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通过加权基因相关网络分析鉴定痛风的潜在生物标志物。

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

机构信息

Department of Endocrinology, First Affiliated Hospital, Guangxi Medical University, Nanning, China.

Department of Geriatric Endocrinology and Metabolism, First Affiliated Hospital, Guangxi Medical University, Nanning, China.

出版信息

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.

摘要

背景

尽管高尿酸血症并不总是与急性痛风性关节炎相关,但尿酸是痛风的一个重要危险因素。因此,我们研究了尿酸活性的具体机制。

方法

使用与痛风相关的转录组数据集 GSE160170,我们进行了差异表达分析,以鉴定差异表达基因(DEGs)。此外,我们使用加权基因共表达网络分析(WGCNA)发现了高度关联的基因模块,并评估了它们的交集。随后,我们使用 STRING 数据库中的 cytoHubba 和 Mcode 算法筛选相关生物标志物,研究它们与免疫细胞的联系,并构建竞争内源性 RNA(ceRNA)网络以鉴定上游 miRNA 和 lncRNA。我们还从急性痛风性关节炎患者和健康个体中收集 PBMCs,并构建了 THP-1 细胞痛风炎症模型,使用 RT-qPCR 和 Western blot(WB)检测 C-X-C 基序配体 8(CXCL8)、C-X-C 基序配体 2(CXCL2)和 C-X-C 基序配体 1(CXCL1)的表达。最后,我们通过枢纽基因预测相关药物靶点,希望找到更好的治疗方法。

结果

根据差异表达分析,在 GSE160170 中有 76 个上调和 28 个下调的 mRNAs。此外,WGCNA 显示,绿松石模块与原发性痛风的相关性最强;最终通过交集获得了 86 个枢纽基因。IL1β、IL6、CXCL8、CXCL1 和 CXCL2 是蛋白质-蛋白质相互作用(PPI)网络的主要枢纽基因。使用 RT-qPCR 和 WB,我们发现痛风组和健康组之间 CXCL8、CXCL1 和 CXCL2 的表达水平存在显著差异,我们还预测了与这些蛋白质相关的 10 种化学物质。

结论

在这项研究中,我们使用多种生物信息学工具筛选和验证了关键基因,为痛风的诊断和治疗提供了新的思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70b8/11056514/8f6ab692c87b/fimmu-15-1367019-g001.jpg

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