Fu Shaojie, Cheng Yanli, Wang Xueyao, Huang Jingda, Su Sensen, Wu Hao, Yu Jinyu, Xu Zhonggao
Department of Nephrology, The First Hospital of Jilin University, Changchun, China.
Department of Urology, The First Hospital of Jilin University, Changchun, China.
Front Med (Lausanne). 2022 Sep 29;9:918657. doi: 10.3389/fmed.2022.918657. eCollection 2022.
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.
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.
A total of 95 DEGs were identified. Using three machine learning algorithms, and were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of and 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.
and are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.
糖尿病肾病（DKD）是全球慢性肾脏病和终末期肾病的主要病因。早期诊断对于预防其进展至关重要。本研究的目的是识别DKD的潜在诊断生物标志物，阐明与这些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。
从基因表达综合数据库下载DKD患者和对照样本的基因表达谱（GSE30528、GSE96804和GSE99339）作为训练集，下载基因表达谱（GSE47185和GSE30122）作为验证集。使用训练集识别差异表达基因（DEG），并进行功能相关性分析。采用最小绝对收缩和选择算子（LASSO）、支持向量机递归特征消除（SVM-RFE）和随机森林（RF）来识别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效能，分别为训练集和验证集绘制受试者工作特征（ROC）曲线，并在DKD和对照肾组织中对生物标志物进行免疫组织化学（IHC）染色。此外，采用CIBERSORT、XCELL和TIMER算法评估DKD中免疫细胞的浸润情况，并研究生物标志物与浸润免疫细胞之间的关系。
共识别出95个DEG。使用三种机器学习算法，DUSP1和PRKAR2B被识别为诊断DKD的潜在生物标志物基因。在训练集的ROC分析中（分别为0.945和0.932）以及验证集的ROC分析中（分别为0.789和0.709），通过曲线下面积评估了DUSP1和PRKAR2B的诊断效能。IHC染色表明，与正常相比，DKD患者中DUSP1和PRKAR2B的表达水平显著降低。免疫细胞浸润分析表明，B记忆细胞、γδT细胞、巨噬细胞和中性粒细胞可能参与DKD的发生发展。此外，两个候选基因均在不同程度上与这些免疫细胞亚型相关。
DUSP1和PRKAR2B是DKD的潜在诊断标志物，并与免疫细胞浸润密切相关。
