Identification of MEG3 and MAPK3 as potential therapeutic targets for osteoarthritis through multiomics integration and machine learning.

Knee osteoarthritis (KOA) is a prevalent degenerative joint disorder, yet its underlying molecular mechanisms remain puzzling. This study aimed to uncover the genes with a causal relationship to KOA using Mendelian randomization (MR), transcriptomic profiling, and machine learning methods. MR analysis was conducted utilizing expression quantitative trait loci (eQTL) data from the eQTLGen consortium alongside KOA-related GWAS summary statistics to identify candidate genes. Subsequently, differential expression analysis and WGCNA were applied to synovial tissue microarray datasets obtained from the GEO database. The intersecting genes were further refined using three machine learning algorithms: LASSO, random forest, and SVM-RFE. Diagnostic efficacy was assessed via ROC curve analysis and nomogram construction. Validation was ultimately performed using qPCR on clinical synovial tissue samples. Twelve genes with putative causal associations to KOA were identified, with MEG3 and MAPK3 emerging as the most diagnostically robust. Both exhibited high sensitivity and specificity in ROC analysis, and their differential expression was corroborated by qPCR. This study underscores the diagnostic utility of MEG3 and MAPK3 in KOA and offers a promising molecular framework for early disease detection. Nonetheless, validation in larger, independent cohorts and further mechanistic investigations are warranted to substantiate these findings.