ArticleFrontiers in endocrinology2024
Transcriptome analysis combined with Mendelian randomization screening for biomarkers causally associated with diabetic retinopathy.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification and analysis of oxidative stress-related genes associated with the occurrence and development of diabetic retinopathy.Scientific reports · 2026Article
- Article
- Advancements in diabetic retinopathy: Insights and future directions.World journal of methodology · 2025Article
- Identification of biomarkers for endometriosis based on summary-data-based Mendelian randomization and machine learning.Medicine · 2025Article
- Retinal Biomarkers in Diabetic Retinopathy: From Early Detection to Personalized Treatment.Journal of clinical medicine · 2025Review
- Unveiling prognostic value of JAK/STAT signaling pathway related genes in colorectal cancer: a study of Mendelian randomization analysis.Infectious agents and cancer · 2025Article
- Non-ocular biomarkers for early diagnosis of diabetic retinopathy by non-invasive methods.Frontiers in endocrinology · 2025Review
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Authors and funding
7 authors.
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Abstract
Background: Diabetic retinopathy (DR) is considered one of the most severe complications of diabetes mellitus, but its pathogenesis is still unclear. We hypothesize that certain genes exert a pivotal influence on the progression of DR. This study explored biomarkers for the diagnosis and treatment of DR through bioinformatics analysis. Methods: Within the GSE221521 and GSE189005 datasets, candidate genes were acquired from intersections of genes obtained using WGCNA and DESeq2 packages. Mendelian randomization (MR) analysis selected candidate biomarkers exhibiting causal relationships with DR. Receiver Operating Characteristic (ROC) analysis determined the diagnostic efficacy of biomarkers, the expression levels of biomarkers were verified in the GSE221521 and GSE189005 datasets, and a nomogram for diagnosing DR was constructed. Enrichment analysis delineated the roles and pathways associated with the biomarkers. Immune infiltration analysis analyzed the differences in immune cells between DR and control groups. The miRNet and networkanalyst databases were then used to predict the transcription factors (TFs) and miRNAs, respectively, of biomarkers. Finally, RT-qPCR was used to verify the expression of the biomarkers Results: MR analysis identified 13 candidate biomarkers that had causal relationships with DR. The ROC curve demonstrated favorable diagnostic performance of three biomarkers ( Conclusion:
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