Expression of miR-136 is associated with the primary cisplatin resistance of human epithelial 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
MicroRNAs (miRNAs) are involved in regulating the response of cancer cells to various therapeutic interventions, yet their involvement in the chemoresistance of human epithelial ovarian cancer is not fully understood. We found that miR-136 was significantly downregulated in specimens from patients with chemoresistant epithelial ovarian cancer. In the present study, we aimed to clarify the role of miR-136 in regulating the chemoresistance of ovarian cancer. Thirty-four tumor bank specimens and 2 well-established human ovarian cancer cell lines, C13 and OV2008, were used. We found that miR-136 expression was significantly reduced in primary platinum-resistant patients and the ovarian cancer OVC cell line. Enforced expression of miR-136 decreased the chemoresistance to cisplatin in OVC cells through inhibition of cell survival. In addition, we found no association between miR-136 and migration or invasion potential in the ovarian cancer cell lines. However, in the platinum-resistant C13 cell line, the overexpression of miR-136 markedly promoted an apoptotic response to cisplatin. Furthermore, the levels of adducts corrected with their extent of DNA damage/repair, in terms of the percentage of DNA in comet tails, tail length, tail moment (TM), and olive tail moment (OTM), revealed that miR-136 is essential for the repair of cisplatin-induced DNA damage. Our findings suggest that miR-136 may function as an anti-oncogene and deficiency of miR-136 expression in ovarian cancer can induce chemoresistance at least in part by downregulating apoptosis and promoting the repair of cisplatin-induced DNA damage. Thus, miR-136 may provide a biomarker for predicting the chemosensitivity to cisplatin in patients with epithelial ovarian cancer.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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