Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the "AUCell" function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using "CellChat." The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms. It shows the work flow of the study. The study comprised four sections. In the first section, the scRNA-seq data (GSE127235) from DKD were analyzed. The second section involved cell communication network analysis and AUCell scoring of cell clusters in DKD. The third section utilized bulk RNA-seq data (GSE 96804, GSE104948, GSE30122, and GSE30528) to validate and screen macrophage-specific IRGs and estimate immune cell infiltration. Finally, the fourth part involved in vivo experiments (RT-qPCR, western blot, and immunohistochemistry) to validate the expression of hub genes. Abbreviations: UMAP, uniform manifold approximation and projection; DKD, diabetic kidney disease; RNA-seq, RNA sequencing; DEGs, differentially expressed genes; ROC, receiver operating characteristic.© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.