Xu Jintao, Chen Kai, Yu Yaohui, Wang Yishu, Zhu Yi, Zou Xiangjie, Jiang Yiqiu
Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing 210000, China.
Jiangsu Province Hospital, The First Affiliated Hospital With Nanjing Medical University, Nanjing 210000, China.
J Pers Med. 2023 Feb 19;13(2):367. doi: 10.3390/jpm13020367.
In this research, we aimed to perform a comprehensive bioinformatic analysis of immune cell infiltration in osteoarthritic cartilage and synovium and identify potential risk genes. Datasets were downloaded from the Gene Expression Omnibus database. We integrated the datasets, removed the batch effects and analyzed immune cell infiltration along with differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to identify the positively correlated gene modules. LASSO (least absolute shrinkage and selection operator)-cox regression analysis was performed to screen the characteristic genes. The intersection of the DEGs, characteristic genes and module genes was identified as the risk genes. The WGCNA analysis demonstrates that the blue module was highly correlated and statistically significant as well as enriched in immune-related signaling pathways and biological functions in the KEGG and GO enrichment. LASSO-cox regression analysis screened 11 characteristic genes from the hub genes of the blue module. After the DEG, characteristic gene and immune-related gene datasets were intersected, three genes, PTGS1, HLA-DMB and GPR137B, were identified as the risk genes in this research. In this research, we identified three risk genes related to the immune system in osteoarthritis and provide a feasible approach to drug development in the future.
在本研究中，我们旨在对骨关节炎软骨和滑膜中的免疫细胞浸润进行全面的生物信息学分析，并识别潜在的风险基因。数据集从基因表达综合数据库下载。我们整合了数据集，消除了批次效应，并分析了免疫细胞浸润以及差异表达基因（DEG）。使用加权基因共表达网络分析（WGCNA）来识别正相关的基因模块。进行最小绝对收缩和选择算子（LASSO）-cox回归分析以筛选特征基因。将DEG、特征基因和模块基因的交集确定为风险基因。WGCNA分析表明，蓝色模块高度相关且具有统计学意义，并且在KEGG和GO富集分析中富集于免疫相关信号通路和生物学功能。LASSO-cox回归分析从蓝色模块的枢纽基因中筛选出11个特征基因。在对DEG、特征基因和免疫相关基因数据集进行交集分析后，确定PTGS1、HLA-DMB和GPR137B这三个基因为本研究中的风险基因。在本研究中，我们识别出与骨关节炎免疫系统相关的三个风险基因，并为未来的药物开发提供了一种可行的方法。
