ArticleFrontiers in medicine2022
Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 23 citations in OpenAlex.
- Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.BMC cancer · 2026Article
- Mergers and migrations: drivers of population genetic structure in a captive rhesus macaque colony.BMC genomics · 2026Article
- Peripheral Immune Reprogramming Characterizes a Low C-Peptide Subgroup of Type 2 Diabetes: Transcriptomic Profiling of Peripheral Blood Mononuclear Cells and Systemic Inflammation.Journal of inflammation research · 2026Article
- Determining IFI44 as a key lupus nephritis's biomarker through bioinformatics and immunohistochemistry.Renal failure · 2025Article
- The genomic legacy of selectively breeding rhesus macaques for HIV/AIDS-related research.bioRxiv : the preprint server for biology · 2025Article
- Acetylation-regulated DUSP1 deficiency contributes to renal fibrosis progression.Theranostics · 2025Article
- Association between systemic inflammatory indicators on admission and mortality in critically ill patients with diabetic kidney disease based on the MIMIC-IV database: a cohort study.Frontiers in endocrinology · 2025Article
- Article
- Bioinformatic analysis, clinical implications and experimental validation of ferroptosis-related feature gene in IgA nephropathy: focus on DUSP1.Frontiers in medicine · 2025Article
- Monocyte human leukocyte antigen-DR-mediated diabetic nephropathy progression is a promising therapeutic target.Frontiers in endocrinology · 2025Article
- Multi-scalar data integration decoding risk genes for chronic kidney disease.BMC nephrology · 2024Article
- Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.BMC cardiovascular disorders · 2024Article
- Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture.Genes & diseases · 2024Article
- Cathepsin S (CTSS) in IgA nephropathy: an exploratory study on its role as a potential diagnostic biomarker and therapeutic target.Frontiers in immunology · 2024Article
- The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies.Journal of inflammation research · 2024Review
- Renal interferon-inducible protein 16 expression is associated with disease activity and prognosis in lupus nephritis.Arthritis research & therapy · 2023Article
- Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with non-alcoholic fatty liver disease (NAFLD) by bioinformatics analysis and machine learning.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration. Materials and methods: Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated. Results: A total of 95 DEGs were identified. Using three machine learning algorithms, Conclusion:
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