TRAM‐34 stimulates the proliferation of breast cancer cells via activation of estrogen receptors
Bibliographic record
Abstract
We have investigated the effects of specific K + channel inhibitors on basal and estrogen (E2)‐stimulated proliferation of breast cancer cells. Using the mammary adenocarcinoma cell line MCF‐7 we assayed cell proliferation by [ 3 H]‐thymidine incorporation in the absence or presence of various K + channel inhibitors with or without E2. Inhibitors of hEAG and hIK K + channels inhibited basal, but not E2‐stimulated proliferation of MCF‐7 cells. TRAM‐34, a specific inhibitor of hIK channels increased cell proliferation at intermediate concentrations (3‐10 µM), whereas at higher concentrations (20‐30 µM) TRAM‐34 decreased cell proliferation. The mitogenic effect of TRAM‐34 was blocked by the estrogen receptor antagonist ICI182,780. TRAM‐34 also (i) increased progesterone receptor mRNA expression, (ii) decreased estrogen receptor‐α mRNA expression, and (iii) reduced the binding of radiolabelled E2 to MCF‐7 estrogen receptor protein, in each case mimicking the effects of E2. Our results demonstrate that K + channels hEAG and hIK play a role in basal, but not E2‐stimulated MCF‐7 cell proliferation. TRAM‐34, as well as inhibiting hIK, interacts directly with the estrogen receptor and mimics the effects of E2 on MCF‐7 cell proliferation and gene expression. Our finding that TRAM‐34 may be able to activate the estrogen receptor suggests a novel action of this supposedly specific K + channel inhibitor. (NSHRF, CBCF)
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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) | 0.002 | 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".