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Background <p>Diabetic retinopathy (DR) is a major cause of severe visual impairment, where early diagnosis and intervention are crucial to prevent irreversible damage. Given that obtaining biomarkers from ocular fluids is highly invasive, we here leverage bioinformatic approaches to identify novel biomarkers and potential therapeutic drugs in the peripheral blood of patients with DR.</p> Methods <p>The GSE221521 dataset, derived from peripheral blood leukocytes, was analyzed to identify differentially expressed genes (DEGs) among controls, diabetes mellitus (DM) and DR patients, with subsequent gene set enrichment analysis (GSEA). Weighted gene co-expression network analysis (WGCNA) was applied to screen DR-associated modules. Key genes were further filtered via protein-protein interaction (PPI) network and support vector machine-recursive feature elimination (SVM-RFE). Two SVM diagnostic models based on these key genes were constructed, trained on the GSE221521 training set, and validated on the test set using receiver operating characteristic (ROC) curve analysis. The potential of the identified key genes as diagnostic biomarkers was further verified in independent clinical samples. Immune infiltration patterns were compared across the three groups. Finally, potential therapeutic compounds for DR were predicted via Connectivity Map (CMap) and molecular docking.</p> Results <p>Differential expression analysis of the GSE221521 dataset identified 635 DEGs. GSEA showed that upregulated DEGs in DR patients (vs. DM) were significantly enriched in the MAPK, Insulin and VEGF signaling pathways. WGCNA identified the green and magenta modules as the top DR-associated modules, from which four (<i>FLNA</i>, <i>TSC22D4</i>, <i>U2AF2</i>, and <i>HCFC1</i>) and two (<i>NUDC</i> and <i>NDUFS6</i>) key genes were screened via PPI and SVM-RFE analysis, respectively. These key genes displayed strong diagnostic efficacy for differentiating DR from DM in our in-house clinical cohort. Immune infiltration analysis revealed significant upregulation of activated mast cells, monocytes and M0 macrophages, along with downregulation of resting mast cells, in DR patients. Integrated CMap-based screening and molecular docking further proposed VU-0400193-3, Tyrphostin-AG-1295 and TG100-115 as candidate drugs against DR.</p> Conclusions <p>This study provides novel insights into the circulating biomarkers for diagnosis of DR, and predicts therapeutic compounds for this disease.</p>

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Machine learning-driven identification of circulating biomarkers and therapeutic compounds for diabetic retinopathy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Di Chen,
  • Guanrong Wu,
  • Shuoxin Liao,
  • Haoxian Zhu,
  • Fangfang Li,
  • Chang Liu,
  • Hongyue Zhao,
  • Qianli Meng,
  • Xiang Fang

摘要

Background

Diabetic retinopathy (DR) is a major cause of severe visual impairment, where early diagnosis and intervention are crucial to prevent irreversible damage. Given that obtaining biomarkers from ocular fluids is highly invasive, we here leverage bioinformatic approaches to identify novel biomarkers and potential therapeutic drugs in the peripheral blood of patients with DR.

Methods

The GSE221521 dataset, derived from peripheral blood leukocytes, was analyzed to identify differentially expressed genes (DEGs) among controls, diabetes mellitus (DM) and DR patients, with subsequent gene set enrichment analysis (GSEA). Weighted gene co-expression network analysis (WGCNA) was applied to screen DR-associated modules. Key genes were further filtered via protein-protein interaction (PPI) network and support vector machine-recursive feature elimination (SVM-RFE). Two SVM diagnostic models based on these key genes were constructed, trained on the GSE221521 training set, and validated on the test set using receiver operating characteristic (ROC) curve analysis. The potential of the identified key genes as diagnostic biomarkers was further verified in independent clinical samples. Immune infiltration patterns were compared across the three groups. Finally, potential therapeutic compounds for DR were predicted via Connectivity Map (CMap) and molecular docking.

Results

Differential expression analysis of the GSE221521 dataset identified 635 DEGs. GSEA showed that upregulated DEGs in DR patients (vs. DM) were significantly enriched in the MAPK, Insulin and VEGF signaling pathways. WGCNA identified the green and magenta modules as the top DR-associated modules, from which four (FLNA, TSC22D4, U2AF2, and HCFC1) and two (NUDC and NDUFS6) key genes were screened via PPI and SVM-RFE analysis, respectively. These key genes displayed strong diagnostic efficacy for differentiating DR from DM in our in-house clinical cohort. Immune infiltration analysis revealed significant upregulation of activated mast cells, monocytes and M0 macrophages, along with downregulation of resting mast cells, in DR patients. Integrated CMap-based screening and molecular docking further proposed VU-0400193-3, Tyrphostin-AG-1295 and TG100-115 as candidate drugs against DR.

Conclusions

This study provides novel insights into the circulating biomarkers for diagnosis of DR, and predicts therapeutic compounds for this disease.