Cyclin-like proteins tip regenerative balance in the liver to favour cancer formation
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. A variety of factors can contribute to the onset of this disease, including viral infection, obesity, alcohol abuse and non-alcoholic fatty liver disease (NAFLD). These stressors predominantly introduce chronic inflammation leading to liver cirrhosis and finally the onset of HCC; however, approximately 20% of HCC cases arise in the absence of cirrhosis via a poorly defined mechanism. The atypical cyclin-like protein Spy1 is capable of overriding cell cycle checkpoints, promoting proliferation and has been implicated in HCC. We hypothesize that Spy1 promotes sustained proliferation making the liver more susceptible to accumulation of deleterious mutations, leading to the development of non-cirrhotic HCC. We report for the first time that elevation of Spy1 within the liver of a transgenic mouse model leads to enhanced spontaneous liver tumourigenesis. We show that the abundance of Spy1 enhanced fat deposition within the liver and decreased the inflammatory response. Interestingly, Spy1 transgenic mice have a significant reduction in fibrosis and sustained rates of hepatocyte proliferation, and endogenous levels of Spy1 are downregulated during the normal fibrotic response. Our results provide support that abnormal regulation of Spy1 protein drives liver tumorigenesis in the absence of elevated fibrosis and, hence, may represent a potential mechanism behind non-cirrhotic HCC. This work may implicate Spy1 as a prognostic indicator and/or potential target in the treatment of diseases of the liver, such as HCC. The cyclin-like protein Spy1 enhances lipid deposition and reduces fibrosis in the liver. Spy1 also promotes increased hepatocyte proliferation and onset of non-cirrhotic hepatocellular carcinoma (HCC). Thus, Spy1 may be used as a potential target in the treatment of HCC.
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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".