ArticleFrontiers in immunology2024
Identification of potential biomarkers of gout through weighted gene correlation network analysis.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- TFAP2A transcriptionally regulates B3GALT2 to affect gouty arthritis progression through pyroptosis: a study based on machine learning and multi-omics integration analysis.Journal of orthopaedic surgery and research · 2026Article
- From uric acid to tophi: multistage molecular and cellular mechanisms of tophi formation.Frontiers in immunology · 2026Review
- GDF-15 Levels in Gouty Arthritis and Correlations with Decreasing Renal Function: A Clinical Study.Biomedicines · 2025Article
- Article
- Purine metabolism-associated key genes depict the immune landscape in gout patients.Discover oncology · 2025Article
- Integrative bioinformatics analysis and experimental validation reveals key genes and regulatory mechanisms in the development of gout.Frontiers in genetics · 2025Article
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Authors and funding
8 authors.
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Abstract
Background: Although hyperuricemia is not always associated with acute gouty arthritis, uric acid is a significant risk factor for gout. Therefore, we investigated the specific mechanism of uric acid activity. Methods: Using the gout-associated transcriptome dataset GSE160170, we conducted differential expression analysis to identify differentially expressed genes (DEGs). Moreover, we discovered highly linked gene modules using weighted gene coexpression network analysis (WGCNA) and evaluated their intersection. Subsequently, we screened for relevant biomarkers using the cytoHubba and Mcode algorithms in the STRING database, investigated their connection to immune cells and constructed a competitive endogenous RNA (ceRNA) network to identify upstream miRNAs and lncRNAs. We also collected PBMCs from acute gouty arthritis patients and healthy individuals and constructed a THP-1 cell gout inflammatory model, RT-qPCR and western blotting (WB) were used to detect the expression of C-X-C motif ligand 8 (CXCL8), C-X-C motif ligand 2 (CXCL2), and C-X-C motif ligand 1 (CXCL1). Finally, we predicted relevant drug targets through hub genes, hoping to find better treatments. Results: According to differential expression analysis, there were 76 upregulated and 28 downregulated mRNAs in GSE160170. Additionally, WGCNA showed that the turquoise module was most strongly correlated with primary gout; 86 hub genes were eventually obtained upon intersection. IL1β, IL6, CXCL8, CXCL1, and CXCL2 are the principal hub genes of the protein-protein interaction (PPI) network. Using RT-qPCR and WB, we found that there were significant differences in the expression levels of CXCL8, CXCL1, and CXCL2 between the gouty group and the healthy group, and we also predicted 10 chemicals related to these proteins. Conclusion: In this study, we screened and validated essential genes using a variety of bioinformatics tools to generate novel ideas for the diagnosis and treatment of gout.
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