Identification of genes associated with osteoarthritis by microarray analysis

Sun, Jianwei; Yan, Bingshan; Yin, Weilun; Zhang, Xinchao

doi:10.3892/mmr.2015.4048

Cited by 18 publications

(11 citation statements)

References 39 publications

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“…FOXM1 is a transcription factor that acts as a regulator of the cell cycle [44], which may involve in such biological process as mitotic cell cycle, Wnt signaling and apoptosis [45]. In addition, the role of overlapped hub genes including KIF11, BUB1 and MAD2L1 in the pathogenesis of OA was also found in the previous studies [46,47]. Accordingly, under mechanical stress, the cell cycle-related genes were downregulated, as a result, chondrocyte cell proliferation would be blocked and transformed to differentiation and hypertrophy, which may be important in the pathogenesis of OA.…”

Section: Discussionmentioning

confidence: 59%

Bioinformatics analysis reveals different gene expression patterns in chondrocytes from knee and finger joints: implications for the effect of mechanical stress in the osteoarthritic development

Mei

Yin

Shi

et al. 2020

Preprint

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Background Mechanical stress has been well known to be a significant risk factor for the onset and progression of osteoarthritis (OA). The objective of this study is to clarify the key genes and the signaling pathways which is associated with the effect of mechanical stress on OA development. Results A total of 213 Differentially expressed genes (DEGs) were identified in cultured chondrocytes from knee and finger joints, including 101 up- and 112 down-regulated genes, which brought an enrichment in positive or negative regulation of cell proliferation, positive regulation of cell division, positive regulation of apoptotic process and embryonic limb morphogenesis. Two comparison cohorts shared 122 overlapping genes including 49 up- and 73 down-regulated genes, which enriched in DNA replication, cell division, calcium ion binding and positive regulation of apoptotic process. Twelve hub overlapped genes including Cyclin dependent kinase 1 (CDK1), Kinesin family member 11 (KIF11), Mitotic checkpoint serine/threonine kinase (BUB1) and Mitotic arrest deficient 2 like 1 (MAD2L1) were defined and predicted miRNAs of the genes mainly enriched in Transforming growth factor beta (TGF-β) signaling pathway, Wnt signaling pathway and Mitogen activated kinase-like protein (MAPK) signaling pathway. Conclusions Mechanical stress shown multiple influences on chondrocytes, physiologically necessary for maintaining chondrocyte homeostasis and promoting cartilage development, pathologically help for cartilage destruction and OA development via boosting chondrocyte differentiation and hypertrophy and accelerating cell apoptosis.

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Section: Discussionmentioning

confidence: 59%

Bioinformatics analysis reveals different gene expression patterns in chondrocytes from knee and finger joints: implications for the effect of mechanical stress in the osteoarthritic development

Mei

Yin

Shi

et al. 2020

Preprint

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“…In addition, another four bioinformatics analyses identified AGT , CXCL12 , KDM2B , VEGFA , JUN , JUNB , PISD , RARRES3 , EIF4G1 , EPHA3 , KIF2C , KIF11 , KIF20A , PTTG1 , MAP2K6 , PPP3CC , and CSNK1E as potentially essential genes associated with the pathogenesis of knee OA. 25 26 27 28 …”

Section: Discussionmentioning

confidence: 99%

Exploring the Key Genes and Pathways of Osteoarthritis in Knee Cartilage in a Rat Model Using Gene Expression Profiling

