ArticleFrontiers in immunology2024
Exploring the transcriptomic landscape of moyamoya disease and systemic lupus erythematosus: insights into crosstalk genes and immune relationships.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Lymphocyte activation gene 3 in autoimmune disease: from molecular mechanisms to clinical applications.Frontiers in immunology · 2026Review
- Identification of Proteomic Biomarkers and Therapeutic Targets for Vitiligo Using a Two-Sample Proteome-Wide Mendelian Randomization Approach.Journal of cosmetic dermatology · 2025Article
- Circulating T cell atlas in Moyamoya disease: insights into immunopathogenesis of cerebrovascular disorders.Journal of neuroinflammation · 2025Article
- Recent Advances in Genetics of Moyamoya Disease: Insights into the Different Pathogenic Pathways.International journal of molecular sciences · 2025Review
- Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches.Discover oncology · 2025Article
- Bioinformatics analysis of FCER1A as a key immune marker in dilated cardiomyopathy and systemic lupus erythematosus.American journal of clinical and experimental immunology · 2025Article
- The joint role of the immune microenvironment and NFrontiers in genetics · 2025Article
- Downregulation of IL-20RA in Cerebrospinal Fluid Associated with the Risk of Moyamoya Disease: A Molecular Signatures Analysis with an Inflammation Proteomics Landscape.Journal of inflammation research · 2025Article
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Systemic Lupus Erythematosus (SLE) is acknowledged for its significant influence on systemic health. This study sought to explore potential crosstalk genes, pathways, and immune cells in the relationship between SLE and moyamoya disease (MMD). Methods: We obtained data on SLE and MMD from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify common genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these shared genes. Hub genes were further selected through the least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was generated based on the results of this selection. Finally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess the infiltration levels of 28 immune cells in the expression profile and their association with the identified hub genes. Results: By intersecting the important module genes from WGCNA with the DEGs, the study highlighted Conclusion: This study is pioneering in its use of bioinformatics tools to explore the close genetic relationship between MMD and SLE. The genes
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Registered trials
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