UNASSIGNED: We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.
UNASSIGNED: Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs.
UNASSIGNED: The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.
■我们利用RA外周血单核细胞(PBMC)的独立微阵列数据集。对上调的差异表达基因(DEGs)进行功能富集分析。然后采用无监督聚类分析来鉴定RA外周血基因表达驱动的亚型。我们基于识别的404个上调的DEGs定义了三种不同的聚类亚型。
■子类型A,名为NE驾驶,富含与中性粒细胞活化和对细菌反应相关的途径。亚型B,称为干扰素驱动(IFN驱动),表现出丰富的B细胞,并显示参与IFN信号传导和对病毒的防御反应的转录本的表达增加。在亚型C中,发现了CD8+T细胞的富集,最终将其定义为CD8+T细胞驱动。使用XGBoost机器学习算法对RA亚型方案进行了验证。我们还评估了生物疾病缓解抗风湿药物的治疗效果。
■这些发现为深层分层提供了有价值的见解,能够设计分子诊断,并作为未来RA患者分层治疗的参考。