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  • Title: Potential candidate biomarkers associated with osteoarthritis: Evidence from a comprehensive network and pathway analysis.
    Author: Zhang R, Guo H, Yang X, Li Z, Zhang D, Li B, Zhang D, Li Q, Xiong Y.
    Journal: J Cell Physiol; 2019 Aug; 234(10):17433-17443. PubMed ID: 30820958.
    Abstract:
    Osteoarthritis (OA) is one of the most common forms of arthritis world widely. Some key genes and diagnostic markers have been reported due to the development of modern molecular biology technologies. However, the etiology and pathogenesis of OA remains unknown. In this study, an integrated network and pathway analysis towards the biological function of OA-associated genes was conducted to provide valuable information to further explore the etiology and pathogenesis of OA. A total of 2,548 genes which reported a statistically significant association with OA were screened. An integrated network and pathway analysis was performed to identify the pathways and genes most associated to OA. Moreover, OA-specific protein-protein interaction (PPI) network was constructed by cytocluster based on the Molecular Complex Detection Algorithm (MCODE) to screen its candidate biomarkers. Quantitative real-time polymerase chain reaction was used to confirm the expression levels and to validate the results of MCODE cluster analysis by six genes. The pathway networks suggested that extracellular matrix (ECM) organization, collagen degradation and collagen formation showed important associations with OA. In top two PPI clusters, 61 of the OA-associated genes were included in the OA-specific PPI network, which also included 23 candidate genes that are likely to be highly associated with OA based on MCODE clusters. Analysis of mRNA showed that the expression levels of COL9A1, COL9A2, ITGA3, COL9A3, ITGA2, and LAMA1 in the peripheral blood mononuclear cells of OA patients were significantly lower than those of the normal controls (p<0.005). To our knowledge, this is the first comprehensive and systematic report based on OA-related genes demonstrating that the functional destruction of collagen in cartilage may be a very important contributing factor to OA. Quantitative detection of collagen synthesis may be of great help in early identification and prediction of OA. Maintaining the quality and quantity of collagen can be a potential target for clinical treatment of OA in the future practice.
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