IL-6/IL-6R pathway is a therapeutic target in chemoresistant ovarian cancer
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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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".