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<p>Diabetic retinopathy (DR), a frequently encountered microvascular complication of diabetes, currently lacks effective treatment options due to the complexity of its underlying pathophysiological mechanisms. The identification of disulfidptosis as a subtype of cell death opens up novel avenues for investigating the pathogenesis of several diseases. This study aims to identify and validate differentially expressed disulfidptosis-related genes (DRGs) in peripheral blood samples of patients with DR, and to explore their association with immune cell infiltration. Clinical patient datasets (GSE221521) were obtained from public online databases. Based on this dataset, differential expression, correlation, and enrichment analyses of DRGs were performed using R software to determine their potential mechanisms of action. False Discovery Rate (FDR) correction was employed to reduce false positive results (significance threshold at FDR &lt; 0.05) based on the Benjamini-Hochberg method. Subsequently, the CIBERSORT algorithm was deployed to assess the infiltration levels immune cell associated with the differentially expressed DRGs, in order to explore immune dysregulation in the context of DR. Meanwhile, nomograms, calibration curves, ROC curves, nomograms, and decision curve analyses were conducted to validate the accuracy of key genes and construct a disease prediction model for assessing DR risks. Finally, the differentially expressed DRGs were validated using clinical samples from DR patients. Based on the GSE221521 dataset, significantly differential expressions of eight disulfidptosis-related genes were observed, and individual validation using clinical samples confirmed consistent expression patterns for FLNB, GYS1, FLNA, PRDX1, among which FLNB and GYS1 showed statistically significant differences. Immune infiltration analysis revealed that five DRGs (TLN1, FLNA, PRDX1, FLNB, and GYS1) were strongly correlated with macrophages, CD4 memory activated T cells, and M0 monocytes in DR patients. Functional enrichment analysis highlighted the involvement of platelet aggregation and activation, as well as Rap1 signaling, in the initiation and development of the disease. A joint predictive model was constructed based on eight differentially expressed DRGs, and achieved an AUC of 0.818, significantly outperforming single-gene models. This model was visualized as a nomogram to facilitate rapid assessment of individual risk based on gene expression patterns for early risk prediction and personalized intervention. However, this prediction model was built on a single dataset and required further validation in other independent queues. This is the first study to identify a strong association between DR and disulfidptosis, providing a novel perspective for identifying biomarkers and potential treatment strategies for DR. </p>

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Study on the differential expression of disulfidptosis-related genes and their association with immune regulation in patients with diabetic retinopathy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Yue Hao,
  • Xi-Xi Zhang,
  • Xin-Yi Wang,
  • Jun-Tao Zhang,
  • Li-Fen Guo,
  • Heng-Qian He,
  • Li-Qin Ying,
  • Si-Yu Xian,
  • Hao Liu,
  • Qin-Kang Lu

摘要

Diabetic retinopathy (DR), a frequently encountered microvascular complication of diabetes, currently lacks effective treatment options due to the complexity of its underlying pathophysiological mechanisms. The identification of disulfidptosis as a subtype of cell death opens up novel avenues for investigating the pathogenesis of several diseases. This study aims to identify and validate differentially expressed disulfidptosis-related genes (DRGs) in peripheral blood samples of patients with DR, and to explore their association with immune cell infiltration. Clinical patient datasets (GSE221521) were obtained from public online databases. Based on this dataset, differential expression, correlation, and enrichment analyses of DRGs were performed using R software to determine their potential mechanisms of action. False Discovery Rate (FDR) correction was employed to reduce false positive results (significance threshold at FDR < 0.05) based on the Benjamini-Hochberg method. Subsequently, the CIBERSORT algorithm was deployed to assess the infiltration levels immune cell associated with the differentially expressed DRGs, in order to explore immune dysregulation in the context of DR. Meanwhile, nomograms, calibration curves, ROC curves, nomograms, and decision curve analyses were conducted to validate the accuracy of key genes and construct a disease prediction model for assessing DR risks. Finally, the differentially expressed DRGs were validated using clinical samples from DR patients. Based on the GSE221521 dataset, significantly differential expressions of eight disulfidptosis-related genes were observed, and individual validation using clinical samples confirmed consistent expression patterns for FLNB, GYS1, FLNA, PRDX1, among which FLNB and GYS1 showed statistically significant differences. Immune infiltration analysis revealed that five DRGs (TLN1, FLNA, PRDX1, FLNB, and GYS1) were strongly correlated with macrophages, CD4 memory activated T cells, and M0 monocytes in DR patients. Functional enrichment analysis highlighted the involvement of platelet aggregation and activation, as well as Rap1 signaling, in the initiation and development of the disease. A joint predictive model was constructed based on eight differentially expressed DRGs, and achieved an AUC of 0.818, significantly outperforming single-gene models. This model was visualized as a nomogram to facilitate rapid assessment of individual risk based on gene expression patterns for early risk prediction and personalized intervention. However, this prediction model was built on a single dataset and required further validation in other independent queues. This is the first study to identify a strong association between DR and disulfidptosis, providing a novel perspective for identifying biomarkers and potential treatment strategies for DR.