ArticleFrontiers in immunology2023
Identification of a novel immune landscape signature as effective diagnostic markers related to immune cell infiltration in diabetic nephropathy.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 18 citations in OpenAlex.
- Pathology-Anchored Biomarker Research Progress for the Early Diagnosis of Diabetic Kidney Disease: From Pathological Association to Early Validation.Biomedicines · 2026Review
- Identification of key genes related to glutamine metabolism in diabetic nephropathy by machine learning methods.Scientific reports · 2025Article
- Identification and validation of tricarboxylic acid cycle-related diagnostic biomarkers for diabetic nephropathy via weighted gene co-expression network analysis and single-cell transcriptome analysis.Acta diabetologica · 2025Article
- Single-cell RNA sequencing revealed the association between proximal tubular epithelial cells and renal interstitial cells in chronic kidney disease progression.Scientific reports · 2025Article
- Identification and Validation of Basement Membrane Related LncRNA Signatures as a Novel Prognostic Model for Hepatocellular Carcinoma.Biochemical genetics · 2025Article
- Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.Scientific reports · 2025Article
- Screening core genes for minimal change disease based on bioinformatics and machine learning approaches.International urology and nephrology · 2025Article
- Crosstalk between ferroptosis and innate immune in diabetic kidney disease: mechanisms and therapeutic implications.Frontiers in immunology · 2025Review
- Unveiling fatty acid subtypes: immunometabolic interplay and therapeutic opportunities in gastric cancer.Frontiers in oncology · 2025Article
- Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning.PloS one · 2025Article
- Scutellarin attenuated tubule cell apoptosis by modulating HIF-1α for the treatment of DKD: the insight integrating network analysis, machine learning and single-cell transcriptome.Frontiers in pharmacology · 2025Article
- The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies.Journal of inflammation research · 2024Review
- Role of MCP-1 as an inflammatory biomarker in nephropathy.Frontiers in immunology · 2023Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis. Methods: GSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion |log2FC| >0.5 and adjusted P <0.05. KEGG, GO, and GSEA analyses were performed. Hub genes were screened by conducting PPI networks and calculating node genes using five algorithms with CytoHubba, followed by LASSO and ROC analysis to accurately identify diagnostic biomarkers. In addition, two different GEO datasets, GSE175759 and GSE47184, and an experiment cohort with 30 controls and 40 DN patients detected by IHC, were used to validate the biomarkers. Moreover, ssGSEA was performed to analyze the immune microenvironment in DN. Wilcoxon test and LASSO regression were used to determine the core immune signatures. The correlation between biomarkers and crucial immune signatures was calculated by Spearman analysis. Finally, cMap was used to explore potential drugs treating renal tubule injury in DN patients. Results: A total of 509 DEGs, including 338 upregulated and 171 downregulated genes, were screened out. "chemokine signaling pathway" and "cell adhesion molecules" were enriched in both GSEA and KEGG analysis. CCR2, CX3CR1, and SELP, especially for the combination model of the three genes, were identified as core biomarkers with high diagnostic capabilities with striking AUC, sensitivity, and specificity in both merged and validated datasets and IHC validation. Immune infiltration analysis showed a notable infiltration advantage for APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. In addition, the correlation analysis showed that CCR2, CX3CR1, and SELP were strongly and positively correlated with checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. Finally, dilazep was screened out as an underlying compound for DN analyzed by CMap. Conclusions: CCR2, CX3CR1, and SELP are underlying diagnostic biomarkers for DN, especially in their combination. APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation may participate in the occurrence and development of DN. At last, dilazep may be a promising drug for treating DN.
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