ArticleAnnals of translational medicine2022
Bioinformatics analysis identifies diagnostic biomarkers and their correlation with immune infiltration in diabetic nephropathy.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Characterizing the Immune Cell Infiltration in Renal Interstitium and Therapeutic Targets of Drugs in Diabetic Nephropathy by Multiomics Study.Diabetes & metabolism journal · 2026Article
- Mendelian randomization analysis identifies HLA-A and AP2M1 as genetic biomarkers linked to immune-endocytic crosstalk in intervertebral disc degeneration.Journal of cell communication and signaling · 2026Article
- Identification of key genes in diabetic nephropathy based on lipid metabolism.Experimental and therapeutic medicine · 2024Article
- The immune-inflammation factor is associated with diabetic nephropathy: evidence from NHANES 2013-2018 and GEO database.Scientific reports · 2024Article
- Combined Placental Mesenchymal Stem Cells with Guided Nanoparticles Effective Against Diabetic Nephropathy in Mouse Model.International journal of nanomedicine · 2024Article
- APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment.Frontiers in endocrinology · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
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Abstract
Background: Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD). Currently, microalbuminuria is mainly used as a diagnostic indicator of DN, but there are still limitations and lack of immune-related diagnostic markers. In this study, we aimed to explore diagnostic biomarkers associated with immune infiltration of DN. Methods: Immune-related differentially expressed genes (DEGs) were derived from those at the intersection of the ImmPort database and DEGs identified from 3 datasets, which were based on the Gene Expression Omnibus (GEO). Functional enrichment analyses were performed; a protein-protein interaction (PPI) network was constructed; and hub genes were identified by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). After screening the key genes using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), a prediction model for DN was constructed. The predictive performance of the model was quantified by receiver-operating characteristic curve, decision curve analysis, and nomogram. Next, infiltration of 22 types of immune cells in DN kidney tissue was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Expression of diagnostic markers was analyzed in DN and control patient groups to determine the genes with the maximum diagnostic potential. Finally, we explored the correlation between diagnostic markers and immune cells. Results: Overall, 191 immune-related DEGs were identified, that primarily positively regulated with cell adhesion, T cell activation, leukocyte proliferation and migration, urogenital system development, lymphocyte differentiation and proliferation, and mononuclear cell proliferation. Gene sets were related to the PI3K-Akt, MAPK, Rap1, and WNT signaling pathways. Finally, Conclusions: Our results provide evidence that
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