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Interaction network of differentially expressed genes between Kashin-Beck disease and osteoarthritis / 中华地方病学杂志
Chinese Journal of Endemiology ; (12): 472-476, 2016.
Article in Chinese | WPRIM | ID: wpr-498028
ABSTRACT
Objective To investigate the data of gene expression microarray by protein interaction network analysis,establish an interaction network of differentially expressed genes between Kashin-Beck disease (KBD) and osteoarthritis (OA) and choose the central nodes of the network.Methods The articular cartilage samples of degrees Ⅱ ° and Ⅲ ° KBD and OA patients were selected according to the national diagnosis criteria for KBD and the Western Ontario and McMaster Universities (WOMAC) for OA.Chondrocytes of 8 patients with KBD and 7 with OA were selected.About 1 000 different genes detected by gene expression microarray were inputted into STRING 9.1 database online for analysis and establishment of the interaction network.The interaction data were imported into Cytoscape 3.2.1 software for screening the central nodes of the network.KEGG database was exploited for pathway analysis and functional study of the central node genes,Real-time PCR (RT-PCR) was used for verification.Results The protein products of 334 differentially expressed genes between KBD and OA had interrelation,forming a complicated interaction network.About 150 central nodes were selected by Cytoscape 3.2.1 that involved in more than ten signal pathways involved in mitochondria,bone metabolism and inflammatory cytokine.Conclusion The interaction network of the differentially expressed genes between KBD and OA,especially the central nodes of this network,can provide clues to the mechanism and early diagnosis and molecular targeted therapy of KBD and OA.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Screening study Language: Chinese Journal: Chinese Journal of Endemiology Year: 2016 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Screening study Language: Chinese Journal: Chinese Journal of Endemiology Year: 2016 Type: Article