Growth factor receptor binding protein 14 inhibition triggers insulin-induced mouse hepatocyte proliferation and is associated with hepatocellular carcinoma
Bibliographic record
Abstract
Metabolic diseases such as obesity and type 2 diabetes are recognized as independent risk factors for hepatocellular carcinoma (HCC). Hyperinsulinemia, a hallmark of these pathologies, is suspected to be involved in HCC development. The molecular adapter growth factor receptor binding protein 14 (Grb14) is an inhibitor of insulin receptor catalytic activity, highly expressed in the liver. To study its involvement in hepatocyte proliferation, we specifically inhibited its liver expression using a short hairpin RNA strategy in mice. Enhanced insulin signaling upon Grb14 inhibition was accompanied by a transient induction of S-phase entrance by quiescent hepatocytes, indicating that Grb14 is a potent repressor of cell division. The proliferation of Grb14-deficient hepatocytes was cell-autonomous as it was also observed in primary cell cultures. Combined Grb14 down-regulation and insulin signaling blockade using pharmacological approaches as well as genetic mouse models demonstrated that Grb14 inhibition-mediated hepatocyte division involved insulin receptor activation and was mediated by the mechanistic target of rapamycin complex 1-S6K pathway and the transcription factor E2F1. In order to determine a potential dysregulation in GRB14 gene expression in human pathophysiology, a collection of 85 human HCCs was investigated. This revealed a highly significant and frequent decrease in GRB14 expression in hepatic tumors when compared to adjacent nontumoral parenchyma, with 60% of the tumors exhibiting a reduced Grb14 mRNA level. Conclusion: Our study establishes Grb14 as a physiological repressor of insulin mitogenic action in the liver and further supports that dysregulation of insulin signaling is associated with 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) | 0.002 | 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".