背景:糖尿病肾病(DN)是糖尿病的主要微血管并发症,已成为全球终末期肾病的主要原因。相当多的DN患者由于无法早期诊断疾病而经历了不可逆的终末期肾病进展。因此,确定了有助于早期诊断和治疗的可靠生物标志物.免疫细胞向肾脏的迁移被认为是DN相关血管损伤进展的关键步骤。因此,在此过程中发现标志物可能更有助于DN的早期诊断和进展预测。
方法:使用搜索词“糖尿病肾病”从GEO数据库检索基因芯片数据。“limma”软件包用于鉴定DN和对照样品之间的差异表达基因(DEGs)。对从分子特征数据库(MSigDB.R包“WGCNA”用于鉴定与DN肾小管间质损伤相关的基因模块,并与免疫相关的DEGs杂交以鉴定靶基因。使用R中的“ClusterProfiler”软件包对差异表达基因进行了基因本体论(GO)富集分析和京都基因和基因组百科全书(KEGG)途径分析。三种方法,最小绝对收缩和选择运算符(LASSO),支持向量机递归特征消除(SVM-RFE)和随机森林(RF),用于选择免疫相关的生物标志物进行诊断。我们从Nephroseq数据库中检索了肾小管间质数据集,以构建外部验证数据集。使用“ConsensusClusterPlus”R软件包对免疫相关生物标志物的表达水平进行无监督聚类分析。收集2021年9月至2023年3月在北京中医药大学东直门医院就诊的患者尿液,和ELISA检测尿液中免疫相关生物标志物的mRNA表达水平。采用Pearson相关分析检测免疫相关生物标志物表达对DN患者肾功能的影响。
结果:来自GEO数据库的四个微阵列数据集包括在分析中:GSE30122、GSE47185、GSE99340和GSE104954。这些数据集包括63名DN患者和55名健康对照。在数据集中共检测到9415个基因。我们发现了153个差异表达的免疫相关基因,其中112个基因上调,41个基因下调,并鉴定出119个重叠基因。GO分析表明,它们参与各种生物过程,包括白细胞介导的免疫。KEGG分析表明,这些靶基因主要参与金黄色葡萄球菌感染中吞噬体的形成。在这119个重叠基因中,机器学习结果识别AGR2、CCR2、CEBPD、CISH,CX3CR1、DEFB1和FSTL1是潜在的肾小管间质免疫相关生物标志物。外部验证表明,上述标记物在区分DN患者与健康对照方面显示出诊断功效。临床研究表明,DN患者尿样中AGR2、CX3CR1和FSTL1的表达与GFR呈负相关,尿样中CX3CR1和FSTL1的表达与血清肌酐呈正相关,DEFB1在DN尿样中的表达与血肌酐呈负相关。此外,CX3CR1在DN尿样中的表达与蛋白尿呈正相关,而DN尿样中DEFB1的表达与蛋白尿呈负相关。最后,根据蛋白尿的水平,DN患者分为肾病性蛋白尿组(n=24)和肾下蛋白尿组。两组尿AGR2、CCR2、DEFB1经t检验差异有统计学意义(P<0.05)。
结论:我们的研究为免疫相关生物标志物在DN肾小管间质损伤中的作用提供了新的见解,并为DN患者的早期诊断和治疗提供了潜在的靶点。七个不同的基因(AGR2,CCR2,CEBPD,CISH,CX3CR1,DEFB1,FSTL1),作为有前途的敏感生物标志物,可能通过调节免疫炎症反应影响DN的进展。然而,需要进一步全面的研究以充分了解其在DN中的确切分子机制和功能通路。
BACKGROUND: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN.
METHODS: The gene chip data were retrieved from the GEO database using the search term \' diabetic nephropathy \'. The \' limma \' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package \'WGCNA\' was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the \'ClusterProfiler\' software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the \'ConsensusClusterPlus \'R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients.
RESULTS: Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05).
CONCLUSIONS: Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.