ArticleFrontiers in endocrinology2022
Screening of the Key Genes and Signalling Pathways for Diabetic Nephropathy Using Bioinformatics Analysis.
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 22 papers.
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22 citing papers in PubMed, 36 citations in OpenAlex.
- Identification and validation of prognostic genes related to mitochondrial dynamics and mitochondrial autophagy in esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Identification of key genes related to glutamine metabolism in diabetic nephropathy by machine learning methods.Scientific reports · 2025Article
- Unveiling the role of angiogenesis-related biomarkers in diabetic nephropathy via transcriptomics.Diabetology & metabolic syndrome · 2025Article
- Proximity extension assay inflammatory profiling cannot distinguish the presence of residual C-peptide in patients with long-standing type 1 diabetes.Acta diabetologica · 2025Article
- VISTA Alleviates Microglia-Mediated Neuroinflammation After Cerebral Ischemia-Reperfusion Injury via Regulating ACOD1/Itaconic Acid Metabolism.Molecular neurobiology · 2025Article
- Integrative Bioinformatics-Guided Analysis of Glomerular Transcriptome Implicates Potential Therapeutic Targets and Pathogenesis Mechanisms in IgA Nephropathy.Bioengineering (Basel, Switzerland) · 2025Article
- Identification and validation of epithelial‑mesenchymal transition‑related genes for diabetic nephropathy by WGCNA and machine learning.Molecular medicine reports · 2025Article
- Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.Scientific reports · 2025Article
- Investigation of the Pathogenesis of Liver Fibrosis Associated with Type 2 Diabetes Mellitus via Bioinformatic Analysis.Biomedicines · 2025Article
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- L-shaped nonlinear relationship between magnesium intake from diet and supplements and the risk of diabetic nephropathy: a cross-sectional study.Frontiers in nutrition · 2025Article
- Elevated RHAMM as a biomarker for predicting diabetic kidney disease in patients with type 2 diabetes.Clinical kidney journal · 2024Article
- Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis.Frontiers in medicine · 2024Article
- VCAM1: an effective diagnostic marker related to immune cell infiltration in diabetic nephropathy.Frontiers in endocrinology · 2024Article
- Unveiling the hub genes in the SIGLECs family in colon adenocarcinoma with machine learning.Frontiers in genetics · 2024Article
- To Develop Biomarkers for Diabetic Nephropathy Based on Genes Related to Fibrosis and Propionate Metabolism and Their Functional Validation.Journal of diabetes research · 2024Article
- Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy.Journal of translational medicine · 2023Article
- Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease.Frontiers in immunology · 2023Article
- Identification and validation of disulfidptosis-related gene signatures and their subtype in diabetic nephropathy.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to identify biological markers for diabetic nephropathy (DN) and explore their underlying mechanisms. Methods: Four datasets, GSE30528, GSE47183, GSE104948, and GSE96804, were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the "limma" package, and the "RobustRankAggreg" package was used to screen the overlapping DEGs. The hub genes were identified using cytoHubba of Cytoscape. Logistic regression analysis was used to further analyse the hub genes, followed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. Correlation analysis and enrichment analysis of the hub genes were performed to identify the potential functions of the hub genes involved in DN. Results: In total, 55 DEGs, including 38 upregulated and 17 downregulated genes, were identified from the three datasets. Four hub genes ( Conclusions: We identified four candidate genes:
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