Machine Learning Identification and Animal Model Validation of Key Genes for Lipid Metabolism in Diabetic Nephropathy

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 differential 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 forest 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,ALDH7A1,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.