ArticleInternational journal of molecular sciences2025
PTEN: A Novel Diabetes Nephropathy Protective Gene Related to Cellular Senescence.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Targeting the miR-224-5p/PTEN pathway: A previously under-characterized strategy to attenuate mesangial cell injury and predict prognosis in diabetic nephropathy.Journal of diabetes investigation · 2026Article
- Chronic Kidney Disease and Cellular Senescence.International journal of molecular sciences · 2026Review
- Unveiling the role of angiogenesis-related biomarkers in diabetic nephropathy via transcriptomics.Diabetology & metabolic syndrome · 2025Article
- Multi-omics and machine learning identify FN1 and ALDH2 as diagnostic biomarkers and therapeutic targets in early and late diabetic kidney disease.Renal failure · 2025Article
- Identification and validation of epithelial‑mesenchymal transition‑related genes for diabetic nephropathy by WGCNA and machine learning.Molecular medicine reports · 2025Article
- G-Protein-Coupled Receptors in Chronic Kidney Disease Induced by Hypertension and Diabetes.Cells · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD). The current diagnostic and therapeutic approaches need to be improved. Cellular senescence has been implicated in the pathogenesis of DN, but its precise role remains unclear. This study aimed to identify key pathogenic genes related to cellular senescence in DN and explore their potential as diagnostic biomarkers. Using transcriptomic data from GEO datasets (GSE96804, GSE30122, GSE142025, and GSE104948) and cellular senescence-related genes sourced from the GenAge database, we integrated multiple bioinformatics approaches, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), machine learning and protein-protein interaction (PPI), to identify diagnostic genes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.