Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

Background Osteoarthritis (OA) is characterized by synovial inflammation, articular cartilage degradation, and subchondral bone changes. Currently, there are no reliable biomarkers for the diagnosis and treatment of OA. Therefore, exploring OA biomarkers is crucial for its prevention, diagnosis, and treatment. Materials and Methods The GSE51588, GSE12021, GSE55457, GSE56409, GSE114007, GSE168505, GSE169077, GSE55235, GSE129147, and GSE48556 datasets of patients with OA and normal control samples were obtained from the GEO database. Differentially expressed genes (DEGs) in OA and normal controls were identified using R language. Protein-protein interaction (PPI) network and module analysis were performed to screen and filter key genes. Enrichment analyses were conducted to determine the biological functions and pathways of key DEGs and predict potential transcription factors. Machine learning models (XGBoost, LASSO regression, and SVM) were used to identify the best characteristic genes, and the intersection of hub genes was used as the final diagnostic genes. ROC analysis and nomogram were used to evaluate the diagnostic value of candidate genes. The expression levels of characteristic genes were validated in external GEO datasets containing cartilage, synovial membrane, and blood samples from patients. The expression levels of the key gene IRS2 in chondrocytes were further confirmed through in vitro experiments. Results Fifteen OA characteristic genes (IRS2, ADM, SIK1, PTN, CX3CR1, WNT5A, IL21R, APOD, CRLF1, FKBP5, PNMAL1, NPR3, RARRES1, ASPN, POSTN) were identified using three machine learning algorithms. Enrichment analysis indicated that abnormal expression of DEGs and hub genes may be mediated by extracellular matrix organization, extracellular structure organization, Relaxin signaling pathway, IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and PI3K-Akt signaling pathway, which are involved in OA occurrence. Four diagnostic genes (IRS2, WNT5A, PTN, POSTN) were highly correlated with OA. Validation data set analysis showed that IRS2 was down-regulated, while WNT5A, PTN, and POSTN were up-regulated in the experimental group compared to the normal group. qRT-PCR and WB results verified that the expression level of diagnostic gene IRS2 was consistent with bioinformatics analysis results. Conclusion This study integrates bioinformatics analysis and machine learning algorithms to identify and validate four promising biomarkers: IRS2, WNT5A, PTN, and POSTN. POSTN can be used as a biomarker for OA cartilage, and early diagnosis of PTN in OA deserves attention. WNT5A and IRS2 offer new diagnostic perspectives for OA.