PDF(Pubmed)
Abstract：
UNASSIGNED: For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.
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.
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.
摘要：
■对于类风湿性关节炎（RA），长期的慢性疾病,识别和描述具有可比的目标状态和分子生物标志物的患者亚型至关重要.本研究旨在开发和验证一种新的分型方案，该方案整合了RA外周血基因的基因组尺度转录组学图谱，为分层治疗提供了新的视角。
■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。
■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。
