Transforming growth factor β<SUB>1</SUB> selectively regulates ferritin gene expression in malignant H-ras-transformed fibrosarcoma cell lines
Bibliographic record
Abstract
Transforming growth factor beta1 is an important growth regulator in many cell types, usually exerting a negative effect on cellular growth. Inhibition of DNA synthesis and cell proliferation is frequently lost during malignant transformation, and in some cases, tumor cell proliferation is actually stimulated by TGF-beta1. The present study demonstrates a novel link between alterations in TGF-beta1 regulation during malignant conversion, and the expression of ferritin, an important activity involved in a number of biological functions including iron homeostasis and cell-growth control. A series of H-ras-transformed mouse 10 T 1/2 cell lines, exhibiting increasing malignant potential, was investigated for possible TGF-beta1-mediated changes in ferritin gene expression. Selective induction of gene expression was observed, since only H-ras-transformed cells with malignant potential exhibited marked elevations in ferritin gene expression, in particular, alterations in H-ferritin gene expression. The regulation of H-ferritin gene expression in response to TGF-beta1 did not involve alterations in transcription, but occurred through mechanisms of post-transcriptional stabilization of the H-ferritin mRNA. Additionally, evidence was obtained for a cycloheximide-sensitive regulator of H-ferritin gene expression, since the presence of this protein synthesis inhibitor increased H-ferritin message levels, and in combination with TGF-beta1, cooperated in an additive manner to augment H-ferritin gene expression. These results show for the first time that TGF-beta1 can regulate ferritin gene expression in malignant H-ras transformed cells, and suggest a mechanism for growth factor stimulation of malignant cells, in which early alterations in the control of H-ferritin gene expression are important.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".