Exploration of the Key Pathways and Genes Involved in Osteoarthritis Genesis: Evidence from Multiple Platforms and Real-World Validation.

Background: Osteoarthritis (OA), a degenerative and chronic joint disease, is essential for identifying novel biomarkers for the clinical diagnosis of OA. Methods: We collected 35 OA patients and 32 healthy controls from four clinical cohorts and 8 real-world samples from our institute. The activation status of 7530 signalling pathways was calculated via the gene set enrichment analysis (GSEA) algorithm. Ten machine learning algorithms and 101 algorithm combinations were further applied to recognize the most diagnostic genes. KDELR3 was chosen for further validation via immunohistochemical staining to determine its diagnostic value in real-world samples. Results: Sixteen pathways, namely, the cellular respiration chain, protein transport, lysosomal and endocytosis pathways, were activated in OA patients. A total of 101 types of algorithm combinations were considered for the diagnostic model, and 58 were successfully output. The two-step model of glmBoost plus RF had the highest average AUC value of 0.95 and was composed of LY86, SORL1, KDELR3, CSK, PTGS1, and PTGS2. Preferable consistency of the diagnostic mole and real conditions was observed in all four cohorts (GSE55235: Kappa=1.000, Conclusion: We constructed and validated a six-gene diagnostic model for OA patients via machine learning, and KDELR3 emerged as a novel biomarker for OA.