Differential regulation of somatostatin receptors 1 and 2 mRNA and protein expression by tamoxifen and estradiol in breast cancer cells.
Bibliographic record
Abstract
Somatostatin (SST) inhibition of hormone hypersecretion from tumors is mediated by somatostatin receptors (SSTRs). SSTRs also play an important role in controlling tumor growth through specific antiproliferative actions. These receptors are well expressed in numerous normal and tumor tissues and are susceptible to regulation by a variety of factors. Estradiol, a potent trophic and mitogenic hormone in its target tissues, is known to modulate the expression of SST and its receptors. Accordingly, in the present study, we determined the effects of tamoxifen, a selective estrogen receptor (ER) modulator (SERM), and estradiol on SSTR1 and SSTR2 expression at the mRNA and protein levels in ER-positive and -negative breast cancer cells. We found that SSTR1 was upregulated by tamoxifen in a dose-dependent manner but no effect was seen with estradiol. In contrast, SSTR2 was upregulated by both tamoxifen and estradiol. Combined treatment caused suppression of SSTR1 below control levels but had no significant effect on SSTR2. Treatment with SSTR1-specific agonist was significantly more effective in suppressing cell proliferation of cells pre-treated with tamoxifen. Taking these data into consideration, we suggest that tamoxifen and estradiol exert variable effects on SSTR1 and SSTR2 mRNA and protein expression and distributional pattern of the receptors. These changes are cell subtype-specific and affect the ability of SSTR agonists to inhibit cell proliferation.
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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".