NONINVASIVE URINARY PROTEOMICS PROVIDES VALUABLE INSIGHT INTO THE MOLECULAR CHARACTERISTICS FOR LUPUS NEPHRITIS
Bibliographic record
Abstract
O057 / #226 Topic:AS15 - Lupus Nephritis-Clinical ABSTRACT CONCURRENT SESSION 10: INTEGRATING PROTEOMIC & TRANSCRIPTOMICS IN SLE 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. Clinically, the therapy of lupus nephritis mainly depends on the renal pathological LN classification by renal biopsy. However, the renal biopsy could not repeated as much as clinically request for invasiveness and the renal outcomes are still unsatisfactory. Thus, noninvasive tools stratifying LN patients are urgently needed. Methods Here, we collected 112 urine samples from LN patients on the day before they received renal biopsy and performed in-depth urine proteomics test. Associations between urinary proteomic analysis with clinical features, pathological data, and laboratory findings were further investigated. Results Unsupervised clustering distinguished 4 molecular subtypes (type1-4). Type 1 was marked with high expression of immunomodulatory proteins and associated with renal pathological findings of higher LN pathological acute index (AI) including more cellular/cytofibrous crescent and increased leukocyte infiltration in glomeruli. Clinically, patients with type 1 showed a higher proportion of renal response rate (83%) to prednisone combined with mycophenolate mofetil. In patients with type 2, keratins were significantly upregulated, indicating their role in cell structure maintaining. Type 3 displayed the overactivation of complement system with higher expression of complement from 3 to 9 and regulatory protein of factor B and D, which might suggest potential therapeutic of complement inhibitors like eculizumab or iptocapan. Clinically, patients with type 3 showed more acute and chronic renal function injury. Type 4 was characterized by metabolic abnormalities and overexpression of SLC5A1 emerged as a distinctive hallmark. The patients with type 4 had higher rate of proteinuria and lower treatment response rate to standard therapy (55%) with stable renal function. Conclusions Noninvasive urinary proteomics provides valuable insight into the molecular characteristics and suggest new biomarkers of precise treatment in lupus nephritis.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".