首页|期刊导航|江苏大学学报(医学版)|基于生物信息学分析鉴定FMOD作为糖尿病肾病免疫浸润相关标志物

基于生物信息学分析鉴定FMOD作为糖尿病肾病免疫浸润相关标志物OA

Identification of FMOD as an immune infiltration-related biomarker in diabetic kidney disease based on bioinformatics analysis

中文摘要英文摘要

目的:基于生物信息学筛选糖尿病肾病(diabetic kidney disease,DKD)免疫浸润相关基因,验证纤调蛋白(fibromodulin,FMOD)作为该病的潜在免疫生物标志物.方法:从基因表达综合数据库(GEO)中获取糖尿病肾病肾脏样本数据集 GSE111154(健康对照组 4 例,DKD 组 4 例)和 GSE142025(健康对照组 9 例,DKD 组 28 例).运用Limma R 包进行差异表达基因(differentially expressed genes,DEGs)筛选,通过基因本体论(GO)、京都基因与基因组百科全书(KEGG)及基因集富集分析(GSEA)探究 DEGs 的生物学功能.进一步采用多种机器学习算法和回归模型筛选候选基因,并利用 CIBERSORT 算法评估免疫细胞浸润及其与候选基因的相关性.结果:GSE111154 和 GSE142025数据集中共鉴定出49个 DEGs.DEGs 显著富集于细胞因子-细胞因子受体相互作用、趋化因子信号通路及免疫相关通路.基于机器学习算法和回归模型筛选,确定 FMOD 为潜在生物标志物.CIBERSORT 分析显示,FMOD 与记忆 B细胞(r=0.73,P<0.05)、初始T 细胞(r=0.69,P<0.05)、调节T 细胞(r=0.58,P<0.05)、单核细胞(r=0.64,P<0.05)、M2 型巨噬细胞(r=0.81,P<0.05)、静息肥大细胞(r=0.73,P<0.05)、活化肥大细胞(r=0.61,P<0.05)及嗜酸性粒细胞(r=0.57,P<0.05)呈正相关,与静息树突状细胞呈负相关(r=-0.69,P<0.05).该免疫浸润模式与 DKD 组中的呈高度一致性.进一步研究显示,FMOD 在 DKD 中表达显著上调,且与估算肾小球滤过率(r=-0.75,P<0.01)呈负相关.结论:FMOD 可能通过调控免疫细胞浸润参与 DKD 的发生和发展.

Objective:To screen immune infiltration-related genes in diabetic kidney disease(DKD)based on bioinformatics and validate fibromodulin(FMOD)as a potential immune biomarker for DKD.Methods:The kidney sample datasets pertaining to diabetic nephropathy were sourced from the gene expression omnibus(GEO).Specifically,the datasets included GSE111154,which comprises samples from four healthy controls and four individuals with DKD,as well as GSE142025,which consists of samples from nine healthy controls and twenty-eight DKD patients.The Limma R package was employed to identify differentially expressed genes(DEGs),followed by gene ontology(GO)and kyoto encyclopedia of genes and genomes(KEGG)and gene set enrichment analyses(GSEA)to explore the biological functions of these DEGs.Various machine learning algorithms and regression models were applied to identify candidate genes,and the CIBERSORT algorithm was utilized to assess immune cell infiltration and its correlation with the identified candidate genes.Results:A total of 49 DEGs were identified across the GSE111154 and GSE142025 datasets.The DEGs were significantly enriched in cytokine-cytokine receptor interactions,chemokine signaling pathways,and immune-related pathways.Utilizing machine learning algorithms and regression model assessments,FMOD has been recognized as a potential biomarker.CIBERSORT analysis indicated that the expression of FMOD exhibited a significant positive correlation with memory B cells(r=0.73,P<0.05),naïve T cells(r=0.69,P<0.05),regulatory T cells(r=0.58,P<0.05),M2 macrophages(r=0.81,P<0.05),both resting(r=0.73,P<0.05)and activated mast cells(r=0.61,P<0.05),as well as eosinophils(r=0.57,P<0.05).Conversely,FMOD expression demonstrated a negative correlation with resting dendritic cells(r=-0.69,P<0.05).The immune infiltration pattern was highly consistent with the characteristics of the DKD group.Further examination indicated that FMOD was markedly upregulated in DKD and exhibited a negative correlation with estimated glomerular filtration rate(r=-0.75,P<0.01).Conclusion:FMOD may be involved in the occurrence and progression of DKD by regulating immune infiltration.

孙紫烟;邓霞;袁国跃

江苏大学附属医院内分泌代谢科,江苏 镇江 212001江苏大学附属医院内分泌代谢科,江苏 镇江 212001江苏大学附属医院内分泌代谢科,江苏 镇江 212001

医药卫生

糖尿病肾病免疫细胞生物标志物纤调蛋白免疫细胞浸润

diabetic kidney diseaseimmune cellsbiomarkersfibromodulinimmuneinfiltration

《江苏大学学报(医学版)》 2026 (3)

207-215,9

江苏省社会发展重点研发项目(BE2018692)

10.13312/j.issn.1671-7783.y250023

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