Identification of Potential Predictive and Diagnostic Urinary Biomarkers for Acute Rejection in Renal Transplant Recipients: A Proteomics Study
Bibliographic record
Abstract
Acute rejection (AR) is one of the main predictors of long-term survival of allograft. The development of noninvasive diagnostic biomarkers of AR is an unmet need for the timely detection. This study aimed to identify novel detective biomarkers of AR by analyzing the urine proteome profile of transplant patients. Forty-two transplant patients including 30 biopsy-proven AR patients (including antibody and T-cell mediated rejection) and 12 transplant patients with stable renal function (control group) were enrolled. Label-free quantification (LFQ) proteomics technique was performed on urine samples. Multivariate statistical analysis was applied for biomarker identification. The ELISA method validated EGF (epidermal growth factor) from the top 10 candidate biomarkers in an independent cohort. Gene ontology and possible pathways were also analyzed. LFQ analysis revealed 453 identified proteins differentially expressed between groups that mainly participated in complement and coagulation pathways and proteolysis. Ten proteins with the highest AUCs (Area under the ROC Curve) were identified as candidate diagnostic biomarkers. Candidate biomarkers were mainly associated with extracellular matrix (ECM) degradation and epithelial-to-mesenchymal transition (EMT). Reduction of urinary EGF measured by ELISA in an independent group confirmed proteomics results. We introduced a unique set of diagnostic urinary biomarkers for AR. Interactions of biomarkers and validation of EGF among biomarker panels revealed that ECM remodeling and EMT might be the consequence of immunological processes in AR. If validated as a panel, the mentioned biomarkers might shed light on the pathogenesis of chronic injury after AR and point out the potential treatment strategies.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".