Insight into dysregulated VEGF-related genes in diabetic retinopathy through bioinformatic analyses.

Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus. VEGF plays a pivotal role in the pathogenesis of DR. To characterize the VEGF-related genes in DR patients, the RNAseq dataset of DR and normal control were downloaded from the GEO database and analyzed using R package limma. The differentially expressed VEGFGs between DR and NC were identified, and their expression levels were verified through qRT-PCR and Western blotting. Enrichment analyses were performed to understand the key functions and involved pathways of DE-VEGFGs. A two-sample MR analysis was carried out to study the causal link between prostate cancer and DR. Next, we built a nomogram model to predict the risk of DR using the expression level of DE-VEGFGs. Additionally, we estimated the immune cell infiltration between clusters and calculated the correlation between DE-VEGFGs expression and immune cell infiltration in DR. The DGIdb database was used to identify potential target drug for DE-VEGFGs. Finally, we constructed a ceRNA regulation network with predictions from miRNA-mRNA interaction databases and miRNA-lncRNA interaction database. We identified six DE-VEGFGs that are involved in the regulation of the VEGF pathway. The two-sample MR analysis revealed a positive correlation between prostate cancer and the risk of DR. The nomogram which uses the DE-VEGFGs expression to predict the DR risk shows good performance based on the calibration curve and AUC value. Monocytes and T cells CD4 memory activated show different expression between DR and NC; meanwhile, these cell types were correlated with DE-VEGFGs. The drug-gene interaction network provides candidates for DR treatment, and the ceRNA regulation network suggests a potential biomarker for DR. Our study identified dysregulated VEGF-related genes in DR and emphasized their significance in the pathogenesis of DR. Additionally, our findings offer insights into their potential clinical predictive value, immune implications, targeting drug candidates, and regulatory network dynamics.