The imbalance in macrophage phenotype transition is a central mechanism driving chronic inflammation in diabetic kidney disease (DKD). Macrophages can polarize toward the M2 phenotype via efferocytosis, exerting anti-inflammatory and pro-resolving effects. However, the identification and functional validation of regulatory genes governing M2 macrophage and efferocytosis in DKD remain to be thoroughly explored.

Differentially expressed genes were obtained based on GSE96804 and GSE30122 data sets. Based on efferocytosis-related genes (ERGs) and M2 polarization-related genes (MRGs), ERG and MRG scores were computed in the GSE96804 dataset. WGCNA was carried out to identify critical module genes. Finally, macrophage-efferocytosis-related DEGs (MEDEGs) were identified. Further, machine learning (ML)—support vector machine (SVM), BORUTA, and lasso regression—were employed to identify hub genes and build Nomogram predictive model. Additionally, hub genes were confirmed through animal experiments.

Differentially expressed genes were obtained based on GSE96804 and GSE30122 data sets. Based on efferocytosis-related genes (ERGs) and M2 polarization-related genes (MRGs), ERG and MRG scores were computed in the GSE96804 dataset. WGCNA was carried out to identify critical module genes. Finally, macrophage-efferocytosis-related DEGs (MEDEGs) were identified. Further, machine learning (ML)—support vector machine (SVM), BORUTA, and lasso regression—were employed to identify hub genes and build Nomogram predictive model. Additionally, hub genes were confirmed through animal experiments.

This study uncovered 3 hub genes—MCUR1, CYP27B1, and G6PC—linked to M2 polarization, efferocytosis, and DKD. These genes may contribute to DKD pathogenesis, providing novel targets for early diagnosis and therapeutic interventions in DKD.