Identification and validation of a glutamine metabolism-related gene signature as a diagnostic and therapeutic target in osteoarthritis through integrated multi-omics analysis.

Background: The understanding of the pathogenesis of osteoarthritis (OA) is often fragmented, with studies focusing on individual tissues. A holistic view integrating multi-tissue molecular changes with systemic metabolic shifts is urgently needed. Glutamine metabolism, a central bioenergetic and biosynthetic hub, represents a critical but largely unexplored nexus in this disease network. This study leverages a multi-omics, multi-tissue approach to deconstruct the role of glutamine metabolism in OA and identify a robust, blood-based signature for potential diagnostic use. Methods: We conducted a comprehensive bioinformatic investigation by integrating multiple GEO transcriptomic datasets from cartilage, synovium, subchondral bone, and peripheral blood. A machine learning pipeline, incorporating weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression, was employed to identify a signature of glutamine metabolism-related genes (GMRGs). The signature's clinical relevance was then validated in an independent cohort of 62 subjects (31 OA patients Results: Our multi-layered analysis identified a core three-gene signature ( Conclusions: This study identifies a blood-based, multi-omics-derived gene signature that links localized joint pathology with systemic metabolic dysfunction in osteoarthritis. The signature offers a robust non-invasive diagnostic marker and reveals new opportunities for patient stratification and therapeutic development.