ArticleFrontiers in endocrinology2025
Immune regulatory mechanisms of M2 macrophage polarization and efferocytosis in diabetic kidney disease: an integrated screening study with therapeutic implications.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Combined Mesenchymal Stem Cells and Low-Energy Shock Wave Therapy Significantly Reduced Inflammation, Oxidative Stress and Preserved Podocytes in Diabetic Nephropathy.Life (Basel, Switzerland) · 2026Article
- Myeloid myeloid PRDM16 restrains macrophage inflammatory remodeling and renal fibrosis in diabetic kidney disease by limiting PI3K/AKT activation.Acta diabetologica · 2026Article
- Directing Neutrophil Fate via Sensory-Immune Interactions Accelerates Diabetic Bone Healing.Research (Washington, D.C.) · 2026Article
- Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine.Frontiers in genetics · 2026Review
- Unveiling the immune microenvironment in diabetic nephropathy: from mechanisms to therapeutics.Frontiers in immunology · 2026Review
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Authors and funding
9 authors.
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Abstract
Background: 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. Methods: 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. Weighted gene co-expression network analysis (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. Results: A total of 35 MEDEGs were identified. ML recognized 3 hub genes-MCUR1, CYP27B1, and G6PC. Hub genes were notably downregulated in DKD group and exhibited high predictive ability. Furthermore, the Nomogram model based on key genes has shown potential in predicting DKD. The findings were further validated through transcriptome sequencing of DKD model. Conclusion: 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.
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