The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, People's Republic of China.
Department of Endocrinology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, People's Republic of China.
Medicine (Baltimore). 2023 Sep 1;102(35):e34992. doi: 10.1097/MD.0000000000034992.
The leading cause of end-stage renal disease is diabetic nephropathy (DN). A key factor in DN is immune cell infiltration (ICI). It has been shown that immune-related genes play a significant role in inflammation and immune cell recruitment. However, neither the underlying mechanisms nor immune-related biomarkers have been identified in DNs. Using bioinformatics, this study investigated biomarkers associated with immunity in DN.
Using bioinformatic methods, this study aimed to identify biomarkers and immune infiltration associated with DN. Gene expression profiles (GSE30528, GSE47183, and GSE104948) were selected from the Gene Expression Omnibus database. First, we identified 23 differentially expressed immune-related genes and 7 signature genes, LYZ, CCL5, ALB, IGF1, CXCL2, NR4A2, and RBP4. Subsequently, protein-protein interaction networks were created, and functional enrichment analysis and genome enrichment analysis were performed using the gene ontology and Kyoto Encyclopedia of Genes and Genome databases. In the R software, the ConsensusClusterPlus package identified 2 different immune modes (cluster A and cluster B) following the consistent clustering method. The infiltration of immune cells between the 2 clusters was analyzed by applying the CIBERSORT method. And preliminarily verified the characteristic genes through in vitro experiments.
In this study, the samples of diabetes nephropathy were classified based on immune related genes, and the Hub genes LYZ, CCL5, ALB, IGF1, CXCL2, NR4A2 and RBP4 related to immune infiltration of diabetes nephropathy were obtained through the analysis of gene expression differences between different subtypes.
This study was based on bioinformatics technology to analyze the biomarkers of immune related genes in diabetes nephropathy. To analyze the pathogenesis of diabetes nephropathy at the RNA level, and ultimately provide guidance for disease diagnosis, treatment, and prognosis.
终末期肾病的主要病因是糖尿病肾病（DN）。DN 的一个关键因素是免疫细胞浸润（ICI）。已经表明，免疫相关基因在炎症和免疫细胞募集中发挥重要作用。然而，DN 中尚未确定潜在机制或免疫相关生物标志物。本研究使用生物信息学方法研究与 DN 相关的免疫相关生物标志物。
本研究使用生物信息学方法，旨在鉴定与 DN 相关的免疫生物标志物和免疫浸润。从基因表达综合数据库中选择基因表达谱（GSE30528、GSE47183 和 GSE104948）。首先，我们鉴定了 23 个差异表达的免疫相关基因和 7 个特征基因（LYZ、CCL5、ALB、IGF1、CXCL2、NR4A2 和 RBP4）。随后，构建蛋白质-蛋白质相互作用网络，并使用基因本体和京都基因与基因组百科全书数据库进行功能富集分析和基因组富集分析。在 R 软件中，ConsensusClusterPlus 包使用一致聚类方法在 CIBERSORT 方法分析 2 个簇之间免疫细胞的浸润后，识别出 2 种不同的免疫模式（簇 A 和簇 B）。通过体外实验初步验证了特征基因。
本研究基于免疫相关基因对糖尿病肾病样本进行分类，通过不同亚型间基因表达差异分析，获得与糖尿病肾病免疫浸润相关的 LYZ、CCL5、ALB、IGF1、CXCL2、NR4A2 和 RBP4 等 Hub 基因。
本研究基于生物信息学技术分析糖尿病肾病免疫相关基因的生物标志物，从 RNA 水平分析糖尿病肾病的发病机制，为疾病的诊断、治疗和预后提供指导。
