ArticleIET systems biology2023
Identification of key immune-related genes and immune infiltration in diabetic nephropathy based on machine learning algorithms.
Article in IET systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Unveiling the immune microenvironment in diabetic nephropathy: from mechanisms to therapeutics.Frontiers in immunology · 2026Review
- A lipid metabolism-based gene signature defines diagnosis and molecular heterogeneity in diabetic nephropathy.Iranian journal of basic medical sciences · 2026Article
- Identification of IL1R1 as a potential key PANoptosis-related gene in myocardial ischemia-reperfusion injury using machine learning.Scientific reports · 2025Article
- Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.Scientific reports · 2025Article
- Comprehensive Analysis and Experimental Validation of Immune-Related Biomarkers and Immune Microenvironment in Diabetic Nephropathy.Journal of inflammation research · 2025Article
- Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms.BMC medical genomics · 2024Article
- Identification of key immune-related genes and immune infiltration in diabetic nephropathy based on machine learning algorithms.IET systems biology · 2023Article
- Investigating the Impact of Antibiotics on Environmental Microbiota Through Machine Learning Models.IET systems biologyArticle
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology.
methodsThe GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls.
resultsIn total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people.
conclusionImmune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.
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