Iron specific growth inhibition of Burkitt's lymphoma cells in vitro, associated with a decrease in translocated <i>c‐myc</i> expression
Bibliographic record
Abstract
The cellular proto-oncogene c-myc is an important transcription factor that plays a role in several cellular activities such as proliferation, differentiation, and apoptosis. It follows that regulation of c-myc expression is crucial for maintaining cell integrity. Amplification or translocation can convert this proto-oncogene into an activated oncogene, thereby deregulating c-myc expression. Changes in the expression of c-myc leading to malignant cell growth and tumor progression can also be triggered by extrinsic factors. It has been reported that iron can increase cell proliferation, mainly by stimulating DNA synthesis as well as by enhancing c-myc expression. Here, we studied the effect of iron on cells in which c-myc expression is deregulated by either chromosomal translocation or gene amplification. When added to Burkitt's lymphoma cell lines, iron markedly inhibits cell proliferation. This effect is mediated by a cell cycle arrest in G2/M, followed by an important decrease in c-myc expression, whereas no effect could be observed in a cell line harboring amplified c-myc. Moreover, the down-regulation of c-myc expression, which is independent from cell cycle blockade, leads to cell death by apoptosis. Collectively, our results suggest the existence of a novel iron-dependent cell cycle regulatory mechanism involving modulation of translocated c-myc gene expression.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".