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Volume 25, Issue 2
  • ISSN: 1871-5303
  • E-ISSN: 2212-3873

Abstract

Objectives

The aim of this study was to reveal the biological functionalities associated with endoplasmic reticulum stress (ERS)-related genes (ERSGs) in the context of diabetic retinopathy (DR).

Methods

Differentially expressed genes (DEGs) within the DR group and the Control group were identified and then integrated with ERSGs. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) methodologies were used to investigate potential biological mechanisms. A diagnostic model for ERS and a nomogram were formulated based on biomarkers selected through the Least Absolute Shrinkage and Selection Operator method. The diagnostic efficacy of this model was thoroughly evaluated. ERS-associated subtypes were identified, and the Single-Sample GSEA (ssGSEA) and CIBERSORT algorithms were used to assess immune infiltration.

Results

We identified 10 ERS-related DEGs (ERSRDEGs) within the DR Group. Subsequently, a diagnostic model was constructed based on 5 ERS genes, namely CCND1, IGFBP2, TLR4, TXNIP, and VIM. The validation analysis demonstrated the commendable diagnostic performance of the model. Analysis of the ssGSEA immune characteristics revealed a positive correlation in the DR group between myeloid-derived suppressor cells (MDSC), regulatory T cells (Tregs), and CCND1 TXNIP. Furthermore, a significant negative correlation was observed between central memory CD4 T cells and CCND1. In the context of CIBERSORT, the results indicated a positive correlation between macrophages and IGFBP2, as well as Tregs and IGFBP2 in the DR group. Notably, a conspicuous negative correlation was identified between resting mast cells and IGFBP2.

Conclusion

The present study provides novel diagnostic biomarkers for DR from an ERS perspective.

© 2025 Bentham Science Publishers
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2024-07-29
2025-05-20

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Detection of a Diagnostic Model and Comprehensive Examination of Diabetic Retinopathy Utilizing Genes Linked to Endoplasmic Reticulum Stress
Endocr Metab Immune Disord Drug Targets 25, 122 (2025); https://doi.org/10.2174/0118715303300673240725114443
/content/journals/emiddt/10.2174/0118715303300673240725114443
/content/journals/emiddt/10.2174/0118715303300673240725114443
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  • Article Type:     Research Article
Keyword(s): Bioinformatics; diabetic retinopathy; diagnostic model; endoplasmic reticulum stress; gene ontology; ssGSEA

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