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

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. 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. 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). 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.

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Publication Details

Journal
Diabetology & Metabolic Syndrome
Published
2025-08-12
DOI
https://doi.org/10.1186/s13098-025-01899-y
Citations
1
Primary Topic
Retinal Diseases and Treatments
Type
article
Field-Weighted Citation Impact
1.71

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article

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

Yixuan Lin, Jinju Li, Zhaohui Fang, Hao Yang
1 citations
Diabetology & Metabolic Syndrome
Retinal Diseases and Treatments
1.71
article

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

Yixuan Lin, Jinju Li, Zhaohui Fang, Hao Yang
article en
1 citations

Abstract

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. 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. 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). 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.

Diabetology & Metabolic SyndromeVol. 17(1)
Anhui Medical University (CN), Anhui University of Traditional Chinese Medicine (CN), Second Affiliated Hospital of Anhui Medical University (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 15%
Retinal Diseases and Treatments
1.71
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