OBJECTIVES Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients' whole-blood transcriptomes. METHODS We applied machine learning approaches to RNA-sequencing (RNA-seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on three recently published whole-blood RNA-seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. RESULTS Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. CONCLUSION Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

中文翻译：

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应用于全血 RNA 测序数据的机器学习揭示了系统性红斑狼疮患者的不同亚群。

目的 系统性红斑狼疮 (SLE) 是一种难以治疗的异质性自身免疫性疾病。目前还没有 SLE 患者的最佳分层，因此对可用治疗的反应是不可预测的。在这里，我们基于对患者全血转录组的计算分析，为 SLE 患者开发了一种新的分层方案。方法 我们将机器学习方法应用于 RNA 测序 (RNA-seq) 数据集，以根据 SLE 患者的基因表达谱将其分为四个不同的集群。对最近发表的三个全血 RNA-seq 数据集进行了荟萃分析，另外一个类似的数据集包括 30 名 SLE 患者和 29 名健康供体。共分析了 161 名 SLE 患者和 57 名健康供体。结果 与未分层的 SLE 患者相比，SLE 簇​​的检查揭示了疾病相关基因表达模式相对于临床表现的未被充分认识的差异。此外，成功鉴定了与耀斑活性相关的基因特征。结论 鉴于 SLE 疾病的异质性是阻碍最佳临床试验设计和患者充分管理的关键挑战，我们的方法开辟了一条新的可能途径，通过更深入地了解人类 SLE 异质性来解决这一限制。基于基因表达特征的患者分层可能是一种有价值的策略，可以识别支持 ​​SLE 疾病的单独分子机制。更远，