Identification of HO-1 as a novel biomarker for graft acute cellular rejection and prognosis prediction after liver transplantation
Bibliographic record
Abstract
BACKGROUND: Liver transplantation (LT) is the most effective treatment for patients with end-stage liver diseases, but acute rejection is still a major concern. However, the mechanisms underlying rejection remain unclear. Biomarkers are lacking for predicting rejection and long-term survival after LT. METHODS: Isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics was performed between acute cellular rejection (ACR) and non-rejection recipients. The molecular signature differences and potential biomarkers were identified by comprehensive bioinformatics. Heme oxygenase-1 (HO-1) expression and its association with clinical outcomes were investigated by tissue microarrays consisted of liver specimens from recipients with (n=80) and without ACR (n=57). RESULTS: A total of 287 differentially expressed proteins (DEPs) were identified. Pathway analysis revealed that T/B cell activation, integrin/inflammation signaling pathway, etc. were significantly correlated with ACR. Through comprehensive bioinformatics, HO-1 was identified as a candidate potential biomarker for ACR. In tissue microarray (TMA) analysis, HO-1 expression was significantly higher in ACR group than in non-rejection group (P<0.01). Preoperative Child-Pugh and Meld scores were significantly higher in recipients with high HO-1 expression (P<0.01). In a mean 5-year follow-up, recipients with high HO-1 expression were associated with a shorter overall survival (P<0.05). Further multivariate analyses indicated that HO-1 could be an independent adverse prognostic factor for post-transplant survival (P=0.005). CONCLUSIONS: A total of 287 DEPs were identified, providing a set of targets for further research. Recipients with high preoperative HO-1 expression were associated with ACR. HO-1 may be used as a potential biomarker for predicting the development of post-transplant allograft ACR and recipient's survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".