ArticleDiabetology & metabolic syndrome2024
Identification of metabolic reprogramming-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics.
Article in Diabetology & metabolic syndrome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Natural products modulate renal metabolic reprogramming in diabetic kidney disease.Journal of translational internal medicine · 2026Article
- Integrative Multi-Omics Analysis Reveals HNRNPLL as a Potential Biomarker Associated with Hepatocellular Carcinoma Progression.Metabolites · 2026Article
- Modified Shen-Yan-Fang-Shuai formula attenuates diabetic kidney disease progression via regulation of HIF-1α-mediated mitochondrial energy metabolism.Chinese medicine · 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
- Exploring Endoplasmic Reticulum Stress-Related Genes in Cartilage Defects: Implications for Diagnosis and Therapy.Combinatorial chemistry & high throughput screening · 2026Article
- Machine Learning-Guided Multi-Omics Integration Identifies UGCG as a Candidate Lipid Metabolic Biomarker and Potential Therapeutic Target in Sepsis.Journal of inflammation research · 2026Article
- Uncovering the Independent Role of Lipid Peroxidation in Osteoporosis Through an Integrated Bibliometric and Bioinformatics Analysis.Mediators of inflammation · 2026Article
- Transcriptomics-based analysis of muscle injury mechanism mediated by inflammatory imbalance in the Duchenne muscular dystrophy muscle microenvironment.Frontiers in neurology · 2026Article
- Integrative multi-omics analysis prioritizes ALDH2 as a candidate Puerarin-associated target linked to macrophage infiltration in lung adenocarcinoma.Frontiers in immunology · 2026Article
- Multimodal analysis reveals potential association of CDH13 with endothelial cells and its overexpression in hepatocellular carcinoma.European journal of medical research · 2025Article
- Unveiling the role of angiogenesis-related biomarkers in diabetic nephropathy via transcriptomics.Diabetology & metabolic syndrome · 2025Article
- Development and validation of transient receptor potential channel-related signature for predicting prognosis in patients with lung adenocarcinoma.Oncology letters · 2025Article
- Transcriptomics-based identification of lysine crotonylation-related biomarkers in pre-eclampsia.BMC pregnancy and childbirth · 2025Article
- Identification and evaluation of metabolic mRNAs and key miRNAs in colorectal cancer liver metastasis.Cancer cell international · 2025Article
- Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.Scientific reports · 2025Article
- Decoding the hypoxia-exosome-immune triad in OSA: PRCP/UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning.Frontiers in immunology · 2025Article
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5 authors.
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Abstract
backgroundDiabetic nephropathy (DN) is a serious complication of diabetes mellitus, marked by progressive renal damage. Recent evidence indicates that metabolic reprogramming is crucial to DN pathogenesis, yet its underlying mechanisms are not well understood. This study aimed to examine how metabolic reprogramming-related genes (MRRGs) are differentially expressed and to explore their potential mechanisms in the development of DN.
methodsWe analyzed the datasets GSE30528 and GSE96804 from the Gene Expression Omnibus (GEO), comprising 50 DN samples and 33 controls. MRRGs were sourced from GeneCards and PubMed. Data preprocessing included batch effect correction using the R package sva, followed by normalization and differential expression analysis with limma (|logFC|> 0.5, adj.p < 0.05). Functional enrichment analyses (GO, KEGG, GSEA) were performed using clusterProfiler. Protein-protein interaction (PPI) networks were constructed via STRING, identifying hub genes through CytoHubba. Regulatory networks (mRNA-TF, mRNA-miRNA) were derived from ChIPBase and StarBase. Validation of hub genes and ROC analysis assessed diagnostic performance. ssGSEA quantified immune cell infiltration.
resultsOur analysis identified 708 differentially expressed genes (DEGs), including 119 metabolic reprogramming-related DEGs (MRRDEGs). Enrichment analyses revealed significant roles for MRRDEGs in processes such as wound healing and pathways like MAPK signaling. The PPI network identified nine hub genes: FN1, CD44, KDR, EGF, HSPG2, HGF, FGF9, IGF1, and ALB, which exhibited high diagnostic accuracy (AUC 0.7 to 0.9). Notably, FN1 and CD44 showed significant association with renal fibrosis and could serve as potential biomarkers for early diagnosis and therapeutic targets in DN. Immune infiltration analysis showed notable differences in immune cell composition between DN and control samples.
conclusionThis study identifies hub genes such as FN1 and CD44, with potential diagnostic value in DN. It also reveals immune cell infiltration differences between DN patients and controls, offering insights into disease progression and potential therapeutic targets.
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