Glycosylation gene-based molecular recognition model for diabetic retinopathy

Diabetic retinopathy (DR) represents a significant public health challenge, with the potential to cause blindness and escalate healthcare costs, yet current diagnostic and therapeutic approaches remain insufficient. This study investigated the role of glycosylation-related differentially expressed genes (GRDEGs) in DR to identify novel biomarkers and therapeutic targets. Through analysis of Gene Expression Omnibus (GEO) datasets using differential expression analysis, functional enrichment, and machine learning, we identified 39 GRDEGs-including RPS23, VCAN, and ST8SIA4-that play significant roles in DR pathogenesis. These genes were enriched in biological processes such as wound healing and sphingolipid metabolism, as well as cancer-related pathways. Immune infiltration analysis revealed distinct patterns and correlations between immune cell types and GRDEGs, suggesting immune microenvironment involvement in DR progression. External validation using an independent blood dataset demonstrated moderate discriminatory performance (AUC 0.7-0.9), though this cross-tissue comparison should be interpreted as exploratory evidence of partial gene expression consistency rather than confirmation of biological mechanisms or clinical utility. Given the limited sample size and group imbalances in the discovery cohort, these results constitute proof-of-concept findings requiring validation in larger, balanced populations. Future research should focus on functional validation of identified GRDEGs and integration of the molecular recognition model into clinical workflows to enable proactive DR management.