中国医学科学院学报2025，Vol.47Issue(6)：873-887,15.DOI:10.3881/j.issn.1000-503X.16594
机器学习识别及动物模型验证糖尿病肾病脂质代谢关键基因
Machine Learning Identification and Animal Model Validation of Key Genes for Lipid Metabolism in Diabetic Nephropathy
摘要
Abstract
Objective To identify key genes of lipid metabolism in diabetic nephropathy(DN)through machine learning models and animal model validation.Methods The limma R package was used for dif-ferential gene expression analysis on 69 samples from two transcriptome datasets of the Gene Expression Omnibus and 2 184 differentially expressed genes were identified.Subsequently,we adopted undifferentiated consensus clustering to classify DN samples into two specific subtypes.At the same time,we performed weighted gene co-expression network analysis to mine the gene modules significantly associated with DN.In addition,using least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random for-est machine learning techniques,combined with protein-protein interaction network analysis,we screened out three core genes.Finally,we constructed a mouse model of type 2 diabetes mellitus to verify the effectiveness of the expression of these key genes.Results Three core genes,APOO,ALDH7A1,and ALB,were predicted as potential biomarkers of lipid metabolism in DN,and their expression levels were downregulated in DN.Through experimental validation in a diabetic mouse model,we confirmed the altered expression of APOO,ALDH7A1,and ALB in DN,which supported their potential as diagnostic markers.Conclusions Our findings suggest that APOO,ALDH7 A1,and ALB are new diagnostic markers associated with lipid metabolism in DN,which provides new perspectives for understanding the molecular mechanisms of lipid metabolism in DN.关键词
糖尿病肾病/脂质代谢/机器学习/生物标志物/生物信息学Key words
diabetic nephropathy/lipid metabolism/machine learning/biomarkers/bioinformatics分类
医药卫生引用本文复制引用
LI Yue,ZHANG Yuyu,WAN Xing,NIU Songlin,SHI Honghong,WANG Lihua..机器学习识别及动物模型验证糖尿病肾病脂质代谢关键基因[J].中国医学科学院学报,2025,47(6):873-887,15.基金项目
国家自然科学基金(82170746)、山西省科技厅基础研究项目(202303021222331)和山西医科大学第二医院博士点基金(202301-20) （82170746）
