RETRACTED: Deciphering the Autophagic Machinery: Unveiling Novel Genes in Osteoarthritis Synovial Tissue through Bioinformatics Analysis

Abstract

Background Autophagy is a crucial cellular homeostatic mechanism that primarily influences osteoarthritis (OA) by modulating inflammation. Despite its recognized role in the broader context of OA, the specific effects of autophagy on OA-associated synovitis are not yet well understood. Objective This study aims to identify and validate potential autophagy-related genes (ARGs) involved in OA synovitis using bioinformatics analysis. Methods The synovial bulk RNA-seq dataset GSE55235 was acquired from the GEO database. Initially, this dataset was analyzed to evaluate autophagy levels between the OA synovial group and the healthy synovial group. Subsequently, differentially expressed genes (DEGs) and differentially expressed ARGs (DEARGs) were identified through differential analysis. Functional enrichment analysis was performed to investigate the possible biological functions and pathways of DEARGs. Additionally, the protein-protein interaction (PPI) network of DEARGs was constructed, and the hub genes were identified. Lastly, the Network Analyst platform was used to predict target miRNAs for the hub genes. Results Autophagy levels were significantly lower in the OA synovial group compared to the healthy synovial group. Thirteen DEARGs were identified, mainly enriched in mitophagy, the FoxO signaling pathway, and the PI3K-Akt signaling pathway. MYC, CDKN1A, and FOXO3 were recognized as key ARGs in OA synovitis. The accuracy of these genes was confirmed by analyzing two additional synovial bulk RNA-seq datasets. Moreover, three miRNAs targeting these hub genes were identified using the NetworkAnalyst platform. Conclusion This study identified and validated three hub ARGs in OA synovitis through bioinformatics analysis. These findings offer valuable insights for future research.