Identification of inflammatory biomarkers in IgA nephropathy using the NanoString technology: a validation study
Bibliographic record
Abstract
ABSTRACT Objective and design Immunoglobulin A nephropathy (IgAN) is a kidney disease characterized by the accumulation of IgA deposits in the glomeruli of the kidney, leading to inflammation and damage to the kidney. The inflammatory markers involved in IgAN remain to be defined. Gene expression analysis platforms, such as the NanoString nCounter system, are promising screening and diagnostic tools, especially in the oncology field, but its role as a diagnostic and prognostic tool in IgAN remains scarce. In this study, we aimed to validate the use of NanoString technology to identify potential inflammatory biomarkers involved in the progression of IgAN. Subjects A total of 30 patients with biopsy-proven IgAN and 7 cases of antineutrophil cytoplasmic antoantibodies (ANCA) associated pauci-immune glomerulonephritis were included for gene expression measurement. For the immunofluorescence validation experiments, a total of 6 IgAN patients and 3 healthy controls were included. Methods Total RNA was extracted from formalin-fixed-paraffin-embedded kidney biopsy specimens, and a customized 48-plex human gene CodeSet was used to study 29 genes implicated in different biological pathways. Comparisons in gene expression were made between IgAN and ANCA-associated pauci-immune glomerulonephritis patients to delineate an expression profile specific to IgAN. Gene expression was compared between patients with low and moderate risk of progression. Genes for which RNA expression was associated with disease progression were analyzed for protein expression by immunofluorescence, and compared with healthy controls. Results IgAN patients had a distinct gene expression profile with decreased expression in genes IL-6, INFG and C1QB compared to ANCA patients. C3 and TNFR2 were identified as potential biomarkers for IgAN progression in patients early in their disease course. Protein expression for those 2 candidate genes was upregulated in IgAN patients compared to controls. Expression of genes implicated in fibrosis ( PTEN, CASPASE 3, TGM2, TGFB1, IL2 , and TNFRSF1B ) was more pronounced in IgAN patient with severe fibrosis compared to those with none. Conclusions Our findings validate our NanoString mRNA profiling by examining protein expression levels of two candidate genes, C3 and TNFR2 , in IgAN patients and healthy controls. We were also able to identify several upregulated mRNA transcripts implicated in the development of fibrosis that may be considered as fibrotic markers within IgAN patients.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".