{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T22:24:46Z","timestamp":1792448686470,"version":"4.5.0"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81772261"],"award-info":[{"award-number":["81772261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Metabolic dysregulation and redox imbalance in immune cells are key drivers of systemic lupus erythematosus (SLE) pathogenesis. This study explores critical oxidative stress (OS) features and their interrelationships in SLE pathogenesis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Three transcriptomic datasets from the Gene Expression Omnibus (GEO) were analyzed to identify SLE- and OS-associated pathways via Gene Set Variation Analysis (GSVA). Multiple machine learning methods\u2014including deep learning (DL), random forest (RF), XGBoost, support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO)\u2014were deployed to build OS-related gene prediction frameworks. Immune infiltration was assessed using CIBERSORT, and single-cell transcriptomic data from GEO elucidated gene expression patterns in various immune cell subsets. Peripheral blood plasma samples from confirmed SLE patients and healthy controls (HC) were analyzed using liquid chromatography-mass spectrometry (LC-MS) for metabolomics profiling and to evaluate OS and antioxidant stress (AOS) levels. Finally, real-time quantitative PCR (RT-qPCR) was used to validate the expression differences of key genes in peripheral blood mononuclear cells (PBMCs) from SLE patients and HC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>GSVA identified 15 metabolic pathways significantly linked to SLE, seven of which were strongly associated with OS and energy metabolism. LC-MS revealed substantial alterations in serum OS-related metabolites, clearly distinguishing SLE patients from healthy controls. A comprehensive machine learning approach pinpointed 10 OS-related genes; among these, six (ABCB1, AKR1C3, EIF2AK2, IFIH1, NPC1, SCO2) showed robust predictive performance and significant correlations with immune cell subsets. Single-cell analysis confirmed these genes\u2019 expression in diverse immune cell types, consistent with the observed metabolic pathway disruptions. RT-qPCR verified downregulation of ABCB1, AKR1C3, and NPC1 and upregulation of EIF2AK2, IFIH1, and SCO2 in SLE PBMCs. SLE patients exhibited higher OS levels and lower AOS levels. Correlation analysis underscored strong relationships among key genes, OS\/AOS levels, and vital metabolites.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This multi-omics and machine learning\u2013based investigation uncovered major disruptions in OS-related metabolic pathways and metabolites in SLE, ultimately identifying six key genes with distinct expression patterns across immune cell subsets. Their strong associations with OS\/AOS levels and crucial metabolites highlight their diagnostic and therapeutic potential, laying a foundation for early detection and targeted treatment strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1567466","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T07:17:09Z","timestamp":1750403829000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus 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