ArticleRenal failure2026
Integrative transcriptomic and machine learning analysis identifies PYCARD and IFI30 as immune-lysosomal biomarkers of ANCA-associated glomerulonephritis.
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
objectivesANCA-associated glomerulonephritis (ANCA-GN) is an immune-mediated kidney disease leading to acute or chronic renal failure. This study investigates the role of mitophagy-related genes in ANCA-GN, as mitochondrial dysfunction is closely linked to the pathogenesis of various kidney diseases.
methodsThis study analyzed transcriptomic data from GEO datasets (GSE104948 and GSE108109) to investigate mitophagy-related mechanisms in ANCA-GN. Methods included batch correction, consensus clustering (identifying two subtypes), weighted gene co-expression network analysis (WGCNA), differential expression screening, and machine learning (LASSO, random forest, SVM-RFE). A diagnostic nomogram was constructed and validated, and immune cell infiltration was profiled.
resultsAnalyses revealed distinct activation of immune pathways, including complement and phagosome signaling, alongside abnormal infiltration of CD8+ T cells in ANCA-GN. Subtype-specific analysis identified 131 differentially expressed genes (DEGs), while 143 DEGs distinguished ANCA-GN from controls.Intersection analysis and machine learning prioritized two hub genes,
conclusionsThese findings suggest that mitophagy-immune crosstalk drives ANCA-GN progression, with PYCARD and IFI30 as potential diagnostic biomarkers. This study provides mechanistic insights into ANCA-GN pathogenesis and proposes novel targets for clinical intervention.
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