Department of Rheumatology and Immunology, Shanghai Changhai Hospital, the first affiliated Hospital of Naval Medical University, Shanghai 200433, People's Republic of China.
Department of Joint Bone Disease Surgery, Shanghai Changhai Hospital, the first affiliated Hospital of Naval Medical University, Shanghai 200433, People's Republic of China.
Int Immunopharmacol. 2024 Apr 20;131:111809. doi: 10.1016/j.intimp.2024.111809. Epub 2024 Mar 13.
Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that is characterized by persistent morning stiffness, joint pain, and swelling. However, there is a lack of reliable diagnostic markers and therapeutic targets that are both effective and trustworthy.
In this study, gene expression profiles (GSE89408, GSE55235, GSE55457, and GSE77298) were obtained from the Gene Expression Omnibus database. Differentially expressed necroptosis-related genes were attained from intersection of necroptosis-related gene set, differentially expressed genes, and weighted gene co-expression network analysis. The LASSO, random forest, and SVM-RFE machine learning algorithms were utilized to further screen potential diagnostic genes for RA. Immune cell infiltration was analyzed using the CIBERSORT method. The expressions of diagnostic genes were validated through quantitative real-time PCR, western blotting, immunohistochemistry, and immunofluorescence staining in synovial tissues collected from three trauma controls and three RA patients.
Five core necroptosis-related genes (FAS, CYBB, TNFSF10, EIF2AK2, and BIRC2) were identified as potential biomarkers for RA. Two different necroptosis patterns based on these five genes were confirmed to significantly correlated with immune cells (especially macrophages). In vitro experiments showed significantly higher mRNA and protein expression levels of CYBB and EIF2AK2 in RA patients compared to normal controls, consistent with the bioinformatics analysis results.
Our study identified a novel necroptosis-related subtype and five diagnostic biomarkers of RA, revealed vital roles in the development and occurrence of RA, and offered potential targets for clinical diagnosis and immunotherapy.
类风湿关节炎（RA）是一种慢性自身免疫性炎症性疾病，其特征为持续晨僵、关节疼痛和肿胀。然而，目前缺乏既有效又可靠的诊断标志物和治疗靶点。
本研究从基因表达综合数据库中获取基因表达谱（GSE89408、GSE55235、GSE55457 和 GSE77298）。通过交集坏死相关基因集、差异表达基因和加权基因共表达网络分析，获得差异表达的坏死相关基因。利用 LASSO、随机森林和 SVM-RFE 机器学习算法进一步筛选 RA 的潜在诊断基因。采用 CIBERSORT 方法分析免疫细胞浸润。通过定量实时 PCR、western blot、免疫组化和免疫荧光染色验证诊断基因在取自 3 例创伤对照和 3 例 RA 患者的滑膜组织中的表达。
确定了 5 个核心坏死相关基因（FAS、CYBB、TNFSF10、EIF2AK2 和 BIRC2），它们可能是 RA 的潜在生物标志物。基于这 5 个基因的两种不同坏死模式被证实与免疫细胞（尤其是巨噬细胞）显著相关。体外实验显示，与正常对照组相比，RA 患者的 CYBB 和 EIF2AK2 mRNA 和蛋白表达水平明显更高，与生物信息学分析结果一致。
本研究确定了一种新的坏死相关亚型和 5 个 RA 的诊断生物标志物，揭示了它们在 RA 发展和发生中的重要作用，并为临床诊断和免疫治疗提供了潜在的靶点。
