ArticleLupus science & medicine2024
Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets.
Article in Lupus science & medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Oxidative-Stress-Associated Molecular Signatures in Immune-Mediated Diseases: A Systematic Review Integrating Machine Learning and Systems Biology Approaches.Antioxidants (Basel, Switzerland) · 2026Review
- The Role of Antioxidant Transcription Factor Nrf2 and Its Activating Compounds in Systemic Lupus Erythematosus.Antioxidants (Basel, Switzerland) · 2024Review
- Lupus Nephritis from Pathogenesis to New Therapies: An Update.International journal of molecular sciences · 2024Review
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches.
methodsLN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN.
resultsThe study identified four hub genes, namely
conclusionThese findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets.
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Registered trials
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