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Objective <p>Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes, contributing to vision impairment and related retinal diseases. Growing evidence indicates that cellular senescence (CS) under high-glucose conditions plays a role in the pathogenesis of DR. This study aims to identify key biomarkers of CS in DR by integrating transcriptomics, single-cell sequencing data, and experimental validation, thereby offering insights for understanding the disease mechanism and developing novel therapeutic strategies.</p> Methods <p>DR-related datasets and CS-related genes (CSRGs) were retrieved from the Gene Expression Omnibus (GEO) and CellAge databases. The characteristic gene set for DR-CS was obtained by intersecting differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) results, and CSRGs. Subsequent analyses involved constructing protein-protein interaction (PPI) network, cytoHubba screening, enrichment analysis, and immune infiltration analysis. Machine learning methods were used to identify key biomarkers from the DR-CS characteristic gene set, which were then validated using external datasets. Single-cell sequencing and gene set enrichment analysis (GSEA) were employed to determine the cellular location and biological functions of DR-CS key biomarkers, and animal experiments further validated these biomarkers.</p> Results <p>A total of 67 DR-CS-related characteristic genes were identified. Enrichment analysis highlighted pathways like cellular senescence and the Advanced Glycation Endproducts-Receptor for Advanced Glycation Endproducts (AGE-RAGE) signaling pathway in diabetic complications as being closely related to DR development. A set of 13 characteristic genes was selected through a combination of PPI network and six cytoHubba algorithms. Further analysis using machine learning, expression analysis, and Receiver Operating Characteristic (ROC) analysis revealed MYC and LOX as key biomarkers of DR-CS. The expression characteristics of MYC and LOX in various cells were examined using single-cell RNA sequencing. Animal experiments demonstrated that the expression levels of MYC and LOX in the retina were significantly higher in DR group than in the control group (<i>P</i> &lt; 0.05).</p> Conclusion <p>MYC and LOX were identified as key biomarkers of DR-CS. Thus, investigating these genes may provide new therapeutic targets for DR treatment by targeting cellular senescence.</p>

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Multi-omics and experimental validation studies reveal key biomarkers of cellular senescence in diabetic retinopathy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月12日

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  • Jinju Li,
  • Hao Yang,
  • Yixuan Lin,
  • Zhaohui Fang

摘要

Objective

Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes, contributing to vision impairment and related retinal diseases. Growing evidence indicates that cellular senescence (CS) under high-glucose conditions plays a role in the pathogenesis of DR. This study aims to identify key biomarkers of CS in DR by integrating transcriptomics, single-cell sequencing data, and experimental validation, thereby offering insights for understanding the disease mechanism and developing novel therapeutic strategies.

Methods

DR-related datasets and CS-related genes (CSRGs) were retrieved from the Gene Expression Omnibus (GEO) and CellAge databases. The characteristic gene set for DR-CS was obtained by intersecting differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) results, and CSRGs. Subsequent analyses involved constructing protein-protein interaction (PPI) network, cytoHubba screening, enrichment analysis, and immune infiltration analysis. Machine learning methods were used to identify key biomarkers from the DR-CS characteristic gene set, which were then validated using external datasets. Single-cell sequencing and gene set enrichment analysis (GSEA) were employed to determine the cellular location and biological functions of DR-CS key biomarkers, and animal experiments further validated these biomarkers.

Results

A total of 67 DR-CS-related characteristic genes were identified. Enrichment analysis highlighted pathways like cellular senescence and the Advanced Glycation Endproducts-Receptor for Advanced Glycation Endproducts (AGE-RAGE) signaling pathway in diabetic complications as being closely related to DR development. A set of 13 characteristic genes was selected through a combination of PPI network and six cytoHubba algorithms. Further analysis using machine learning, expression analysis, and Receiver Operating Characteristic (ROC) analysis revealed MYC and LOX as key biomarkers of DR-CS. The expression characteristics of MYC and LOX in various cells were examined using single-cell RNA sequencing. Animal experiments demonstrated that the expression levels of MYC and LOX in the retina were significantly higher in DR group than in the control group (P < 0.05).

Conclusion

MYC and LOX were identified as key biomarkers of DR-CS. Thus, investigating these genes may provide new therapeutic targets for DR treatment by targeting cellular senescence.