Tang Chengyang, Liu Qiang, Zhang Yaxuan, Liu Guihu, Shen Guangsi
Department of Orthopaedics, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Institution of Sports Medicine, Peking University Third Hospital, Beijing Key Laboratory of Sports Injuries, Beijing, People's Republic of China.
Int J Gen Med. 2021 Dec 22;14:10235-10245. doi: 10.2147/IJGM.S342286. eCollection 2021.
Osteoarthritis (OA) is the most common chronic joint disorder in elderly individuals. This study aimed to identify immune-related diagnostic gene signatures for OA.
First, we performed single-sample gene set enrichment analysis (ssGSEA) to evaluate the infiltration of immune cells in OA expression data from the Gene Expression Omnibus (GEO) database. Then, weighted gene coexpression network analysis (WGCNA) was performed to identify hub modules and genes related to immune cell types with significant infiltration. Finally, we screened diagnostic markers from the differentially expressed genes (DEGs) in both the OA group and the hub module using least absolute shrinkage and selection operator (LASSO) logistic regression.
Immune filtration analysis showed that immature B cells, mast cells, natural killer T cells, myeloid-derived suppressor cells (MDSCs), and type 2 T helper cells were dysregulated in OA samples. In WGCNA, a total of 120 genes were selected as hub genes associated with mast cell infiltration.The enrichment analysis showed that spliceosome, positive regulation of cell migration, and response to mechanical stimulus were mainly involved. The LASSO regression model for the GSE117999 dataset revealed 15 DEGs for predicting OA. Finally, two genes were obtained by intersection for further investigation.
Cold-inducible RNA-binding protein (CIRBP) and transient receptor potential vanilloid 4 (TRPV4) were identified as diagnostic biomarkers for OA, and both were positively correlated with mast cell infiltration.
骨关节炎(OA)是老年人中最常见的慢性关节疾病。本研究旨在识别与OA相关的免疫诊断基因特征。
首先,我们进行单样本基因集富集分析(ssGSEA)以评估来自基因表达综合数据库(GEO)的OA表达数据中免疫细胞的浸润情况。然后,进行加权基因共表达网络分析(WGCNA)以识别与浸润显著的免疫细胞类型相关的枢纽模块和基因。最后,我们使用最小绝对收缩和选择算子(LASSO)逻辑回归从OA组和枢纽模块中的差异表达基因(DEG)中筛选诊断标志物。
免疫过滤分析表明,未成熟B细胞、肥大细胞、自然杀伤T细胞、骨髓来源的抑制细胞(MDSC)和2型辅助性T细胞在OA样本中失调。在WGCNA中,总共120个基因被选为与肥大细胞浸润相关的枢纽基因。富集分析表明,主要涉及剪接体、细胞迁移的正调控和对机械刺激的反应。GSE117999数据集的LASSO回归模型揭示了15个用于预测OA的DEG。最后,通过交集获得两个基因以供进一步研究。
冷诱导RNA结合蛋白(CIRBP)和瞬时受体电位香草酸受体4(TRPV4)被鉴定为OA的诊断生物标志物,且两者均与肥大细胞浸润呈正相关。