Targeting peroxiredoxin 2 prevents hepatocarcinogenesis in metabolic liver disease models
Bibliographic record
Abstract
Treatment options for advanced liver disease and hepatocellular carcinoma (HCC) are limited, and strategies to prevent HCC development are lacking. Aiming to discover therapeutic targets, we combined genome-wide transcriptomic analysis of liver tissues from patients with advanced liver disease and HCC and a cell-based system predicting liver disease progression and HCC risk. Computational analysis predicted peroxiredoxin 2 (PRDX2) as a candidate gene mediating hepatocarcinogenesis and HCC risk. Analysis of tissues from patients with HCC confirmed a perturbed expression of PRDX2 in cancer. In vivo perturbation studies in mouse models for hepatocarcinogenesis driven by metabolic dysfunction-associated steatohepatitis showed that specific Prdx2 KO in hepatocytes improved metabolic liver functions, restored AMPK activity, and prevented HCC development by suppressing oncogenic signaling. Perturbation studies in HCC cell lines, a cell line-derived xenograft mouse model, and patient-derived HCC spheroids revealed that PRDX2 also mediates cancer initiation, cancer cell proliferation, and survival through its antioxidant activity. Targeting PRDX2 may therefore be a strategy to prevent HCC development in metabolic liver disease.
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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 | 0.000 |
| 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 | 0.001 |
| 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".