Abstract
BACKGROUND: Diabetic retinopathy (DR) can significantly impair vision and lead to blindness. Vitamin D (VD) has been shown to enhance the production of anti-inflammatory factors, alleviating the effects of hyperglycemia. However, downstream genes and molecular networks associated with VD signaling in DR remain unidentified. This study aimed to employ a systems biology approach to nominate high-priority candidate genes and cellular contexts as a hypothesis-generating effort to facilitate future functional studies on the role of VD in DR. METHODS: DR-related datasets were obtained from public databases to identify differentially expressed genes (DEGs). Seven canonical VD metabolism-related genes (VDRGs) were subjected to weighted gene co-expression network analysis (WGCNA) to identify VD signaling-associated model genes. Candidate genes were selected based on the intersection of DEGs and model genes. "Boruta" and support vector machine-recursive feature elimination (SVM-RFE), along with expression validation, were used to screen for biomarkers. Further analyses included immune infiltration, gene set enrichment analysis (GSEA), regulatory network construction, and drug prediction. Single-cell RNA sequencing (scRNA-seq) was utilized to assess cellular heterogeneity, identifying distinct cell clusters and key cells based on gene expression profiles. Cell-cell communication within immune cells was also examined. Biomarker expression levels in clinical samples were validated through real-time reverse transcription polymerase chain reaction (RT-qPCR). RESULTS: The biomarkers SLC36A1 and RAB23 were identified as VD signaling-associated downstream candidates and validated. GSEA revealed their primary association with glucose metabolism. B cells and CD4 T cells were identified as differentially expressed immune cells. Both biomarkers were regulated by a competing endogenous RNA (ceRNA) network, and the drug "methyl methanesulfonate" targeted both biomarkers simultaneously. Single-cell analysis identified 11 distinct cell types, including classical monocytes, B cells, and T cells. B cells and classical monocytes were identified as key cells due to the differential expression of biomarkers. The cell-cell communication network highlighted interactions, particularly between classical monocytes, B cells, and T cells. The differentiation of key cells and the stage of biomarker expression were also uncovered. RT-qPCR analysis revealed a significant upregulation of SLC36A1 and RAB23 in the DR group compared to controls (F = 5.184 p = 0.027 < 0.05; F = 4.147 p = 0.047 < 0.05). CONCLUSION: SLC36A1 and RAB23 were identified as VD signaling-associated downstream biomarkers in DR, providing a framework for exploring the potential link between VD signaling and DR pathogenesis through these candidate genes.