ArticleScientific reports2022
Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Diagnostic potential of B-cell translocation gene 2 in IgA nephropathy: Insights from molecular mechanisms.Journal of cell communication and signaling · 2026Article
- Spatial transcriptomic profiling reveals molecular signatures of endocapillary hypercellularity in IgA nephropathy.BMC nephrology · 2026Article
- Integrative Bioinformatics-Guided Analysis of Glomerular Transcriptome Implicates Potential Therapeutic Targets and Pathogenesis Mechanisms in IgA Nephropathy.Bioengineering (Basel, Switzerland) · 2025Article
- Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.BMC nephrology · 2025Article
- Copper Metabolism-Related Genes as Biomarkers in Colon Adenoma and Cancer.International journal of general medicine · 2025Article
- Mechanistic insights into Shenqi Dihuang decoction in the treatment of immunoglobulin a nephropathy.Frontiers in pharmacology · 2025Article
- Identification of hub fatty acid metabolism-related genes and immune infiltration in IgA nephropathy.Renal failure · 2024Article
- Glomerular spatial transcriptomics of IgA nephropathy according to the presence of mesangial proliferation.Scientific reports · 2024Article
- Ferroptosis-Related Genes in IgA Nephropathy: Screening for Potential Targets of the Mechanism.International journal of genomics · 2024Article
- Prediction of hub genes and key pathways associated with the radiation response of human hematopoietic stem/progenitor cells using integrated bioinformatics methods.Scientific reports · 2023Article
- Omics are Getting Us Closer to Understanding IgA Nephropathy.Archivum immunologiae et therapiae experimentalis · 2023Review
- Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.Scientific reports · 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
- APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN.Frontiers in pharmacology · 2023Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.
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