Zhou Jian, Zou Dazhi, Wan Rongjun, Liu Jie, Zhou Qiong, Zhou Zhen, Wang Wanchun, Tao Cheng, Liu Tang
Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China.
Department of Spine Surgery, Longhui People's Hospital, Shaoyang, China.
Front Genet. 2022 Jun 6;13:870590. doi: 10.3389/fgene.2022.870590. eCollection 2022.
The present study was performed to explore the underlying molecular mechanisms and screen hub genes of osteoarthritis (OA) bioinformatics analysis. In total, twenty-five OA synovial tissue samples and 25 normal synovial tissue samples were derived from three datasets, namely, GSE55457, GSE55235, and GSE1919, and were used to identify the differentially expressed genes (DEGs) of OA by R language. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A Venn diagram was built to show the potential hub genes identified in all three datasets. The STRING database was used for constructing the protein-protein interaction (PPI) networks and submodules of DEGs. We identified 507 upregulated and 620 downregulated genes. Upregulated DEGs were significantly involved in immune response, MHC class II receptor activity, and presented in the extracellular region, while downregulated DEGs were mainly enriched in response to organic substances, extracellular region parts, and cadmium ion binding. Results of KEGG analysis indicated that the upregulated DEGs mainly existed in cell adhesion molecules (CAMs), while downregulated DEGs were significantly involved in the MAPK signaling pathway. A total of eighteen intersection genes were identified across the three datasets. These include Nell-1, ATF3, RhoB, STC1, and VEGFA. In addition, 10 hub genes including CXCL12, CXCL8, CCL20, and CCL4 were found in the PPI network and module construction. Identification of DEGs and hub genes associated with OA may be helpful for revealing the molecular mechanisms of OA and further promotes the development of relevant biomarkers and drug targets.
本研究旨在探索骨关节炎（OA）生物信息学分析的潜在分子机制并筛选核心基因。总共从三个数据集（即GSE55457、GSE55235和GSE1919）中获取了25个OA滑膜组织样本和25个正常滑膜组织样本，并使用R语言来鉴定OA的差异表达基因（DEG）。利用注释、可视化与集成发现数据库（DAVID）对DEG进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析。构建维恩图以展示在所有三个数据集中鉴定出的潜在核心基因。STRING数据库用于构建DEG的蛋白质-蛋白质相互作用（PPI）网络和子模块。我们鉴定出507个上调基因和620个下调基因。上调的DEG显著参与免疫反应、MHC II类受体活性，并存在于细胞外区域，而下调的DEG主要富集于对有机物质的反应、细胞外区域部分和镉离子结合。KEGG分析结果表明，上调的DEG主要存在于细胞黏附分子（CAM）中，而下调的DEG显著参与MAPK信号通路。在这三个数据集中总共鉴定出18个交集基因。这些基因包括Nell-1、ATF3、RhoB、STC1和VEGFA。此外，在PPI网络和模块构建中发现了10个核心基因，包括CXCL12、CXCL8、CCL20和CCL4。鉴定与OA相关的DEG和核心基因可能有助于揭示OA的分子机制，并进一步推动相关生物标志物和药物靶点的开发。
