Janus Kinase 2 (JAK2) Dissociates Hepatosteatosis from Hepatocellular Carcinoma in Mice
Bibliographic record
Abstract
promotes liver cancer, and the molecular mechanisms that control the progression in this disease spectrum remain largely elusive. The Janus kinase signal transducers and activators of transcription (JAK-STAT) pathway mediates signal transduction by numerous cytokines that regulate inflammation and may contribute to hepatocarcinogenesis. Mice with hepatocyte-specific deletion of JAK2 (L-JAK2 KO) develop extensive fatty liver spontaneously. We show here that this simple steatosis was insufficient to drive carcinogenesis. In fact, L-JAK2 KO mice were markedly protected from chemically induced tumor formation. Using the methionine choline-deficient dietary model to induce steatohepatitis, we found that steatohepatitis development was completely arrested in L-JAK2 KO mice despite the presence of steatosis, suggesting that JAK2 is the critical factor required for inflammatory progression in the liver. In line with this, L-JAK2 KO mice exhibited attenuated inflammation after chemical carcinogen challenge. This was associated with increased hepatocyte apoptosis without elevated compensatory proliferation, thus thwarting expansion of transformed hepatocytes. Taken together, our findings identify an indispensable role of JAK2 in hepatocarcinogenesis through regulating critical inflammatory pathways. Targeting the JAK-STAT pathway may provide a novel therapeutic option for the treatment of hepatocellular carcinoma.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".