Identification of Kidney Transcripts Associated with Prognosis in ANCA-Associated Glomerulonephritis
Bibliographic record
Abstract
Background: Kidney involvement in ANCA-associated vasculitis (AAV-GN) predicts poor patient and kidney survival. To get insights into pathogenic mechanisms or identify biomarkers for refining prognosis, we aimed to investigate the potential prognostic value of kidney transcripts associated with kidney survival. Methods: Immune gene transcript analysis was performed on RNA extracted from 97 adult AAV-GN kidney biopsies from the French Maine-Anjou Registry using NanoString technology. Transcripts of interest were selected, and their prognostic performance was compared to current histological-based classifications. Following the identification of a possible role for clusterin (CLU), the relationship between serum CLU and prognosis was assessed. Results: Among the 750 evaluated transcripts, we identified a 4-gene signature (XRCC6, PRKCD, TEK, and CLU) strongly associated with kidney survival (Figure 1A). This signature predicted kidney survival better than histological-based classifications (global C-Index 0.87 vs. 0.65 for Berden classification or 0.81 for Renal Risk Score, with better time-dependent AUC and Brier scores) (Figure 1B). Among these 4 transcripts, the expression level of the CLU transcript had the highest correlation with glomerular involvement, kidney function at diagnosis, and kidney survival. Serum CLU levels were associated with kidney survival, especially when assessed at 6 months from diagnosis (Figure 1C, p = 0.023). Conclusion: Transcriptomic analysis of kidney biopsies of AAV-GN identified potential transcripts that may improve prediction of kidney survival. This transcriptomic signature may help us gain a deeper understanding of the AAV-GN pathogenesis and provide insights for developing new therapeutic options.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".