The effect of IL-1β on MRP2 expression and tamoxifen toxicity in MCF-7 breast cancer cells
Bibliographic record
Abstract
BACKGROUND: Chronic inflammation is considered to be a risk factor for carcinogenesis, tumor development and metastasis by providing tumor-related factors. OBJECTIVES: We aimed to evaluate the effect of cytokine interleukin-1β (IL-1β) as a key mediator of inflammation on multidrug resistance associated protein 2 (MRP2) expression and tamoxifen toxicity in estrogen receptor positive (ER+) MCF-7 breast cancer cells. METHODS: The effects of IL-1β on tamoxifen toxicity following 20-day treatment of MCF-7 cells with IL-1β and/or 17β-estradiol (E2) were measured by MTT assay. Furthermore, the effects of IL-1β and/or E2 on the mRNA expression and protein levels of MRP2 and NF-κB (p65) in breast cancer cells were evaluated by QRT-PCR and Western blot analysis, respectively. RESULTS: Treatment of breast cancer cells with IL-1β+ E2 decreased the sensitivity to 4-OH tamoxifen compared to both E2-treated and untreated cells. The mRNA expression levels of MRP2 and NF-κB (p65) were significantly increased following treatment with IL-1β+ E2, compared to control. In addition, breast cancer cells treatment with IL-1β+ E2 increased protein expression of MRP2 and it had no significant effect on NF-κB/p65 protein expression in these cells. CONCLUSION: Increased expression of mRNA and protein level of MRP2 following 20-day treatment of MCF-7 cells with IL-1β + E2 might be a possible elucidation for the increased tamoxifen resistance which was observed in these cells. More researches are essential to clarify the molecular mechanisms of inflammation on drug-resistance in the tumor environment in order to reducing or eliminating chemotherapy resistance and developing more effective treatment strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".