Identification of potential biomarkers of gout through weighted gene correlation network analysis

Front Immunol. 2024 Apr 15:15:1367019. doi: 10.3389/fimmu.2024.1367019. eCollection 2024.

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.

Keywords: CXCL1; CXCL2; CXCL8; WGCNA; gout.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Arthritis, Gouty / genetics
  • Biomarkers*
  • Chemokine CXCL1 / genetics
  • Chemokine CXCL2 / genetics
  • Chemokine CXCL2 / metabolism
  • Computational Biology / methods
  • Databases, Genetic
  • Gene Expression Profiling*
  • Gene Expression Regulation
  • Gene Regulatory Networks*
  • Gout* / genetics
  • Humans
  • Interleukin-8 / genetics
  • MicroRNAs / genetics
  • Protein Interaction Maps
  • THP-1 Cells
  • Transcriptome
  • Uric Acid

Substances

  • Biomarkers
  • Chemokine CXCL1
  • CXCL1 protein, human
  • Chemokine CXCL2
  • CXCL2 protein, human
  • CXCL8 protein, human
  • Interleukin-8
  • MicroRNAs
  • Uric Acid

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Science and Technology Plan Project of Qingxiu District, Nanning City [grant no. 2019024] and National Natural Science Foundation of China [grant no.82360179].