Insulin-Like Growth Factor-I Regulates the Liver Microenvironment in Obese Mice and Promotes Liver Metastasis
Bibliographic record
Abstract
Among the mechanisms implicated in the tumor-promoting effects of obesity, signaling by insulin-like growth factor-I (IGF-I) and insulin has received considerable attention. However, the emerging realization that obesity is associated with chronic inflammation has prompted other consideration of how the IGF-I axis may participate in cancer progression. In the present study, we used two mouse models of chronic (LID) and inducible (iLID) igf-1 gene deficiency in the liver to investigate the role of IGF-I in regulating the host microenvironment and colorectal carcinoma growth and metastasis in obese mice. Obese mice had a heightened inflammatory response in the liver, which was abolished in mice with chronic IGF-I deficiency (LID). In control animals changes to the hepatic microenvironment associated with obesity sustained the presence of tumor cells in the liver and increased the incidence of hepatic metastases after intrasplenic/portal inoculation of colon carcinoma cells. These changes did not occur in LID mice with chronic IGF-1 deficiency. In contrast, these changes occurred in iLID mice with acute IGF-1 deficiency, in the same manner as the control animals, revealing a fundamental difference in the nature of the requirement for IGF-1 on tumor growth and metastasis. In the setting of obesity, our findings imply that IGF-1 is critical to activate and sustain an inflammatory response in the liver that is needed for hepatic metastasis, not only through direct, paracrine effect on tumor cell growth, but also through indirect effects involving the tumor microenvironment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".