c‐Rel–dependent Chk2 signaling regulates the DNA damage response limiting hepatocarcinogenesis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death. The NF-κB transcription factor family subunit c-Rel is typically protumorigenic; however, it has recently been reported as a tumor suppressor. Here, we investigated the role of c-Rel in HCC. APPROACH AND RESULTS: Histological and transcriptional studies confirmed expression of c-Rel in human patients with HCC, but low c-Rel expression correlated with increased tumor cell proliferation and mutational burden and was associated with advanced disease. In vivo , global ( Rel-/- ) and epithelial specific ( RelAlb ) c-Rel knockout mice develop more tumors, with a higher proliferative rate and increased DNA damage, than wild-type (WT) controls 30 weeks after N-diethylnitrosamine injury. However, tumor burden was comparable when c-Rel was deleted in hepatocytes once tumors were established, suggesting c-Rel signaling is important for preventing HCC initiation after genotoxic injury, rather than for HCC progression. In vitro , Rel-/- hepatocytes were more susceptible to genotoxic injury than WT controls. ATM-CHK2 DNA damage response pathway proteins were suppressed in Rel-/- hepatocytes following genotoxic injury, suggesting that c-Rel is required for effective DNA repair. To determine if c-Rel inhibition sensitizes cancer cells to chemotherapy, by preventing repair of chemotherapy-induced DNA damage, thus increasing tumor cell death, we administered single or combination doxorubicin and IT-603 (c-Rel inhibitor) therapy in an orthotopic HCC model. Indeed, combination therapy was more efficacious than doxorubicin alone. CONCLUSION: Hepatocyte c-Rel signaling limits genotoxic injury and subsequent HCC burden. Inhibiting c-Rel as an adjuvant therapy increased the effectiveness of DNA damaging agents and reduced HCC growth.
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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) | 0.003 | 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".