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已发表论文
糖尿病视网膜病变中 FOXK1 相关生物标志物的识别与验证
Received 14 November 2025
Accepted for publication 5 February 2026
Published 13 February 2026 Volume 2026:19 581708
DOI https://doi.org/10.2147/IJGM.S581708
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Brian C. Gilger
Xin Li,1 He Long,1 Shaomin Peng2
1Department of Fundus Diseases, Wuhan Aier Eye Hanyang Hospital, Wuhan, Hubei, People’s Republic of China; 2Department of Fundus Diseases, Harbin Aier Eye Hospital, Harbin, Heilongjiang, People’s Republic of China
Correspondence: Shaomin Peng, Email aierpsm@163.com
Background: Diabetic retinopathy (DR) is a leading cause of irreversible blindness globally. Analysis of dataset GSE221521 showed significant upregulation of FOXK1 in DR patient blood. Functional studies indicated that silencing FOXK1 promotes endothelial cell apoptosis, migration, and neovascularization under high glucose via the p-AKT/AKT pathway, suggesting FOXK1 plays a key role in DR. This study aims to identify FOXK1-related biomarkers in DR.
Methods: We integrated two DR-related datasets (GSE221521 and GSE189005). Candidate genes were identified through differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Biomarkers were subsequently screened using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and validated based on their expression profiles. Further analyses included immune infiltration assessment, construction of a regulatory network, Gene Set Enrichment Analysis (GSEA), protein-protein interaction analysis via GeneMANIA, and drug prediction. The expression of key biomarkers was experimentally confirmed using reverse transcription quantitative polymerase chain reaction (RT-qPCR) on clinical peripheral blood samples.
Results: From seven initial candidate genes, SPDEF and SLC25A41 were validated as diagnostic biomarkers. A predictive nomogram constructed with these biomarkers showed significant prognostic value. Immune infiltration analysis revealed significantly higher levels of Macrophages M0, monocytes, and activated CD4 memory T cells in DR samples. The constructed TF-miRNA-mRNA network included 10 transcription factors and 2 microRNAs, with FOXC1 and hsa-mir-335-5p co-regulating both SPDEF and SLC25A41. GSEA identified 74 associated pathways, including oxidative phosphorylation. Drug prediction suggested 25 potential targeting compounds, with resveratrol, daunorubicin, and digitoxigenin emerging as promising candidates. Finally, RT-qPCR analysis confirmed the significant downregulation of both SPDEF and SLC25A41 in DR patients, consistent with the bioinformatics findings.
Conclusion: Our findings suggest that SPDEF and SLC25A41 serve as potential biomarkers for DR, which may aid in early detection and provide new insights into the pathogenesis of the disease.
Keywords: diabetic retinopathy, FOXK1, biomarkers, diagnostic model, drug prediction
1Department of Fundus Diseases, Wuhan Aier Eye Hanyang Hospital, Wuhan, Hubei, People’s Republic of China; 2Department of Fundus Diseases, Harbin Aier Eye Hospital, Harbin, Heilongjiang, People’s Republic of China
Correspondence: Shaomin Peng, Email aierpsm@163.com
Background: Diabetic retinopathy (DR) is a leading cause of irreversible blindness globally. Analysis of dataset GSE221521 showed significant upregulation of FOXK1 in DR patient blood. Functional studies indicated that silencing FOXK1 promotes endothelial cell apoptosis, migration, and neovascularization under high glucose via the p-AKT/AKT pathway, suggesting FOXK1 plays a key role in DR. This study aims to identify FOXK1-related biomarkers in DR.
Methods: We integrated two DR-related datasets (GSE221521 and GSE189005). Candidate genes were identified through differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Biomarkers were subsequently screened using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and validated based on their expression profiles. Further analyses included immune infiltration assessment, construction of a regulatory network, Gene Set Enrichment Analysis (GSEA), protein-protein interaction analysis via GeneMANIA, and drug prediction. The expression of key biomarkers was experimentally confirmed using reverse transcription quantitative polymerase chain reaction (RT-qPCR) on clinical peripheral blood samples.
Results: From seven initial candidate genes, SPDEF and SLC25A41 were validated as diagnostic biomarkers. A predictive nomogram constructed with these biomarkers showed significant prognostic value. Immune infiltration analysis revealed significantly higher levels of Macrophages M0, monocytes, and activated CD4 memory T cells in DR samples. The constructed TF-miRNA-mRNA network included 10 transcription factors and 2 microRNAs, with FOXC1 and hsa-mir-335-5p co-regulating both SPDEF and SLC25A41. GSEA identified 74 associated pathways, including oxidative phosphorylation. Drug prediction suggested 25 potential targeting compounds, with resveratrol, daunorubicin, and digitoxigenin emerging as promising candidates. Finally, RT-qPCR analysis confirmed the significant downregulation of both SPDEF and SLC25A41 in DR patients, consistent with the bioinformatics findings.
Conclusion: Our findings suggest that SPDEF and SLC25A41 serve as potential biomarkers for DR, which may aid in early detection and provide new insights into the pathogenesis of the disease.
Keywords: diabetic retinopathy, FOXK1, biomarkers, diagnostic model, drug prediction