Effect of caveolin-1 on Stat3-ptyr705 levels in breast and lung carcinoma cells
Bibliographic record
Abstract
We recently demonstrated that Cav1 (caveolin-1) is a negative regulator of Stat3 (signal transducer and activator of transcription-3) activity in mouse fibroblasts and human lung carcinoma SHP77 cells. We now examined whether the cellular context may affect their levels as well as the relationship between them, by assessing Cav1 and Stat3-ptyr705 amounts in different cell lines. In MDA-MB-231, A549, and HaCat cells, Cav1 levels were high and Stat3-ptyr705 levels were low, consistent with the notion of a negative effect of endogenous Cav1 on Stat3-ptyr705 levels in these lines. In addition, manipulation of Cav1 levels revealed a negative effect in MCF7 and mouse fibroblast cells, while Cav1 upregulation induced apoptosis in MCF7 cells. In contrast, however, line MRC9 had high Cav1 and high Stat3-ptyr705 levels, indicating that high Cav1 is insufficient to reduce Stat3-ptyr705 levels in this line. MCF7 and LuCi6 cells had very low Cav1 and Stat3-ptyr705 levels, indicating that the low Stat3-ptyr705 can be independent from Cav1 levels altogether. Our results reveal a further level of complexity in the relationship between Cav1 and Stat3-ptyr705 than previously thought. In addition, we demonstrate that in a feedback loop, Stat3 inhibition upregulates Cav1 in HeLa cells but not in other lines tested.
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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".