Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus
This 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.
Authors
- Yan Zha (ORCID: https://orcid.org/0000-0002-8713-1665)
- Zhiyu Liu (ORCID: https://orcid.org/0000-0002-6646-6546)
- Hongwei Zhou (ORCID: https://orcid.org/0000-0002-7518-9464)
- Wei Feng (ORCID: https://orcid.org/0000-0002-8304-2737)
- Yanyu Zhang (ORCID: https://orcid.org/0000-0001-8992-1719)
- Xia Liu (ORCID: https://orcid.org/0000-0002-5682-0521)
- Xiaoqing Li (ORCID: https://orcid.org/0000-0001-9689-5862)
- Xuliang Zhuang (ORCID: https://orcid.org/0000-0001-5902-4267)
Institutions
- Harbin Medical University (CN)
- First Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-06-20
- DOI
- https://doi.org/10.3389/fimmu.2025.1567466
- Citations
- 5
- Primary Topic
- Systemic Lupus Erythematosus Research
- Type
- article
- Field-Weighted Citation Impact
- 8.34
Funders
- National Natural Science Foundation of China