IL-6/IL-6R pathway is a therapeutic target in chemoresistant ovarian cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION:: Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy worldwide and despite an initial response to therapeutic agents, the majority of patients have chemoresistant disease. There is no treatment strategy with proven efficacy against chemoresistant EOC and in this setting, overcoming therapy resistance is the key to successful treatment. METHODS:: This study aimed to investigate expression of interleukin-6 (IL-6) (IL-6) and IL-6 receptor (IL-6R) in a panel of the EOC cell lines. To achieve this, the expression of IL-6 and its receptor were compared in the EOC cells using quantitative reverse transcription polymerase chain reaction. MTT assay was performed to obtain chemosensitivity of the EOC cells. RESULTS:: In this report, we show that expressions of IL6 and IL6R are higher in therapy-resistant EOC cells compared to sensitive ones. Higher expression of IL6 and its receptor correlated with resistance to certain chemotherapeutic agents. Moreover, our findings showed that combination of tocilizumab (Actemra; Roche), an anti-IL-6R monoclonal antibody, with carboplatin synergistically inhibited growth and proliferation of the EOC cells and the most direct axis for IL-6 gene expression was NF-κB pathway. CONCLUSION:: Collectively, our findings suggest that blockade of the IL-6 signaling pathway with anti-IL-6 receptor antibody tocilizumab might resensitize the chemoresistant cells to the current chemotherapeutics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.008 | 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 it