Identification and Validation of Autophagy-Related Genes in Osteoarthritis through Bioinformatics and Machine Learning

Abstract

Osteoarthritis (OA) is a common degenerative joint disease affecting the elderly worldwide. Although increasing evidence suggests a close relationship between autophagy and OA, its pathogenesis remains unclear. This study aimed to identify autophagy-related genes in OA using bioinformatics and machine learning methods. Three OA datasets (GSE55235, GSE55457 and GSE12021) were retrieved from the GEO database for differential analysis. Subsequently, differentially expressed genes (DEGs) were intersected with autophagy-related genes to identify differentially expressed autophagy-related genes (DEARGs), which were then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Subsequently, potential key genes were selected using three machine learning algorithms (LASSO, SVM and RF) and their diagnostic accuracy was validated using an external dataset (GSE114007) to determine the key genes. Next, potential interactions between the key genes were predicted using the GeneMANIA database. Additionally, immune cell infiltration analysis was performed to explore the correlation between the key genes and immune cells. Finally, the expression levels of the key genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). In this study, a total of 27 DEARGs were identified. GO and KEGG enrichment analyses indicated that these DEARGs might be associated with pathways related to cellular immunity, autophagy, and inflammation. Four potential key genes were selected through the use of three machine learning algorithms. Notably, validation with the external dataset revealed that the expression levels of PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA and exhibited strong diagnostic performance. Immune infiltration analysis showed that PPP1R15A, GABARAPL1 and FOXO3 were positively correlated with activated mast cells and resting memory CD4 + T cells, but negatively correlated with plasma cells and M0 macrophages. Finally, qRT-PCR confirmed these results, which were consistent with the bioinformatics analysis.In conclusion, this study identifies PPP1R15A, GABARAPL1 and FOXO3 as autophagy key genes in OA, providing potential targets for the diagnosis and treatment of OA.