Identification of key genes in osteoarthritis development: biomarker discovery and therapeutic targets.

INTRODUCTION

Osteoarthritis (OA) is the most common joint disorder and a leading cause of disability in the older adult. Early diagnosis and treatment are crucial for effective disease management and improved outcomes. This study aims to identify key genes involved in OA progression using bioinformatics, which may serve as diagnostic biomarkers and therapeutic targets.

METHODS

Synovial tissue sequencing data (GSE1919, GSE55235, GSE82107) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analysis. ROC curve analysis was used to assess diagnostic potential, and results were validated using the GSE29746 dataset and synovial tissues from five OA patients and controls.

RESULTS

A total of 33 common DEGs were identified across three datasets. Four hub genes (CXCL8, CXCL2, DUSP5, TNFSF11) showed high diagnostic potential [area under the receiver operating characteristic curve (AUC) > 0.8]. These genes were also linked to potential therapeutic agents, including lipopolysaccharide and acetaminophen.

CONCLUSION

CXCL8, CXCL2, DUSP5, and TNFSF11 represent novel multi-functional biomarkers that advance OA research by addressing two critical limitations of prior biomarker studies: (1) overcoming the diagnostic inadequacy of single-biomarker approaches through synergistic clusters, and (2) revealing an unreported integrative mechanism linking inflammatory pathways (CXCL8/2) and bone remodeling processes (TNFSF11/DUSP5). This dual diagnostic-therapeutic potential significantly expands the clinical applicability of OA biomarkers.