ArticleClinical & translational immunology2019
Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus.
Article in Clinical & translational immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- A pathogenic gut lipoglycan drives systemic thromboinflammation in lupus nephritis.Annals of the rheumatic diseases · 2026Article
- Machine learning reveals X chromosome transcriptional signatures that classify systemic lupus erythematosus in females.BMC rheumatology · 2026Article
- Transferrin receptor 1 shedding by the pro-inflammatory iRhom-ADAM17 complex and ADAM10 regulates cellular iron uptake and ferroptosis.Experimental & molecular medicine · 2026Article
- Validity and applicability of machine learning models for systemic lupus erythematosus diagnosis.Lupus science & medicine · 2026Article
- Blood parameters and whole-blood transcriptomics associated with immune-related adverse events in metastatic renal cell carcinoma during nivolumab plus ipilimumab.Scientific reports · 2026Article
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Neural-immune-cancer crosstalk in pancreatic cancer: mechanisms and clinical translation.Frontiers in cell and developmental biology · 2026Review
- Article
- Data-Driven Cluster Analysis of Cerebrospinal Fluid Proteome and Associations with Clinical Phenotypes in Systemic Lupus Erythematosus.ACR open rheumatology · 2025Article
- Neutrophil Heterogeneity Identifies an Association of LAMP1 With Proliferative Lupus Nephritis.European journal of immunology · 2025Article
- Pathogenic strains of a gut commensal drive systemic platelet activation and thromboinflammation in lupus nephritis.bioRxiv : the preprint server for biology · 2025Article
- Dog10K: an integrated Dog10K database summarizing canine multi-omics.Nucleic acids research · 2025Article
- Association of hyperactivated transposon expression with exacerbated immune activation in systemic lupus erythematosus.Mobile DNA · 2024Article
- Integrated image and location analysis for wound classification: a deep learning approach.Scientific reports · 2024Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Phenotypic Clustering of Beta-Thalassemia Intermedia Patients Using Cardiovascular Magnetic Resonance.Journal of clinical medicine · 2023Article
- An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients.iScience · 2023Article
- Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications.Genome medicine · 2023Article
- Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes.Annals of the rheumatic diseases · 2023Article
- SLE is not a one-size-fits-all disease.The Journal of experimental medicine · 2023Article
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9 authors.
Funding
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Abstract
objectivesSystemic 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.
methodsWe 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.
resultsExamination 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.
conclusionGiven 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.
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