BACKGROUND Diabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology. METHODS The GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls. RESULTS In total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people. CONCLUSION Immune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.

中文翻译：

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基于机器学习算法识别糖尿病肾病关键免疫相关基因及免疫浸润。

背景技术糖尿病肾病(DN)是糖尿病的并发症。本研究旨在鉴定 DN 的潜在诊断标志物，并探讨免疫细胞浸润在该病理学中的意义。方法 GSE30528、GSE96804 和 GSE1009 数据集是从 Gene Expression Omnibus 数据库下载的。通过合并 GSE30528 和 GSE96804 数据集来鉴定差异表达基因 (DEG)。对 DEG 进行了富集分析。采用LASSO回归模型、支持向量机递归特征消除分析和随机森林分析方法来识别候选生物标志物。CIBERSORT 算法用于比较 DN 和正常对照之间的免疫浸润。结果总共获得115个DEG。富集分析表明DEGs在免疫和炎症反应中表现突出。DEGs与肾脏疾病、泌尿系统疾病、肾癌等密切相关。CXCR2、DUSP1、LPL被认为是DN的诊断标志物。免疫细胞浸润分析表明，DN患者记忆B细胞、γδT细胞、M1巨噬细胞、M2巨噬细胞等细胞比例高于正常人。结论免疫细胞浸润对于DN的发生具有重要意义。CXCR2、DUSP1和LPL可能成为DN的新型诊断标志物。免疫细胞浸润分析表明，DN患者记忆B细胞、γδT细胞、M1巨噬细胞、M2巨噬细胞等细胞比例高于正常人。结论免疫细胞浸润对于DN的发生具有重要意义。CXCR2、DUSP1和LPL可能成为DN的新型诊断标志物。免疫细胞浸润分析表明，DN患者记忆B细胞、γδT细胞、M1巨噬细胞、M2巨噬细胞等细胞比例高于正常人。结论免疫细胞浸润对于DN的发生具有重要意义。CXCR2、DUSP1和LPL可能成为DN的新型诊断标志物。