis of Angiogenesis in Knee Osteoarthritis: Identification of Diagnostic Biomarkers and Therapeutic Targets

Background Osteoarthritis (OA) is a prevalent disabling disease characterized by cartilage degeneration, subchondral sclerosis, and synovial abnormalities, significantly impairing quality of life. Its pathogenesis involves genetic and immune-inflammatory mechanisms. Angiogenesis and immune infiltration play critical roles in knee osteoarthritis (KOA), though their precise interactions remain unclear. This study aims to compare gene and immune expression profiles between normal and KOA cartilage, identify angiogenesis-related core genes, investigate their association with the immune microenvironment, and construct a diagnostic model to provide novel biomarkers and therapeutic targets. Methods Gene expression data were obtained from the GEO database and batch effects removed. Differentially expressed genes (DEGs) were identified and functionally enriched via GO, KEGG, and GSEA. DEGs were intersected with angiogenesis-related genes (ARGs) to identify differentially expressed ARGs (DEARGs). Core genes were selected using logistic regression, random forest, and LASSO algorithms. A nomogram model was constructed to assess KOA risk, followed by immune infiltration analysis and drug docking prediction. Results A total of 2621 DEGs were identified, enriched in angiogenesis pathways. Thirteen DEARGs were obtained, with POSTN and VEGFA identified as core genes. The diagnostic model demonstrated strong predictive power. Core genes were significantly associated with immune cells, and potential drug candidates were identified. Conclusion POSTN and VEGFA may serve as novel diagnostic and therapeutic targets for KOA. The proposed model offers a promising tool for clinical risk assessment and personalized medicine in KOA.