The immune microenvironment related biomarker CCL18 for patients with gout by comprehensive analysis

Mingchao Zhang; Zhenming Lin; Wenbin Liu

doi:10.1016/j.compbiolchem.2024.108334

The immune microenvironment related biomarker CCL18 for patients with gout by comprehensive analysis

Comput Biol Chem. 2024 Dec 28:115:108334. doi: 10.1016/j.compbiolchem.2024.108334. Online ahead of print.

Authors

Mingchao Zhang¹, Zhenming Lin², Wenbin Liu³

Affiliations

¹ Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; School of basic medical sciences, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; Department of Internal Iedicine, Hospital of Nanhai Economic Development Zone, Foshan 528222, PR China.
² Yuebei People's Hospital in Shaoguan, Shaoguan 512025, PR China.
³ Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, Guangdong Pharmaceutical University, Guangzhou 510006, PR China. Electronic address: 408011126@gdpu.edu.cn.

PMID: 39752850
DOI: 10.1016/j.compbiolchem.2024.108334

Abstract

In the present study, we uncovered and validated potential biomarkers related to gout, characterized by the accumulation of sodium urate crystals in different joint and non-joint structures. The data set GSE160170 was obtained from the GEO database. We conducted differential gene expression analysis, GO enrichment assessment, and KEGG pathway analysis to understand the underlying processes. The overlap of 66 methodologies was visualized through UpSetR (v1.3.3). We used Cytoscape's cytoHubba to detect pivotal genes and mapped out protein-protein interaction (PPI) networks. The overlapping targets among upregulated, downregulated, and key genes were depicted using a Venn diagram. CIBERSORT was employed to ascertain the composition of 22 immune cell types in tissue samples. Subsequently, CCL18 levels in serum samples were quantified using enzyme-linked immunosorbent assay (ELISA) and served as a biomarker evaluation metric. The DEG analysis revealed 1000 genes with varied expression (with an even split of 500 upregulated and 500 downregulated genes) when contrasting gout patients with healthy counterparts. The GO enrichment findings revealed a predominant association with small molecule degradation, positive regulatory catabolic mechanism, organelle division, signal transduction, and axon formation. KEGG assay associated the DEGs predominantly with conditions such as systemic lupus erythematosus, pathways such as tumor necrosis factor (TNF) signaling, as well as alcohol dependency and necroptosis. Intersections were visualized using UpSetR, resulting in the identification of 20 hub genes. A Venn representation highlighted five upregulated genes and three downregulated genes. CIBERSORT analysis revealed a noticeable increase in the number of gamma delta T cells and regulatory T cells. The PPI network analysis revealed CC Chemokine ligand 18 (CCL18) as a critical gene. Gout-afflicted samples exhibited a heightened CCL18 expression compared to healthy ones (P < 0.01). Altogether, CCL18 is a promising biomarker for patients with gout and is suitable for predicting of gout.

Keywords: Biomarker; CCL18; Comprehensive analysis; Differentially expressed genes; Gout.