未经证实:糖尿病肾病(DKD)是全球慢性肾病和终末期肾病的主要原因。早期诊断对于防止其进展至关重要。这项研究的目的是确定DKD的潜在诊断生物标志物,说明与生物标志物相关的生物学过程,并研究它们与免疫细胞浸润之间的关系。
UNASSIGNED:从DKD和对照获得的样品的基因表达谱(GSE30528,GSE96804和GSE99339)从基因表达综合数据库下载作为训练集,并下载基因表达谱(GSE47185和GSE30122)作为验证集。使用训练集鉴定差异表达基因(DEGs),并进行功能相关分析。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)进行鉴定潜在的诊断生物标志物。为了评估这些潜在生物标志物的诊断功效,分别为训练集和验证集绘制受试者工作特性(ROC)曲线,在DKD和对照肾组织中进行生物标志物的免疫组织化学(IHC)染色。此外,CIBERSORT,采用XCELL和TIMER算法评估DKD中免疫细胞的浸润,并且还研究了生物标志物与浸润免疫细胞之间的关系。
未经授权:总共确认了95个DEG。使用三种机器学习算法,DUSP1和PRKAR2B被鉴定为诊断DKD的潜在生物标志物基因。使用训练集(分别为0.945和0.932)和验证集(分别为0.789和0.709)的ROC分析中的曲线下面积评估DUSP1和PRKAR2B的诊断功效。IHC染色提示DKD患者中DUSP1和PRKAR2B的表达水平明显低于正常水平。免疫细胞浸润分析显示,B记忆细胞,γδT细胞,巨噬细胞,而中性粒细胞可能参与了DKD的发生发展。此外,这两个候选基因在不同程度上与这些免疫细胞亚型相关。
未经证实:DUSP1和PRKAR2B是DKD的潜在诊断标记物,它们与免疫细胞浸润密切相关。
UNASSIGNED: Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.
UNASSIGNED: Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.
UNASSIGNED: A total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.
UNASSIGNED: DUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.