Ren¹,

Zhao

Yang³

et al. 2018

Yonsei Med J

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PurposeTo compare differentially expressed genes (DEGs) mediating osteoarthritis (OA) in knee cartilage and in normal knee cartilage in a rat model of OA and to identify their impact on molecular pathways associated with OA.Materials and MethodsA gene expression profile was downloaded from the Gene Expression Omnibus database. Analysis of DEGs was carried out using GEO2R. Enrichment analyses were performed on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway using the Search Tool for the Retrieval of Interacting Genes database (http://www.string-db.org/). Subsequently, the regulatory interaction network of OA-associated genes was visualized using Cytoscape software (version 3.4.0; www.cytoscape.org).ResultsIn the gene expression profile GSE103416, a total of 99 DEGs were identified. Among them, 76 DEGs (76.77%) were overexpressed, and the remaining 23 DEGs (23.23%) were underexpressed. GO and pathway enrichment analyses of target genes were performed. Using gene-gene interaction network analysis, relevant core genes, including MET, UBB, GNAI3, and GNA13, were shown to hold a potential relationship with the development of OA in cartilage. Using quantitative real-time PCR, the Gna13/cGMP-PKG signaling pathway was identified as a potential research target for therapy and for further understanding the development of OA.ConclusionThe results of the present study provide a comprehensive understanding of the roles of DEGs in knee cartilage in relation to the development of OA.

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“…Globally, 250 million people are estimated to have OA of the knee, and the incidence is increasing . Moreover, because of medical costs and lost wages, OA has become a tremendous economic burden for families and societies worldwide . Although treatment options are continually improving, current therapies cannot effectively modify OA progression .…”

Section: Introductionmentioning

confidence: 99%

Identification and functional analysis of long non‐coding RNAs in the synovial membrane of osteoarthritis patients

Shui

Xie

Chen

et al. 2020

Cell Biochemistry & Function

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Osteoarthritis (OA), the most common chronic joint disease in the elderly, has become a significant economic burden for families and societies worldwide. Although treatments are continually improving, current drugs only target joint pain, with no effective therapies modifying OA progression. Long noncoding RNAs (lncRNAs), which have received increasing attention in recent years, are abnormally expressed in OA cartilage. In the present study, weighted coexpression network analysis (WGCNA) was applied to identify modules related to certain OA clinical traits. In total, 4404 coding genes and 161 lncRNAs were differentially expressed based on two OA expression profile data sets and normal control samples. Subsequently, 11 independent modules were acquired, and the green module, with a total of 49 hub genes, was identified as the most relevant to OA. These hub genes were validated using the GSE12021 data set. There was only one lncRNA among the hub genes, namely, NONHSAG034351. Thus, we further explored the function of NONHSAG034351‐related genes in the network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that NONHSAG034351‐associated genes are involved in the response to lipopolysaccharide, angiogenesis, tumour necrosis factor (TNF) signalling, and mitogen‐activated protein kinase (MAPK) signalling pathways. In conclusion, we identified modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA, was downregulated in OA synovial tissue and might play a significant role in the pathological progression of this disease. Our findings have important clinical implications and could provide novel biomarkers that indicate the molecular mechanisms of OA and act as potential therapeutic targets. Significance of this study Long noncoding RNAs (lncRNAs) have been reported to be abnormally expressed in osteoarthritis (OA), which is the most common chronic joint disease among the elderly. In the present study, we report the expression profiles of lncRNAs in OA and the identification of modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA identified to be downregulated in the synovial tissue of OA patients, might play a significant role in the pathological progression of OA. Furthermore, our findings provide novel biomarkers associated with the molecular mechanisms underlying OA pathogenesis, thus implying potential therapeutic targets with important clinical implications.

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Identification of genes associated with osteoarthritis by microarray analysis

Cited by 18 publications

References 39 publications

Bioinformatics analysis reveals different gene expression patterns in chondrocytes from knee and finger joints: implications for the effect of mechanical stress in the osteoarthritic development

Bioinformatics analysis reveals different gene expression patterns in chondrocytes from knee and finger joints: implications for the effect of mechanical stress in the osteoarthritic development

Exploring the Key Genes and Pathways of Osteoarthritis in Knee Cartilage in a Rat Model Using Gene Expression Profiling

Identification and functional analysis of long non‐coding RNAs in the synovial membrane of osteoarthritis patients

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