Multi-omics analysis and validation of autophagy-related diagnostic biomarker in osteoarthritis.

BACKGROUND: Autophagy plays an essential regulatory role in the development of osteoarthritis (OA), its specific regulatory mechanism remains unclear. This work aims to study the critical genes related to autophagy in OA and explore their potential value. METHODS: Datasets were downloaded from the publicly available GEO database, and differentially expressed autophagy-related genes (ARGs) were obtained. LASSO regression analysis, SVM-RFE algorithm, and RF algorithm were used to screen signature genes. The diagnostic accuracy of autophagy-related signature genes (ARSGs) in OA was determined through receiver operating characteristic (ROC) univariate analysis. The accuracy of the results was validated with an external dataset. Signature genes were analyzed for immune-related functions. Single-gene cluster enrichment analysis (GSEA) and single-gene set variation analysis (GSVA) were employed to demonstrate the potential biological functions of signature genes. Competing endogenous RNA networks, including miRNAs and lncRNAs, and drug regulatory networks were constructed to search for potential therapeutic targets. The expression levels of key ARSGs were verified through qRT-PCR, Western blot, immunohistochemistry (IHC) analyses, and animal model construction. RESULTS: A total of 12 up-regulated genes and 37 down-regulated genes were identified and found to be associated with autophagy regulation and inflammatory pathways. Screening identified three candidate genes, namely, CAPN2, ITGA3, and ERBB2. The GSE48556 dataset, as well as qRT-PCR, Western blot, IHC analyses, and animal model further demonstrated that ERBB2 was a key signature gene associated with autophagy and positively correlated with OA severity. The ROC curve showed that ERBB2 had a high diagnostic value. Immunofunctional correlation analysis revealed that the infiltration of immune cells, such as macrophages, neutrophils, and NK cells, was highly abundant in the group with low ERBB2 expression. GSEA and GSVA also revealed that low ERBB2 expression was associated with the activation of cellular immune states. Meanwhile, 30 potential therapeutic drugs for OA and several lncRNAs and miRNAs that regulate ERBB2 were identified. CONCLUSIONS: ERBB2, an autophagy signature gene that is closely related to autophagy regulation and immune response in OA cartilage, holds promise as a bioindicator for the early diagnosis and targeted therapy of OA.