ArticleJournal of inflammation research2024
Identification of m6A-Related Biomarkers in Systemic Lupus Erythematosus: A Bioinformation-Based Analysis.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Review
- Epitranscriptomic regulation by m6A in immunity and autoimmune disorders: emerging mechanisms and clinical perspectives.Frontiers in immunology · 2026Review
- Epigenetic modifier m⁶A methylation: insights into the pathogenesis and therapeutic potential of autoimmune diseases.Journal of translational medicine · 2025Review
- Machine Learning Framework for Ovarian Cancer Diagnostics Using Plasma Lipidomics and Metabolomics.International journal of molecular sciences · 2025Article
- Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes.PloS one · 2025Article
- The Roles of RNA N6-methyladenosine Modifications in Systemic Lupus Erythematosus.Cell biochemistry and biophysics · 2024Review
- Biomarkers for systemic lupus erythematosus: A scoping review.Immunity, inflammation and disease · 2024Article
- POLE -related gene signature predicts prognosis, immune feature, and drug therapy in human endometrioid carcinoma.Heliyon · 2024Article
- An updated review on abnormal epigenetic modifications in the pathogenesis of systemic lupus erythematosus.Frontiers in immunology · 2024Review
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
Background: Systemic Lupus Erythematosus (SLE), a prototypical autoimmune disorder, presents a challenge due to the absence of reliable biomarkers for discerning organ-specific damage within SLE. A growing body of evidence underscores the pivotal involvement of N6-methyladenosine (m6A) in the etiology of autoimmune conditions. Methods: The datasets, which primarily encompassed the expression profiles of m6A regulatory genes, were retrieved from the Gene Expression Omnibus (GEO) repository. The optimal model, selected from either Random Forest (RF) or Support Vector Machine (SVM), was employed for the development of a predictive nomogram model. To identify pivotal genes associated with SLE, a comprehensive screening process was conducted utilizing LASSO, SVM-RFE, and RF techniques. Within the realm of SLE susceptibility, Weighted Gene Co-expression Network Analysis (WGCNA) was harnessed to delineate relevant modules and hub genes. Additionally, MeRIP-qPCR assays were performed to elucidate key genes correlated with m6A targets. Furthermore, a Mendelian randomization study was conducted based on genome-wide association studies to assess the causative influence of MMP9 on ischemic stroke (IS), which is not only a severe cerebrovascular event but also a common complication of SLE. Results: Twelve m6A regulatory genes was identified, demonstrating statistical significance ( Conclusion: Our study identified twelve m6A regulators, shedding light on the molecular mechanisms underlying SLE risk genes. Importantly, our analysis established a causal relationship between MMP9, a key m6A-related gene, and ischemic stroke, a common complication of SLE, thereby providing critical insights for presymptomatic diagnostic approaches.
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