Identification of Inflammation-Related Biomarker Pro-ADM for Male Patients With Gout by Comprehensive Analysis - Qiu, Kangli; Zeng, Tianshu; Liao, Yunfei; Min, Jie; Zhang, Nan; Peng, Miaomiao; Kong, Wen; Chen, Lu-lu (Frontiers in Immunology, 2022)
Objective. Gout is a local inflammatory disease caused by the deposition of monosodium urate (MSU) crystals in joints or adjacent tissues. When some gout occurs without hyperuricemia, or its clinical symptoms and signs are not typical, the diagnosis of gout will be delayed, so there is an urgent need to find a new biomarker to predict and diagnose of gout flare. Our research attempts to find the key genes and potential molecular mechanisms of gout through bioinformatics analysis, and collected general data and blood biochemical samples of patients with gout and healthy, then analyzed and compared the expression of factors regulated by key genes. Method. GSE160170 were downloaded from GEO database for analysis. The data were normalized to identify the differentially expressed genes (DEGs), then GO and KEGG enrichment analysis were applied. Protein-protein interaction (PPI) networks and hub genes between DEGs were identified. Then collect general information and blood samples from male patients with acute gout, hyperuricemia and healthy. ELISA method was used to detect pro-ADM levels of different groups, and the data was input into SPSS statistical software for analysis. Result. We identified 266 DEGs (179 up-regulated and 87 down-regulated) between gout patients and healthy controls. GO analysis results show that DEGs are mostly enriched in inflammatory response, growth factor activity, cytokine activity, chemokine activity, S100 protein binding and CXCR chemokine receptor binding. KEGG pathway analysis showed that DEGs are mainly related to Chemokine signaling pathway and Cytokine-cytokine receptor interaction. ADM, CXCR1, CXCR6, CXCL3, CCL3, CCL18, CCL3L3, CCL4L1, CD69, CD83, AREG, EREG, B7RP1, HBEGF, NAMPT and S100B are the most important hub genes in the PPI network. We found that the expression of pro-ADM in the gout group and hyperuricemia group was higher than that in the healthy group, and the difference was statistically significant. Conclusion. In this study, a series of bioinformatics analyses were performed on DEGs to identify key genes and pathways related to gout. Through clinical verification, we found that pro-ADM can be used as an inflammation-related biomarker for acute attacks of gout, providing new ideas for the diagnosis and treatment of gout.