Abstract
BackgroundThe elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance.MethodsGene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app.ResultsIn total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA.ConclusionAfter comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA.
Highlights
Osteoarthritis (OA), a common degenerative joint disease, is associated with biochemical, metabolic, and morphological changes of the tissues, mainly in articular cartilage (Liao et al, 2015)
Chondrocytes are responsible for maintaining a balance between anabolic and catabolic factors while the extracellular matrix (ECM) of the cartilage goes through continuous remodeling to maintain homeostasis (Ni et al, 2015)
Subchondral bone obtained from 40 OA patients and 10 healthy donors were examined using the GeneChip expression profiling dataset GSE51588, and cartilage tissues obtained from 20 OA patients and 18 healthy donors were examined using the high-throughput sequencing mRNA expression profile dataset GSE114007
Summary
Osteoarthritis (OA), a common degenerative joint disease, is associated with biochemical, metabolic, and morphological changes of the tissues, mainly in articular cartilage (Liao et al, 2015). Increasing evidence shows that 5hmC is an intermediate for DNA demethylation and exists as an independent, stable epigenetic marker to influence gene expression (Bachman et al, 2014; Karlsson et al, 2010; Wu et al, 2011). Comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. We found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA
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