Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus

• Published in: Rheumatology (Oxford, England) England), 2023-07, Vol.62 (7), p.2574-2584
• Main Authors: Qiao, Jun, Zhang, Sheng-Xiao, Chang, Min-Jing, Zhao, Rong, Song, Shan, Hao, Jia-Wei, Wang, Can, Hu, Jing-Xi, Gao, Chong, Wang, Cai-Hong, Li, Xiao-Feng
• Format: Article
• Language: English
• Subjects: Cell activation; Datasets; DNA microarrays; Gene Expression Profiling - methods; Humans; Leukocytes (mononuclear); Leukocytes (neutrophilic); Leukocytes, Mononuclear - metabolism; Lupus; Lupus Erythematosus, Systemic - drug therapy; Lupus Erythematosus, Systemic - genetics; Lymphocytes; Microarray Analysis; Patients; Peripheral blood mononuclear cells; Systemic lupus erythematosus; Therapeutic targets; Transcriptome; Transcriptomes; Transcriptomics
• ISSN: 1462-0324; 1462-0332
• DOI: 10.1093/rheumatology/keac625

Abstract Objectives To leverage the high clinical heterogeneity of systemic lupus erythematosus (SLE), we developed and validated a new stratification scheme by integrating genome-scale transcriptomic profiles to identify patient subtypes sharing similar transcriptomic markers and drug targets. Methods A normalized compendium of transcription profiles was created from peripheral blood mononuclear cells (PBMCs) of 1046 SLE patients and 86 healthy controls (HCs), covering an intersection of 13 689 genes from six microarray datasets. Upregulated differentially expressed genes were subjected to functional and network analysis in which samples were grouped using unsupervised clustering to identify patient subtypes. Then, clustering stability was evaluated by the stratification of six integrated RNA-sequencing datasets using the same method. Finally, the Xgboost classifier was applied to the independent datasets to identify factors associated with treatment outcomes. Results Based on 278 upregulated DEGs of the transcript profiles, SLE patients were classified into three subtypes (subtype A–C) each with distinct molecular and cellular signatures. Neutrophil activation-related pathways were markedly activated in subtype A (named NE-driving), whereas lymphocyte and IFN-related pathways were more enriched in subtype B (IFN-driving). As the most severe subtype, subtype C [NE-IFN-dual-driving (Dual-driving)] shared functional mechanisms with both NE-driving and IFN-driving, which was closely associated with clinical features and could be used to predict the responses of treatment. Conclusion We developed the largest cohesive SLE transcriptomic compendium for deep stratification using the most comprehensive microarray and RNA sequencing datasets to date. This result could guide future design of molecular diagnosis and the development of stratified therapy for SLE patients. Graphical Abstract