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Published on: July 8, 2020
Identification of Renal Transcripts Associated with Kidney Function and Prognosis in ANCA-Associated Vasculitis
Benoît Brilland1,2,3, Jérémie Riou4, Thomas Quéméneur5
1Service de Néphrologie-Dialyse-Transplantation, CHU Angers, Angers, France.
A new 12-gene signature from kidney transcriptomics accurately predicts kidney survival in ANCA-associated vasculitis with glomerulonephritis (AAV-GN). This molecular approach surpasses current risk stratification tools for better patient outcomes.
Area of Science:
- Nephrology
- Immunology
- Genomics
Background:
- ANCA-associated vasculitis with glomerulonephritis (AAV-GN) often leads to kidney failure, with limited tools for predicting outcomes.
- Current risk stratification methods do not fully capture kidney injury's molecular complexity, hindering personalized treatment.
Purpose of the Study:
- To investigate if kidney transcriptomics can identify molecular signatures associated with kidney outcomes in AAV-GN.
- To develop a gene-based prognostic signature for improved risk stratification.
Main Methods:
- Analyzed kidney biopsies from 199 AAV-GN patients and 23 controls using NanoString nCounter for 750 immune-related genes.
- Performed differential gene expression, pathway enrichment, and immune cell infiltration analyses.
- Developed and validated a 12-gene prognostic signature using LASSO-penalized Cox regression.
Main Results:
- AAV-GN showed significant immune dysregulation, with 150 differentially expressed genes and altered immune cell infiltration.
- A 12-gene signature demonstrated a stronger association with kidney survival than established histological classifications (p < 0.0001).
- Specific immune pathway expressions, particularly lymphocyte trafficking, correlated with patient outcomes.
Conclusions:
- Kidney biopsy transcriptomics reveals key immune-related genes in AAV-GN.
- A novel 12-gene signature accurately predicts kidney survival in AAV-GN patients.
- This molecular signature offers superior risk stratification compared to existing methods.
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