Background: Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment.
Methods:From the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential signature genes of OA,and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between signature genes and immune cells.
Results: The Limma package and WGCNA identified a total of 72 DEGs related to OA,of which 12 were up-regulated and 60 were down-regulated. Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). The ANN diagnostic model was constructed based on these four feature genes. The AUC of the training set was 0.942, and the AUC of the validation set was 0.850. Immune cell infiltration analysis revealed B cells memory, T cells gamma delta, B cells naive, Plasma cells, T cells CD4 memory resting, and NK cells The abnormal infiltration of activated cells may be related to the progression of OA.
Conclusions: In this study, BTG2, CALML4, DUSP5, and GADD45B were identified as potential characteristic genes for OA, and an ANN diagnostic model with excellent diagnostic performance has been developed. Therefore, the diagnostic model established in this research can serve as a reliable reference for early OA diagnosis and provide a novel perspective on the pathogenesis of OA.