Multiomics Integration Analysis Reveals the Regulatory Mechanisms of Efferocytosis in Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a prevalent complication in individuals with diabetes. Efferocytosis plays a pivotal role in chronic diseases; however, the precise mechanisms involved in DKD are still not fully understood. DKD-related datasets were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened. These DEGs subsequently intersected with efferocytosis-related genes (ERGs) to produce DKD efferocytosis‒related genes (DKD-ERGs). Potential hub genes were subsequently identified using protein‒protein interaction (PPI) network analysis in combination with machine learning (LASSO regression, Boruta algorithm, and random forest algorithm). Next, we employed transcriptomics, proteomics, and metabolomics analyses of DKD animal models, followed by validation with serum samples from patients with DKD. A nomogram was developed using hub genes to evaluate its predictive accuracy. Consensus clustering was utilized to categorize DKD patients and conduct immune infiltration analysis. A total of 15 DKD-related ERGs were identified. ANXA1, CASP3, IL33, and C3 were identified as potential hub genes. First, validation was performed using the GEO and Nephroseq databases. The hub genes were subsequently validated from multiple perspectives, including transcriptomics, metabolomics, and proteomics of DKD animal models, as well as serological analysis of DKD patients. A risk score model incorporating these 4 hub genes effectively predicted both the onset and progression of DKD. On the basis of these hub genes, DKD patients were classified into Cluster 1 and Cluster 2, with distinct subtypes and immune infiltration correlating with disease stages. This study reveals the potential diagnostic value of ERGs (ANXA1, CASP3, IL33, and C3) in DKD through multidimensional analysis. These genes may serve as promising biomarkers and therapeutic targets for DKD.