ArticleBMC immunology2023
Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis.
Article in BMC immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 15 citations in OpenAlex.
- Integration of Single-Cell and Bulk RNA Sequencing Data to Identify Lactylation-Related Gene Signatures in Hepatic Ischemia-Reperfusion Injury Using Machine Learning Algorithms.International journal of molecular sciences · 2026Article
- Recent developments in the use of biomarkers as diagnostic tools in patients with systemic lupus erythematosus: a narrative review.EULAR rheumatology open · 2026Review
- MX2 and IRF7 are Associated With Disease Activity and Renal Involvement in Systemic Lupus Erythematosus: An Exploratory Study.Journal of immunology research · 2026Article
- Article
- Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer.Scientific reports · 2025Article
- Machine Learning Integration of Bulk and Single-Cell RNA-Seq Data Reveals Cathepsin B as a Central PANoptosis Regulator in Influenza.International journal of molecular sciences · 2025Article
- Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus.Frontiers in immunology · 2025Article
- Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.Frontiers in immunology · 2025Article
- Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.Journal of translational medicine · 2024Article
- TOX: a potential new immune checkpoint in cancers by pancancer analysis.Discover oncology · 2024Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Identification and experimental validation of Alzheimer's disease hub genes via bioinformatics and machine learning.Journal of Alzheimer's disease reportsArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIn recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.
methodsSLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time quantitative polymerase chain reaction (RT-qPCR) assay.
resultsFive key genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, these key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic curves (ROC) of the five genes also revealed that they had critical roles in the pathogenesis of SLE.
conclusionIn summary, five key genes were obtained and validated through machine-learning analysis, offering a new perspective for the molecular mechanism and potential therapeutic targets for SLE.
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