KLF9 and EPYC acting as feature genes for osteoarthritis and their association with immune infiltration

Abstract

Background Osteoarthritis, a common degenerative disease of articular cartilage, is characterized by degeneration of articular cartilage, changes in subchondral bone structure, and formation of osteophytes, with main clinical manifestations including increasingly serious swelling, pain, stiffness, deformity, and mobility deficits of the knee joints. With the advent of the big data era, the processing of mass data has evolved into a hot topic and gained a solid foundation from the steadily developed and improved machine learning algorithms. Aiming to provide a reference for the diagnosis and treatment of osteoarthritis, this paper using machine learning identifies the key feature genes of osteoarthritis and explores its relationship with immune infiltration, thereby revealing its pathogenesis at the molecular level. Methods From the GEO database, GSE55235 and GSE55457 data were derived as training sets and GSE98918 data as a validation set. Differential gene expressions of the training sets were analyzed, and the LASSO regression model and support vector machine model were established by applying machine learning algorithms. Moreover, their intersection genes were regarded as feature genes, the receiver operator characteristic (ROC) curve was drawn, and the results were verified using the validation set. In addition, the expression spectrum of osteoarthritis was analyzed by immunocyte infiltration and the co-expression correlation between feature genes and immunocytes was construed. Conclusion EPYC and KLF9 can be viewed as feature genes for osteoarthritis. The silencing of EPYC and the overexpression of KLF9 are associated with the occurrence of osteoarthritis and immunocyte infiltration.

Integrated analysis reveals potential significance of FKBP5 in the prognosis and immunity of osteoarthritis and pan-cancer

“…Previous studies have identi ed biomarkers of OA using machine-learning methods. For example, Liang et al (39) used SVM-RFE and Lasso algorithms to screen APOLD1 and EPYC as diagnostic genes for OA; Zhang JY et al (40) used similar screening methods to select EPYC and KLF9 as diagnostic genes, which may indicate that differences in logFC values, gene set selection, and parameter selection affect the screening of candidate genes. However, they did not validate the expression of diagnostic genes based on intra-articular cavity samples, while the EPYC and KLF9 genes were identi ed as diagnostic markers for OA, which also indicated the feasibility of our analysis strategy.…”

Kruppel-like Factors in Skeletal Physiology and Pathologies

“…Similar to KLF8, KLF9 has thus far not been reported to be involved in bone development and physiology. KLF9 has been shown to be intimately involved in bone pathology, including osteoarthritis and osteosarcoma [ 91 , 92 , 93 , 94 , 95 ]. This is further discussed in a later section.…”

“…The role of KLF9 in OA is still not well understood, but Zheng et al reported that KLF9 may be related to the suppression of immune cell infiltration [ 95 ].…”

EPYC functions as a novel prognostic biomarker for pancreatic cancer

“…And it can regulate the fibril formation by interacting with collagen fibrils and other extracellular matrix proteins 14 . EPYC has been found to function crucial roles in a variety of human diseases, such as osteoarthritis 15 , 16 , high myopia 17 , 18 , rheumatoid arthritis 19 , and so on. Nonetheless it is not clear enough that what roles EPYC plays in tumors.…”