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<p>Diabetic retinopathy (DR) develops via complex interactions of multiple risk factors and molecular pathways. However, the association between oxidative stress-related genes (OSRGs) and DR progression remains poorly understood. We analyzed two DR datasets (GSE221521, GSE189005) and 986 OSRGs. Differentially expressed genes (DEGs) from GSE221521 were intersected with OSRGs to generate candidate genes; those with protein-protein interaction (PPI) network associations were defined as candidate hub genes. Machine learning further filtered final hub genes, which underwent cross-dataset expression validation. Biomarkers were identified by receiver operating characteristic (ROC) curve analysis (AUC &gt; 0.7) and subjected to nomogram construction, functional annotation, and immune correlation analysis. We obtained 52 candidate genes, 39 of which had PPI interactions. Machine learning yielded 13 final hub genes; CD36, DDIT3, E2F2, NFATC4, and RPL11 showed consistent expression trends across datasets. CD36, DDIT3, E2F2, and RPL11 (AUC &gt; 0.7) were validated as biomarkers, with robust DR predictive performance in nomograms. These genes were enriched in ribosome and spliceosome pathways. Immune infiltration analysis identified three differential cell types: M0 macrophages, monocytes, and activated memory CD4 + T cells. CD36, DDIT3, E2F2, and RPL11 are potential diagnostic biomarkers for DR. The present bioinformatic findings provide preliminary clues for subsequent exploration of DR pathogenesis.</p>

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Identification and analysis of oxidative stress-related genes associated with the occurrence and development of diabetic retinopathy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月22日

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  • Liangjie Niu,
  • Yifan Zhang,
  • Huika Xia,
  • Jianmin Wang

摘要

Diabetic retinopathy (DR) develops via complex interactions of multiple risk factors and molecular pathways. However, the association between oxidative stress-related genes (OSRGs) and DR progression remains poorly understood. We analyzed two DR datasets (GSE221521, GSE189005) and 986 OSRGs. Differentially expressed genes (DEGs) from GSE221521 were intersected with OSRGs to generate candidate genes; those with protein-protein interaction (PPI) network associations were defined as candidate hub genes. Machine learning further filtered final hub genes, which underwent cross-dataset expression validation. Biomarkers were identified by receiver operating characteristic (ROC) curve analysis (AUC > 0.7) and subjected to nomogram construction, functional annotation, and immune correlation analysis. We obtained 52 candidate genes, 39 of which had PPI interactions. Machine learning yielded 13 final hub genes; CD36, DDIT3, E2F2, NFATC4, and RPL11 showed consistent expression trends across datasets. CD36, DDIT3, E2F2, and RPL11 (AUC > 0.7) were validated as biomarkers, with robust DR predictive performance in nomograms. These genes were enriched in ribosome and spliceosome pathways. Immune infiltration analysis identified three differential cell types: M0 macrophages, monocytes, and activated memory CD4 + T cells. CD36, DDIT3, E2F2, and RPL11 are potential diagnostic biomarkers for DR. The present bioinformatic findings provide preliminary clues for subsequent exploration of DR pathogenesis.