Abstract
Osteoarthritis (OA) is a common degenerative disease of joints, which can appear in almost any joint of the body. Therefore, the widespread occurrence of this disease has a huge impact on the lives of patients around the world. As an important part of metabolism, lipid metabolism is closely related to the occurrence and development of osteoarthritis. We screened UGCG and KLF4 based on weighted co-expression network analysis (WGCNA) and SVM-REF analysis. The data from Gene Expression Omnibus (GEO) and single-cell data verified the expression of these two genes. We analyzed KLF4-related genes and established a diagnosis model of OA related to lipid metabolism through the least absolute shrinkage and selection operator (LASSO) analysis. RT-PCR was used to verify the expression of KLF4 in osteoarthritis. Ten important lipid metabolism related genes (LMRGs) in OA were obtained. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that they are involve in the formation of immune microenvironment in osteoarthritis. CIBERSORT analysis revealed that there were significant differences in the immune microenvironment between osteoarthritis patients and normal controls. RT-PCR results showed that the expression of KLF4 in OA samples was lower than that in normal samples. The diagnostic model can be used to diagnose OA patients well. Overall, we demonstrated the potential relationship between the abnormal lipid metabolism and the pathological process of OA. Finally, we identified KLF4 as our significant LMRG and constructed a KLF4-related scoring model to accurately diagnose OA. In conclusion, therapy strategies targeting on regulating lipid metabolism may become a key factor in treating OA. Key Points (a) We identified the significant LMRG KLF4 and constructed a novel KLF4-related scoring model for the accuracy diagnosis of OA. (b) The potential relationship between lipid metabolism and the immune microenvironment in OA was demonstrated in our research. (c) The relationship of lipid metabolism and OA has been further improved in our research and provided novel insight for the diagnosis and therapy for OA patients.
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