Application of machine learning-based exosome-related gene profiles in precision diagnosis and treatment of osteoarthritis.

Osteoarthritis (OA) involves a complex pathogenesis encompassing inflammation, metabolic dysregulation, and aberrant intercellular communication. Despite their crucial role as mediators of intercellular signaling, exosomes remain largely underexplored in OA. This study aims to investigate exosome-related genes (ERGs) and their roles in OA pathogenesis. Four OA-related gene expression datasets retrieved from GEO were harmonized using the ComBat algorithm to mitigate batch effects. Differentially expressed genes (DEGs) were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). ERGs were screened utilizing the ExoCarta and Vesiclepedia databases. Core ERGs were prioritized using LASSO, random forest, and XGBoost algorithms. Predictive models were constructed and subsequently evaluated using SHAP analysis to ascertain feature importance. Additionally, pathway enrichment, immune infiltration, and molecular subtype identification were performed, followed by validation of core ERG expression via RT-qPCR in clinical samples. Integration of the four GEO datasets yielded 231 DEGs significantly enriched within OA-associated pathways (e.g., inflammation, immune cell migration, extracellular matrix remodeling). From a pool of 79 candidate ERGs, 10 core ERGs (EPB41L2, ISLR, HLA-DRB1, HLA-DRA, PGLYRP1, PTEN, TKT, CTNNB1, THSD4, ATP9A) were identified. Random forest models achieved impressive AUCs of 0.991, 1.0, and 0.935 in the training, validation, and external validation sets, respectively, demonstrating substantial clinical net benefit. SHAP analysis underscored CTNNB1 and PGLYRP1 as pivotal predictors. Core ERGs were intricately linked to immune regulation (e.g., M1 macrophage infiltration) and metabolic perturbations (e.g., fatty acid metabolism). Distinct molecular subtypes of OA were delineated based on ERG profiles, thereby revealing the inherent disease heterogeneity. RT-qPCR further corroborated the differential expression of core ERGs in clinical samples. This study comprehensively integrates exosome-related genomic data with advanced machine learning techniques to identify and validate 10 core ERGs associated with OA, thereby elucidating their pivotal roles in immunometabolic regulation. These seminal findings illuminate the intricate molecular heterogeneity of OA, concurrently offering promising novel biomarkers and therapeutic targets for early diagnosis and precision treatment.