Integrative transcriptomic and machine learning analysis identifies PYCARD and IFI30 as immune-lysosomal biomarkers of ANCA-associated glomerulonephritis - PubMed
3 months ago
- #Biomarkers
- #Mitophagy
- #ANCA-GN
- Study identifies PYCARD and IFI30 as immune-lysosomal biomarkers for ANCA-associated glomerulonephritis (ANCA-GN).
- Transcriptomic data from GEO datasets (GSE104948 and GSE108109) were analyzed using methods like batch correction, consensus clustering, WGCNA, and machine learning.
- Two distinct subtypes of ANCA-GN were identified, with 131 differentially expressed genes (DEGs) specific to subtypes and 143 DEGs distinguishing ANCA-GN from controls.
- PYCARD and IFI30 showed strong diagnostic accuracy (AUC >0.9) and correlation with CD8+ T-cell infiltration.
- A diagnostic nomogram validated the clinical utility of PYCARD and IFI30 (AUC >0.9).
- Functional enrichment highlighted phagocytosis and immune signaling pathways in ANCA-GN.
- Immune profiling revealed significant upregulation of 20 immune cell types in ANCA-GN.
- Findings suggest mitophagy-immune crosstalk drives ANCA-GN progression, offering novel targets for clinical intervention.