BACKGROUND:Increasing evidence suggests that N6-methyladenosine(m6A)regulators are closely associated with osteoarthritis and are considered to be a new direction in the prevention and treatment of osteoarthritis,but their specific mechanism of action is unknown. OBJECTIVE:To conduct a bioinformatics analysis of the osteoarthritis gene microarray dataset in order to explore the role of m6A in osteoarthritis and analyze the pathogenesis of osteoarthritis. METHODS:The m6A regulators associated with osteoarthritis and their expression were first extracted from the GSE1919 dataset in the GEO database using R software,and then the results were analyzed by gene difference analysis and GO and KEGG enrichment analyses.Subsequently,the results of protein-protein interaction network topology analysis and machine learning results were intersected to obtain the m6A Hub regulators,which were validated by in vitro cellular experiments. RESULTS AND CONCLUSION:A total of 16 osteoarthritis-related m6A regulators were extracted and 11 m6A differential regulators,including ZC3H13,YTHDC1,YTHDF3 and HNRNPC,were obtained by differential analysis.GO enrichment analysis showed that osteoarthritis-related m6A differential regulators played a role in the biological processes such as mRNA transport,RNA catabolism,and regulation of insulin-like growth factor receptor signaling pathway.(3)KEGG enrichment analysis showed that the differential regulators were mainly involved in the p53,interleukin-17 and AMPK signaling pathways.The combined protein-protein interaction network topology analysis and machine learning results obtained the m6A Hub regulator-YTHDC1.(5)The results of in vitro cellular experiments showed that there was a significant difference in the expression of m6A key regulator between the control and experimental groups(P<0.05).To conclude,YTHDC1 is closely related to the development of osteoarthritis,which is expected to be a molecular target of m6A for the treatment of osteoarthritis.

BACKGROUND:Cellular senescence is closely related to the development and progression of osteoarthritis,but the specific targets and regulatory mechanisms are not yet clear. OBJECTIVE:To mine key genes in cellular senescence-mediated osteoarthritis by integrating bioinformatics and machine learning approaches and validate them via experiments to explore the role of cellular senescence in osteoarthritis. METHODS:The osteoarthritis gene expression profiles obtained from the GEO database were intersected with cellular senescence-related genes obtained from the CellAge database and the expression of the intersected genes was extracted for differential analysis,followed by GO and KEGG analysis of the differential genes.The key osteoarthritis cellular senescence genes were then screened by protein-protein interaction network analysis and machine learning,and in vitro cellular experiments were performed.Finally,the expression of the key genes was detected by qPCR. RESULTS AND CONCLUSION:A total of 31 osteoarthritis cell senescence differential genes were identified.GO analysis showed that these genes were mainly involved in the biological processes,such as regulation of leukocyte differentiation,monocyte differentiation,regulation of T cell differentiation and exerted roles in DNA transcription factor binding,histone deacetylase binding,chromatin DNA binding,and chemokine binding.KEGG analysis showed that osteoarthritis cell senescence differential genes were mainly activated in the JAK/STAT signaling pathway,PI3K/Akt signaling pathway and FoxO signaling pathway.MYC,a key gene for osteoarthritis cellular senescence,was identified by protein-protein interaction network topology analysis and machine learning methods.The results of the in vitro cellular assay showed that the mRNA expression of MYC was significantly lower in the experimental group(osteoarthritis group)than the control group(normal group)(P＜0.05).To conclude,MYC can be a key gene in the senescence of osteoarthritic cells and may be a new target in the prevention and treatment of osteoarthritis by mediating immune response,inflammatory response and transcriptional regulation.