Data from Inflammatory Signaling and Aryl Hydrocarbon Receptor Mediate Synergistic Induction of Interleukin 6 in MCF-7 Cells
Bibliographic record
Abstract
Abstract The pleiotropic cytokine interleukin 6 (IL-6) is involved in immune cell homeostasis. Additionally, IL-6 expression and signaling in tumor cells have been shown to elicit both protumor and antitumor properties. There is a plethora of mechanistic knowledge regarding how IL-6 signal transduction translates to biological responses. However, there is little understanding as to what factors control IL-6 expression within a tumor cell environment. The studies presented herein show that, in MCF-7 breast and ECC-1 endocervical cancer cells, the stimulation of aryl hydrocarbon receptor (AHR) activity, in combination with IL-1β or phorbol 12-myristate 13-acetate (PMA) treatment, results in a marked synergistic induction of IL-6 levels over what is seen without AHR activation. Chromatin immunoprecipitation experiments suggest that the regulation of IL-6 mRNA expression occurs at the chromatin level, as AHR presence on the IL-6 promoter was observed in response to treatment with AHR ligand. Synergistic induction of IL-6 expression was sustained for 72 hours, with accumulation of IL-6 protein reaching levels 4.8-fold above IL-1β treatment alone. In addition, transcriptional regulation of the prototypic AHR responsive gene Cyp1a1 was negatively regulated by PMA and IL-1β treatment. Silencing of RELA expression alleviated IL-1β–mediated repression of AHR transcriptional activity, whereas PMA-mediated repression was maintained. Additionally, small interfering RNA studies reveal that AHR and RELA are necessary for synergistic induction of IL-6. The findings presented here reveal the AHR as a potential therapeutic target for selective modulation of IL-6 expression in some tumor cell types. The data also suggest a possible previously unrecognized mechanism of AHR-mediated tumor promotion. [Cancer Res 2008;68(10):3609–17]
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.007 | 0.002 |
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".