Stimulation of MC38 tumor growth by insulin analog X10 involves the serine synthesis pathway
Bibliographic record
Abstract
Recent evidence suggests that type II diabetes is associated with increased risk and/or aggressive behavior of several cancers, including those arising from the colon. Concerns have been raised that endogenous hyperinsulinemia and/or exogenous insulin and insulin analogs might stimulate proliferation of neoplastic cells. However, the mechanisms underlying possible growth-promoting effects of insulin and insulin analogs in cancer cells in vivo, such as changes in gene expression, are incompletely described. We observed that administration of the insulin analog X10 significantly increased tumor growth and proliferation in a murine colon cancer model (MC38 cell allografts). Insulin and X10 altered gene expression in MC38 tumors in a similar fashion, but X10 was more potent in terms of the number of genes influenced and the magnitude of changes in gene expression. Many of the affected genes were annotated to metabolism, nutrient uptake, and protein synthesis. Strikingly, expression of genes encoding enzymes in the serine synthesis pathway, recently shown to be critical for neoplastic proliferation, was increased following treatment with insulin and X10. Using stable isotopic tracers and mass spectrometry, we confirmed that insulin and X10 increased glucose contribution to serine synthesis in MC38 cells. The data demonstrate that the tumor growth-promoting effects of insulin and X10 are associated with changes in expression of genes involved in cellular energy metabolism and reveal previously unrecognized effects of insulin and X10 on serine synthesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".