ArticleFrontiers in endocrinology2022
Identification and Verification of Diagnostic Biomarkers for Glomerular Injury in Diabetic Nephropathy Based on Machine Learning Algorithms.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 43 citations in OpenAlex.
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- Identification and validation of epithelial‑mesenchymal transition‑related genes for diabetic nephropathy by WGCNA and machine learning.Molecular medicine reports · 2025Article
- Tracing the molecular landscape of diabetic nephropathy: Insights from machine learning and experiment verification.Journal of diabetes investigation · 2025Article
- Immune biomarkers as early indicators of renal damage in type 1 diabetic children: A step toward translational medicine.World journal of diabetes · 2025Article
- Construction of a diagnostic model utilizing m7G regulatory factors for the characterization of diabetic nephropathy and the immune microenvironment.Scientific reports · 2025Article
- Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke.Scientific reports · 2024Article
- Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms.Scientific reports · 2024Article
- Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture.Genes & diseases · 2024Article
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- Influencing factors of acute kidney injury in elderly patients with diabetic nephropathy and establishment of nomogram model.Frontiers in endocrinology · 2024Article
- Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.Current medicinal chemistry · 2024Article
- Network pharmacology combined with Mendelian randomization analysis to identify the key targets of renin-angiotensin-aldosterone system inhibitors in the treatment of diabetic nephropathy.Frontiers in endocrinology · 2024Article
- Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression.BMC immunology · 2023Article
- Co-Expression of MultipleInternational journal of molecular sciences · 2023Article
- Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study.Frontiers in oncology · 2023Article
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Authors and funding
10 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Diabetic nephropathy (DN) is regarded as the leading cause of end-stage renal disease worldwide and lacks novel therapeutic targets. To screen and verify special biomarkers for glomerular injury in patients with DN, fifteen datasets were retrieved from the Gene Expression Omnibus (GEO) database, correspondingly divided into training and testing cohorts and then merged. Using the limma package, 140 differentially expressed genes (DEGs) were screened out between 81 glomerular DN samples and 41 normal ones from the training cohort. With the help of the ConsensusClusterPlus and WGCNA packages, the 81 glomerular DN samples were distinctly divided into two subclusters, and two highly associated modules were identified. By using machine learning algorithms (LASSO, RF, and SVM-RFE) and the Venn diagram, two overlapping genes (PRKAR2B and TGFBI) were finally determined as potential biomarkers, which were further validated in external testing datasets and the HFD/STZ-induced mouse models. Based on the biomarkers, the diagnostic model was developed with reliable predictive ability for diabetic glomerular injury. Enrichment analyses indicated the apparent abnormal immune status in patients with DN, and the two biomarkers played an important role in the immune microenvironment. The identified biomarkers demonstrated a meaningful correlation between the immune cells' infiltration and renal function. In conclusion, two robust genes were identified as diagnostic biomarkers and may serve as potential targets for therapeutics of DN, which were closely associated with multiple immune cells.
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