Colocalization of somatostatin receptors and epidermal growth factor receptors in breast cancer cells
Bibliographic record
Abstract
BACKGROUND: Somatostatin receptor (SSTR) expression is positively correlated with tumor size and inversely correlated with epidermal growth factor receptor (ErbB) levels and tumor differentiation. In the present study, we compared SSTR1-5 and ErbB1-4 mRNA and protein expression in two breast cancer cell lines: MCF-7 (ER+) and MDA-MB-231 (ERalpha-). RESULTS: All five SSTRs and four ErbBs were variably expressed as both cell surface and cytoplasmic proteins. In both cell lines, SSTR4 and SSTR1 were highly expressed, followed by SSTR2 and SSTR5 with SSTR3 being the least expressed subtype, at the protein level. ErbBs were variably expressed with ErbB1 as the predominant subtype in both cell lines. ErbB1 is followed by ErbB3, ErbB2 and ErbB4 in MCF-7 at both the protein and mRNA levels. In MDA-MB-231 cells, ErbB1 is followed by ErbB2, ErbB4 and ErbB3. Our results indicate significant correlations at the level of mRNA and protein expression in a cell and receptor-specific manner. Using indirect immunofluorescence, we found that, in MCF-7 cells, SSTR5 was the most prominent subtype coexpressed with ErbBs followed by SSTR3, SSTR4, SSTR1 and SSTR2, respectively. In MDA-MB-231 cells, SSTR1 colocalized strongly with ErbBs followed by SSTR5, SSTR4, SSTR3 and SSTR2. ErbBs displayed higher levels of colocalization amongst themselves in MCF-7 cells than in MDA-MB-231 cells. CONCLUSION: These findings may explain the poor response to endocrine therapy in ER-cancer. Differential distribution of SSTR subtypes with ErbBs in breast cancer cells in a receptor-specific manner may be considered as a novel diagnosis for breast tumors.
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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 | 0.001 |
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".