Li Yang, Huang ChengCheng, Xie Yuhan, Liu WenBin, Wei MengJuan, Li Shudong, Yang Zhenguo, Wang JingWu, Li Gang
Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, China.
Department of Orthopedic, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, China.
Heliyon. 2024 Oct 30;10(22):e39957. doi: 10.1016/j.heliyon.2024.e39957. eCollection 2024 Nov 30.
Gout is a prevalent form of chronic inflammatory arthritis, and its etiology remains incompletely understood. Ferroptosis is a form of cell death that relies on iron. As of now, the relationship between ferroptosis and gout is not entirely clear. Hence, the primary objective of this study is to employ bioinformatics methods for the analysis and identification of potential genes associated with ferroptosis in the context of gout.
Utilizing both bioinformatics analysis and machine learning algorithms to systematically identify biomarkers for gout. The gout-related dataset (GSE160170) was acquired from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were extracted from the FerrDb database. subsequently, we identified DEGs associated with ferroptosis in the context of gout. Following that, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the DEGs. Subsequently, SVM-RFE analysis and the LASSO regression model were employed for biomarker screening. Additionally, CIBERSORT software was utilized to assess the composition of twenty-two immune cells in gout, and correlation analyses between hub genes and immune cells were conducted.
This study screened a total of twenty-five DEGs related to Ferroptosis in healthy population and gout patient. The KEGG analysis indicates that these DEGs are predominantly enriched in: the AGE-RAGE signaling pathway, nod like receptor signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, etc. The intersection of the top 10 genes identified through PPI network, SVM-RFE analysis, and LASSO regression model resulted in two hub genes, namely JUN and ATF3. Analysis of immunocyte infiltration revealed that JUN exhibited associations with various immune cells, including NK cells resting, Monocytes, Mast cells resting, etc. ATF3, on the other hand, showed associations with immune cells Mast cells resting and Eosinophels.
The outcomes of our study pinpointed JUN and ATF3, genes associated with ferroptosis, as promising biomarkers for both diagnosing and treating gout, providing additional evidence to support the important role of ferroptosis in gout and providing potential therapeutic methods for clinical targeted ferroptosis prevention and treatment of gout.
痛风是一种常见的慢性炎症性关节炎,其病因尚未完全明确。铁死亡是一种依赖铁的细胞死亡形式。目前,铁死亡与痛风之间的关系尚不完全清楚。因此,本研究的主要目的是运用生物信息学方法分析和鉴定痛风背景下与铁死亡相关的潜在基因。
利用生物信息学分析和机器学习算法系统地鉴定痛风的生物标志物。从基因表达综合数据库(GEO)获取痛风相关数据集(GSE160170)。从FerrDb数据库中提取铁死亡相关基因。随后,我们鉴定了痛风背景下与铁死亡相关的差异表达基因(DEGs)。接着,我们对这些DEGs进行了基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路富集分析。随后,采用支持向量机-递归特征消除(SVM-RFE)分析和套索回归模型进行生物标志物筛选。此外,利用CIBERSORT软件评估痛风中22种免疫细胞的组成,并进行枢纽基因与免疫细胞之间的相关性分析。
本研究共筛选出25个健康人群和痛风患者中与铁死亡相关的DEGs。KEGG分析表明,这些DEGs主要富集在:晚期糖基化终末产物受体(AGE-RAGE)信号通路、NOD样受体信号通路、丝裂原活化蛋白激酶(MAPK)信号通路、白细胞介素-17(IL-17)信号通路等。通过蛋白质-蛋白质相互作用(PPI)网络、SVM-RFE分析和套索回归模型确定的前10个基因的交集产生了两个枢纽基因,即JUN和ATF3。免疫细胞浸润分析显示,JUN与多种免疫细胞有关,包括静息自然杀伤(NK)细胞、单核细胞、静息肥大细胞等。另一方面,ATF3与静息肥大细胞和嗜酸性粒细胞等免疫细胞有关。
我们的研究结果确定了与铁死亡相关的基因JUN和ATF3,作为诊断和治疗痛风的有前景的生物标志物,为支持铁死亡在痛风中的重要作用提供了额外证据,并为临床针对性预防和治疗痛风的铁死亡提供了潜在的治疗方法。