Abstract
Purpose
Diabetic retinopathy (DR) remains a leading cause of vision loss, with macrophages and glycosylation dysregulation implicated in DR pathogenesis. However, the potential as diagnostic biomarkers has been rarely investigated.
Methods
We integrated single‐cell RNA sequencing (scRNA‐seq) datasets to profile DR cell landscapes. The immune cell heterogeneity was dissected, and glycosylation‐related transcriptional programs were delineated in DR. Machine learning–based diagnostic modeling was also conducted to identify macrophage differentiation–related glycosylation genes (MDRGGs).
Results
Macrophages exhibited elevated abundance in proliferative DR and showed intense interactions with other monocytes. Endothelial cells were subdivided into four subtypes, with Endo_KCNQ3 representing a dominant proliferative and highly glycosylated phenotype. Monocytes were clustered into three subtypes; Mono_RGS1 emerged as a transitional phenotype in the monocyte‐to‐macrophage trajectory. Macro_MIR181A1HG was identified as a proliferative and glycosylation‐active macrophage subset. A total of 100 MDRGGs were identified. Among them, seven hub genes (AKAP13, SRGAP2, AFF1, ARHGAP24, RNF149, PTK2B, ATP1B3) were incorporated into a diagnostic model. The model achieved high predictive accuracy in both training and external validation cohorts (AUC > 0.85) and was further validated via nomogram and decision curve analyses.
Conclusion
Glycosylation is closely associated with macrophage heterogeneity in DR. A seven‐gene MDRGG‐based diagnostic model demonstrated robust diagnostic performance in DR.