ArticleFrontiers in immunology2024
Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Large language models show high clinical safety but differences in completeness for reproductive counselling in women with inflammatory rheumatic and musculoskeletal diseases: a comparative expert evaluation.Rheumatology international · 2026Article
- Exploring the potential targets and mechanisms of artemisinin in the treatment of diabetic kidney disease using network pharmacology and molecular docking.Functional & integrative genomics · 2026Article
- Identification and validation of lactylation-related diagnostic biomarkers for type 2 diabetes by WGCNA.Journal of clinical biochemistry and nutrition · 2026Article
- Functional genomics integration of glycolysis-related gene networks reveals prognostic biomarkers and immune microenvironment regulation in breast cancer.Scientific reports · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Implications of pentose phosphate metabolism and astrocyte co-expression patterns in the pathogenesis of Alzheimer's disease: evidence from artificial intelligence-driven omics and clinical validation.Frontiers in neuroscience · 2026Article
- Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validation.Frontiers in neuroscience · 2026Article
- Artificial-intelligence- and multi-omics-guided predictive and drug repurposing framework construction for colorectal cancer: evidence from succinylation-neutrophil signatures.Frontiers in cell and developmental biology · 2026Article
- Decoding isonicotinylation-associated patterns in neutrophil chronic obstructive pulmonary disease: evidence from integrative bioinformatic-driven multi-omics andFrontiers in physiology · 2026Article
- Implications of autolysosome- astrocyte-associated signature in the pathogenesis of Alzheimer's disease: evidence from artificial intelligence and multi-omics and clinical validation.Frontiers in neuroscience · 2026Article
- Implications of isonicotinylation-associated patterns in NK cells in the pathogenesis of Kawasaki disease: evidence from artificial intelligence-driven multi-omics and clinical validation.Frontiers in pediatrics · 2026Article
- Identification and validation of mitophagy and astrocyte-related molecular signature in the pathogenesis of Alzheimer's disease: evidence from ensemble learning-driven multi-omics and clinical validation.Frontiers in neuroscience · 2026Article
- Decoding serum C-reactive protein-associated molecular patterns in aging and secondhand smoke exposure chronic obstructive pulmonary disease male patients: evidence from cross-sectional, multi-omic and clinical studies.Frontiers in medicine · 2026Article
- PTEN: A Novel Diabetes Nephropathy Protective Gene Related to Cellular Senescence.International journal of molecular sciences · 2025Article
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10 authors.
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Abstract
Background: Diabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy. Methods: Transcriptomic profiles of DN samples and controls were extracted from GEO databases. Differentially expressed genes (DEGs) and their functional enrichments were identified. Glycolysis-related genes (GRGs) were selected by combining DEGs, weighted gene co-expression network, and glycolysis candidate genes. We established a diagnostic signature termed GScore via integrative machine learning framework. The diagnostic efficacy was evaluated by decision curve and calibration curve. Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. The cMAP database was used to find potential therapeutic agents targeting GScore for DN. The expression levels of diagnostic signatures were verified Results: Through the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. Based on them, an integrative model named GScore was established for predicting DN onset and stratifying clinical risk. We observed distinct biological characteristics and immunological microenvironment states between the high-risk and low-risk groups. GScore was significantly associated with neutrophils and non-classical monocytes. Potential agents including esmolol, estradiol, ganciclovir, and felbamate, targeting the 12 diagnostic signatures were identified. Conclusion: An integrative machine learning frame established a novel diagnostic signature using glycolysis-related genes. This study provides a new direction for the early diagnosis and treatment of DN.
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