Global transcriptome analysis to identify critical genes involved in the pathology of osteoarthritis

Zhang, X; Bu, Yingjie; Zhu, Baoan; Zhao, Qiang; Lv, Zhi; Li, B; Liu, J

doi:10.1302/2046-3758.74.bjr-2017-0245.r1

Cited by 26 publications

(21 citation statements)

References 44 publications

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“…ADAMTS1 breaks aggrecan and versican and is expressed in cartilage and synovium. Data about its expression in OA are controversial; some authors reported an increased expression in cartilage [139] whereas recent results using qRT-PCR showed that ADAMTS1 is downregulated in joint synovial tissues [140].…”

Section: Aggrecanases and Proteoglycanasesmentioning

confidence: 99%

Profile of Matrix-Remodeling Proteinases in Osteoarthritis: Impact of Fibronectin

Pérez‐García

Carrión

Gutiérrez‐Cañas

et al. 2019

Cells

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The extracellular matrix (ECM) is a complex and specialized three-dimensional macromolecular network, present in nearly all tissues, that also interacts with cell surface receptors on joint resident cells. Changes in the composition and physical properties of the ECM lead to the development of many diseases, including osteoarthritis (OA). OA is a chronic degenerative rheumatic disease characterized by a progressive loss of synovial joint function as a consequence of the degradation of articular cartilage, also associated with alterations in the synovial membrane and subchondral bone. During OA, ECM-degrading enzymes, including urokinase-type plasminogen activator (uPA), matrix metalloproteinases (MMPs), and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTSs), cleave ECM components, such as fibronectin (Fn), generating fibronectin fragments (Fn-fs) with catabolic properties. In turn, Fn-fs promote activation of these proteinases, establishing a degradative and inflammatory feedback loop. Thus, the aim of this review is to update the contribution of ECM-degrading proteinases to the physiopathology of OA as well as their modulation by Fn-fs.

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Section: Aggrecanases and Proteoglycanasesmentioning

confidence: 99%

Profile of Matrix-Remodeling Proteinases in Osteoarthritis: Impact of Fibronectin

Pérez‐García

Carrión

Gutiérrez‐Cañas

et al. 2019

Cells

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“…FK506 binding protein 5, encoded by the FKBP5 gene, belongs to the immunophilin protein family, which plays a key role in immunoregulation and cellular processes by affecting protein folding and transportation (Zhang et al, 2018). Researchers have found that the expression of FKBP5 is downregulated in the cartilage of OA (Thakur, Dawes, & Mcmahon, 2013).…”

Section: Roc Curve Analysismentioning

confidence: 99%

Analysis of gene expression and methylation datasets identified ADAMTS9, FKBP5, and PFKBF3 as biomarkers for osteoarthritis

Zhang

Yang

et al. 2018

Journal Cellular Physiology

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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.

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“…Youden's index was used to show the most diagnostic threshold for each gene. The area under curve (AUC) was used to evaluate the effectiveness of these genes as biomarkers of OA [22,23] .…”

Section: Receiver Operating Characteristic (Roc) Analysismentioning

confidence: 99%

Integrated bioinformatics analysis of cartilage for osteoarthritis

Wang

Zhong

Lin

et al. 2020

Preprint

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Background Osteoarthritis (OA) is a joint disease which threatens the health of older people around the world. However, its pathological mechanism remains unclear. In the current study, we aimed to identify the key genes and underlying pathological mechanisms associated with OA. Methods Firstly, GSE117999 was obtained from the Gene Expression Omnibus (GEO) database. We then obtained the DEGs, hub genes and key genes of the dataset using bioinformatics analysis, after which we analyzed these items using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Furthermore, the target miRNAs of key genes were predicted by miRDB. The Drug Gene Interaction database (DGIdb) was used to predict target drugs for key genes. Receiver operating characteristic (ROC) analysis was employed to evaluate the effectiveness of key genes. Results: A total of 974 DEGs were identified. Furthermore, we found 19 hub genes and 10 key genes. We then predicted that hsa-mir-3613-3p, hsa-mir-548e-5p and hsa-mir-5692a would be closely related to key genes through web tools, and that two drugs, etoposide and everolimus, were highly specific to some key genes. Finally, we analyzed the receiver operating characteristic (ROC) of key genes in the samples and speculated that ESR1 may be a biomarker of OA. Conclusions This study deepens our understanding of OA and may be beneficial for further explorations of the pathological mechanism of the disease.

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Global transcriptome analysis to identify critical genes involved in the pathology of osteoarthritis

Cited by 26 publications

References 44 publications

Profile of Matrix-Remodeling Proteinases in Osteoarthritis: Impact of Fibronectin

Profile of Matrix-Remodeling Proteinases in Osteoarthritis: Impact of Fibronectin

Analysis of gene expression and methylation datasets identified ADAMTS9, FKBP5, and PFKBF3 as biomarkers for osteoarthritis

Integrated bioinformatics analysis of cartilage for osteoarthritis

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