Dong Shuanghai, Xia Tian, Wang Lei, Zhao Qinghua, Tian Jiwei
Shanghai First People's Hospital, Shanghai, China.
Shanghai First People's Hospital, Shanghai, China.
Acta Orthop Traumatol Turc. 2016 Dec;50(6):686-690. doi: 10.1016/j.aott.2016.04.002. Epub 2016 Nov 18.
To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation.
Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein-protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules.
In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle.
The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine-cytokine receptor interaction pathway.
探讨骨关节炎(OA)的发病机制,为进一步研究提供有效的生物学信息。
从基因表达综合数据库下载GSE46750基因表达谱。使用微阵列数据线性模型(limma)软件包(生物导体项目,http://www.bioconductor.org/packages/release/bioc/html/limma.html)鉴定炎症性OA样本中的差异表达基因(DEG)。基于注释、可视化和综合发现数据库对DEG进行基因本体功能富集分析和京都基因与基因组百科全书(KEGG)通路富集分析,并基于相互作用基因/蛋白质检索工具数据库构建蛋白质-蛋白质相互作用(PPI)网络。基于DNA元件百科全书筛选调控网络。使用分子复合物检测进行子网筛选。将两个节点度最高的子网与转录调控网络整合,并对2个模块进行KEGG功能富集分析。
共获得401个上调和196个下调的DEG。上调的DEG参与炎症反应,而下调的DEG参与细胞周期。构建了具有2392个蛋白质相互作用的PPI网络。此外,发现包括白细胞介素6(IL6)和极光B激酶(AURKB)在内的10个基因在PPI网络中表现突出。转录因子(TF)调控网络中有214个上调和8个下调的TF-靶标对。模块1包含SPI1、PRDM1和FOS等TF,而模块2包含FOSL1。模块1中的节点富集于趋化因子信号通路,而模块2中的节点主要富集于细胞周期。
筛选出的包括IL6、AGT和AURKB在内的DEG可能是OA基因治疗的潜在生物标志物,它们受FOS和SPI1等TF调控,参与细胞周期和细胞因子-细胞因子受体相互作用通路。