ArticleRenal failure2026
A nine-gene diagnostic model for IgA nephropathy based on multi-cohort machine learning: integrating gene expression and immunohistochemical validation.
Article in Renal failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
IgA nephropathy (IgAN) is the most common primary glomerulonephritis, requiring improved diagnostic tools. We analyzed three cohorts (GSE37460, GSE93798, and GSE115857 are internal validation cohorts) using gene set enrichment analysis on 7751 pathways. A machine learning model was developed and externally validated in multi-cohort gene expression data (external validation cohorts are GSE99339, GSE116626, and GSE104948). Additionally, immunohistochemistry was performed to validate the expression of key biomarkers and the presence of functionally active immune cells. We developed and validated a multi-cohort machine learning diagnostic model. The selected two-step glmBoost + Enet [alpha = 0.4] model achieved high concordance in GSE37460 (κ = 0.704,
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