Cysteinyl leukotriene receptor expression pattern affects migration of breast cancer cells and survival of breast cancer patients
Bibliographic record
Abstract
The fact that breast cancer patients with local or distal dissemination exhibit decreased survival, promotes a search for novel mechanisms to suppress such tumor progression. Here, we have determined the expression of proinflammatory cysteinyl leukotriene receptors (CysLTRs) in breast tumor tissue and their signaling effect on breast cancer cell functions related to tumor progression. Patients with breast tumors characterized by high CysLT(1)R and low CysLT(2)R expression levels exhibited increased risk of cancer-induced death in univariate analysis for both the total patient group (hazard ratio [HR] = 2.88, 95% confidence interval [CI] = 1.11-7.41), as well as patients with large (>20 mm) tumors (HR = 5.08, 95% CI = 1.39-18.5). Multivariate analysis revealed that patients with large tumors exhibiting high CysLT(1)R and low CysLT(2)R expression levels had a significantly reduced survival, also when adjusted for established prognostic parameters (HR = 7.51, 95% CI = 1.83-30.8). In patients with large (>20 mm) tumors, elevated CysLT(2)R expression predicted an improved 5-year survival (log-rank test p = 0.04). Surprisingly, for longer time periods, this prognostic value was lost. This disappearance coincided with the termination of hormonal treatment. Tamoxifen preserved and even induced transcription of CysLT(2)R, but not CysLT(1)R, in estrogene receptor-positive MCF-7 breast cancer cells. This elevated CysLT(2)R expression decreased, even below the level of untreated cells, when tamoxifen was withdrawn. CysLT(2)R signaling reduced MCF-7 cell migration, but had no effect on either proliferation or apoptosis. Our data indicate that low CysLT(1)R together with high CysLT(2)R expression levels might be useful parameters in prognostication and treatment stratification of breast cancer patients.
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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 | 0.001 |
| 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".