Ji Xinxin, Li Lingyun, Jiao Yuanzhuo, Cheng Hui
School of Nursing, Shanxi Medical University, Taiyuan, 030000, China.
Sci Rep. 2025 Aug 23;15(1):31042. doi: 10.1038/s41598-025-15370-5.
Rheumatoid arthritis (RA) is increasingly prevalent among older adults, who often experience more severe symptoms and face significant treatment challenges. This study aims to identify specific genes associated with aging in RA and to analyze their immune infiltration using machine learning techniques. We sourced senescent genes from the HARG database and utilized three RA patient datasets obtained from the GEO database. Differential analysis revealed 50 age-related differentially expressed genes (ARDEGs) that intersected with senescent genes. Hub genes were identified through protein-protein interaction (PPI) network analysis as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning methods, including LASSO regression, random forest (RF), and support vector machine recursive feature elimination (SVM-RFE), were employed to extract feature genes. Single-sample gene set enrichment analysis (ssGSEA) quantified immune cell infiltration, revealing 242 up-regulated and 176 down-regulated differentially expressed genes (DEGs). Notably, high levels of effector memory CD8 T cells and macrophages were found to be associated with robust immune responses. This study successfully identified four biomarkers related to aging in RA, suggesting that STAT1 may serve as a viable therapeutic target. These findings have the potential to enhance treatment strategies and improve patient outcomes while providing valuable insights into immune cell subpopulations in RA.
类风湿性关节炎（RA）在老年人中越来越普遍，这些老年人往往经历更严重的症状，并面临重大的治疗挑战。本研究旨在识别与RA衰老相关的特定基因，并使用机器学习技术分析其免疫浸润情况。我们从HARG数据库中获取衰老基因，并利用从GEO数据库获得的三个RA患者数据集。差异分析揭示了50个与衰老相关的差异表达基因（ARDEGs），这些基因与衰老基因相交。通过蛋白质-蛋白质相互作用（PPI）网络分析以及基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析确定了枢纽基因。采用包括套索回归、随机森林（RF）和支持向量机递归特征消除（SVM-RFE）在内的机器学习方法来提取特征基因。单样本基因集富集分析（ssGSEA）对免疫细胞浸润进行了量化，揭示了242个上调和176个下调的差异表达基因（DEGs）。值得注意的是，发现高水平的效应记忆CD8 T细胞和巨噬细胞与强大的免疫反应相关。本研究成功识别了四个与RA衰老相关的生物标志物，表明STAT1可能是一个可行的治疗靶点。这些发现有可能加强治疗策略并改善患者预后，同时为RA中的免疫细胞亚群提供有价值的见解。
