Anhui University of Chinese Medicine, Hefei, China.
Eur Rev Med Pharmacol Sci. 2024 Feb;28(3):863-878. doi: 10.26355/eurrev_202402_35324.
In this study, we investigated the internal relationship between the pathogenesis of diabetic kidney disease (DKD) and abnormal glucose and lipid metabolism to identify potential biomarkers for diagnosis and treatment and investigated the role of the immune microenvironment of glucose and lipid metabolism disorders in the occurrence and progression of DKD.
The chip datasets GSE104948 and GSE96804 from the Gene Expression Common Database (GEO) were merged using the "lima" and "sva" software packages in R Software (4.2.3), and the merged dataset was used as the validation set. The intersection between the differential genes of DKD and the glucose and lipid metabolism genes in the MSigDB database was identified, and a nomogram of the incidence risk of DKD was built using three machine learning methods, namely LASSO regression, support vector machine (SVM), and random forest (RF), to validate the accuracy of the prediction model. Immune scores were conducted using the unsupervised clustering method, and patients were divided into two subgroups. The two subgroups were screened for differential genes for enrichment analysis. The differential genes of patients diagnosed with DKD were clustered into two gene subgroups for co-expression analysis. In this study, we utilized the Cytoscape software to construct a network of interactions among key genes.
Using machine learning, a diagnostic model was developed with G6PC and HSD17B14 as key factors. Enrichment analysis and immune scoring demonstrated that the development of DKD was related to the imbalance in the microenvironment brought about by glucose lipid metabolism disorders.
G6PC and HSD17B14 may be potential biomarkers for DKD, and the established predictive model is more helpful in predicting the incidence of DKD.
本研究旨在探讨糖尿病肾病（DKD）发病机制与糖脂代谢异常的内在关系，寻找潜在的诊断和治疗标志物，并探讨糖脂代谢紊乱所致免疫微环境失衡在 DKD 发生发展中的作用。
使用 R 软件（4.2.3）中的“lima”和“sva”软件包对基因表达公共数据库（GEO）中的芯片数据集 GSE104948 和 GSE96804 进行合并，并将合并后的数据集作为验证集。鉴定 DKD 的差异基因与 MSigDB 数据库中糖脂代谢基因的交集，采用 LASSO 回归、支持向量机（SVM）和随机森林（RF）三种机器学习方法构建 DKD 发病风险的列线图，验证预测模型的准确性。采用无监督聚类方法进行免疫评分，将患者分为两组。对两组患者进行差异基因富集分析。将诊断为 DKD 的患者的差异基因聚类为两个基因亚组进行共表达分析。本研究利用 Cytoscape 软件构建关键基因之间的相互作用网络。
利用机器学习构建了以 G6PC 和 HSD17B14 为关键因素的诊断模型。富集分析和免疫评分表明，DKD 的发生发展与糖脂代谢紊乱导致的微环境失衡有关。
G6PC 和 HSD17B14 可能是 DKD 的潜在生物标志物，建立的预测模型更有助于预测 DKD 的发病。
