Zhu Y C, Deng B Y, Zhang L G, Xu P, Du X P, Zhang Q G, Yang B
Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Qidong Hospital of TCM, Jiangsu, China.
Genet Mol Res. 2014 Nov 11;13(4):9343-51. doi: 10.4238/2014.November.11.1.
The purpose of this study was to identify genes and pathways for osteoarthritis (OA) diagnosis and therapy. We downloaded the gene expression profile of OA from Gene Expression Omnibus (GEO) database including 10 early OA, 9 late OA, and 5 normal control samples. Next, we screened differentially expressed genes (DEGs) between early- and late-stage OA samples comparing with healthy control samples. Then, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software was used to construct protein-protein interaction (PPI) network, which was to predict the proteins that may interact with DEGs. The Gene Ontology (GO)-enrichment method was used to analyze the function of genes in the PPI networks. Meanwhile network module analysis was performed using Cytoscape. A total of 24 and 29 DEGs were identified for the early and late OA, respectively. TAC1 showed the highest degree in the PPI network. Functional annotation of the TAC1 network module indicated that this gene is associated with the G protein-coupled signal transduction pathway. In summary, TAC1, together with G protein-coupled receptors, appear to play a role in the biogenesis and progress of OA. Further analysis of this gene and pathway could therefore provide a potential target for the diagnosis and treatment of OA.
本研究的目的是确定用于骨关节炎(OA)诊断和治疗的基因及信号通路。我们从基因表达综合数据库(GEO)下载了OA的基因表达谱,包括10例早期OA、9例晚期OA和5例正常对照样本。接下来,我们筛选了早期和晚期OA样本与健康对照样本之间的差异表达基因(DEG)。然后,使用检索相互作用基因/蛋白质的搜索工具(STRING)软件构建蛋白质-蛋白质相互作用(PPI)网络,以预测可能与DEG相互作用的蛋白质。基因本体(GO)富集方法用于分析PPI网络中基因的功能。同时,使用Cytoscape进行网络模块分析。早期和晚期OA分别共鉴定出24个和29个DEG。TAC1在PPI网络中显示出最高的度。TAC1网络模块的功能注释表明该基因与G蛋白偶联信号转导通路相关。总之,TAC1与G蛋白偶联受体似乎在OA的发生和发展中起作用。因此,对该基因和信号通路的进一步分析可为OA的诊断和治疗提供潜在靶点。