Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus
ObjectiveMetabolic 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.MethodsThree transcriptomic datasets from the Gene Exp...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1567466/full |
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| author | Hongwei Zhou Xiaoqing Li Yanyu Zhang Feng Wei Zhiyu Liu Yan Zhao Xubo Zhuang Xia Liu Haizhou Zhou |
| author_facet | Hongwei Zhou Xiaoqing Li Yanyu Zhang Feng Wei Zhiyu Liu Yan Zhao Xubo Zhuang Xia Liu Haizhou Zhou |
| author_sort | Hongwei Zhou |
| collection | DOAJ |
| description | ObjectiveMetabolic 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.MethodsThree 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—including deep learning (DL), random forest (RF), XGBoost, support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO)—were 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.ResultsGSVA 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’ 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.ConclusionThis multi-omics and machine learning–based 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. |
| format | Article |
| id | doaj-art-e4dc3e9481fe4ea5b4bc6c3d44eaea21 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-e4dc3e9481fe4ea5b4bc6c3d44eaea212025-08-20T03:31:24ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-06-011610.3389/fimmu.2025.15674661567466Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosusHongwei ZhouXiaoqing LiYanyu ZhangFeng WeiZhiyu LiuYan ZhaoXubo ZhuangXia LiuHaizhou ZhouObjectiveMetabolic 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.MethodsThree 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—including deep learning (DL), random forest (RF), XGBoost, support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO)—were 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.ResultsGSVA 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’ 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.ConclusionThis multi-omics and machine learning–based 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.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1567466/fullsystemic lupus erythematosusoxidative stressmetabolomicstranscriptomicssingle-cell transcriptomicmachine learning |
| spellingShingle | Hongwei Zhou Xiaoqing Li Yanyu Zhang Feng Wei Zhiyu Liu Yan Zhao Xubo Zhuang Xia Liu Haizhou Zhou Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus Frontiers in Immunology systemic lupus erythematosus oxidative stress metabolomics transcriptomics single-cell transcriptomic machine learning |
| title | Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus |
| title_full | Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus |
| title_fullStr | Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus |
| title_full_unstemmed | Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus |
| title_short | Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus |
| title_sort | machine learning combined multi omics analysis to explore key oxidative stress features in systemic lupus erythematosus |
| topic | systemic lupus erythematosus oxidative stress metabolomics transcriptomics single-cell transcriptomic machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1567466/full |
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