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Background: Osteoarthritis (OA) is a kind of chronic osteoarthropathy and degenerative joint disease. Epigenetic regulation in the gene expression dynamics has become increasingly important in OA. We performed a combined analysis of two types of microarray datasets (gene expression and DNA methylation) to identify methylation‐based key biomarkers to provide a better understanding of molecular biological mechanisms of OA. Methods: We obtained two expression profiling datasets (GSE55235, GSE55457) and one DNA methylation profiling data set (GSE63695) from the Gene Expression Omnibus. First, differentially expressed genes (DEGs) between patients with OA and controls were identified using the Limma package in R(v3.4.4). Then, function enrichment analysis of DEGs was performed using a DAVID database. For DNA methylation datasets, ChAMP methylation analysis package was used to identify differential methylation genes (DMGs). Finally, a comprehensive analysis of DEGs and DMGs was conducted to identify genes that exhibited differential expression and methylation simultaneously. Results: We identified 112 DEGs and 2,896 DMGs in patients with OA compared with controls. Functional analysis of DEGs obtained that inflammatory responses, immune responses, and positive regulation of apoptosis, tumor necrosis factor (TNF) signaling pathway, and osteoclast differentiation may be involved in the pathogenesis of OA. Cross‐analysis revealed 26 genes that exhibited differential expression and methylation in OA. Among them, ADAMTS9, FKBP5, and PFKBF3 were identified as valuable methylation‐based biomarkers for OA. Conclusion: In summary, our study identified different molecular features between patients with OA and controls. This may provide new clues for clarifying the pathogenetic mechanisms of OA. We performed the combined analysis of two types of microarray datasets (gene expression and DNA methylation) to identify methylation‐based key biomarkers to provide a better understanding of molecular biological mechanisms of osteoarthritis (OA). We identified different molecular features between patients with OA and controls and identified ADAMTS9, FKBP5, and PFKBF3 as valuable methylation‐based biomarkers for OA. [ABSTRACT FROM AUTHOR]

Kashin–Beck disease (KBD) is a complex endemic osteoarthropathy, which mainly occurs in the northeast to southwest China. Iodothyronine deiodinases 3 (DIO3) is one of the selenoproteins, which is closely related to bone metabolism and unclear to KBD. This study aims to investigate the role and associated mechanisms of methylation and expression of DIO3 with disease severity in patients with KBD. We performed a bioinformatics analysis first to identify the biological mechanisms involved in selenoproteins. The methylation status of the DIO3 gene and DIO3 gene expression, as well as DIO3‐related regulatory genes in patients with KBD, were analyzed. We found that 15 CpG sites of six selenoproteins were hypomethylated with 5‐azacytidine treatment. DIO3 hypermethylation was associated with an increased risk of KBD and may lead to downregulation of DIO3 gene expression as well as be an indicator of the severity of KBD, which may provide a new insight for gene–environment correlations and interactions in etiology and pathogenesis of KBD. [ABSTRACT FROM AUTHOR]