KLF9 and EPYC function as characteristic genes of osteoarthritis and are associated with immune infiltration

Background: Osteoarthritis is a common degenerative disease of articular cartilage. Its typical features include articular cartilage degeneration, subchondral bone structural changes and osteophyte formation. The main clinical manifestations are increasingly severe knee joint swelling, stiffness, deformity and limited mobility. With the advent of the era of big data, the processing of massive data has become a hot topic, and the continuous development and improvement of machine learning algorithms have laid the foundation for the era of big data. This paper uses machine learning methods to identify the real key characteristic genes of osteoarthritis and explore the relationship between them and immune infiltration, so as to reveal the pathogenic mechanism of osteoarthritis at the molecular biology level, aiming to provide osteoarthritis diagnosis and treatment. Method: Download the GSE55235 and GSE55457 datasets from the GEO database, merge the two as the training set, and download the GSE98918 data as the validation set. Gene differential expression analysis was performed on the training set, lasso regression model and support vector machine model were constructed by machine learning algorithm, and the intersection genes were taken as feature genes and receiver operating characteristic curves were drawn. Finally, the expression profile of osteoarthritis was analyzed by immune cell infiltration and the correlation between the co-expression of characteristic genes and immune cells was analyzed. Conclusion: EPYC and KLF9 can be used as the characteristic genes of osteoarthritis.The silencing of EPYC and the overexpression of KLF9 are related to the occurrence of osteoarthritis and the infiltration of immune cells.