MicroRNA 376c enhances ovarian cancer cell survival by targeting activin receptor-like kinase 7: implications for chemoresistance
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that have important roles in gene regulation. We have previously reported that activin receptor-like kinase 7 (ALK7) and its ligand, Nodal, induce apoptosis in human epithelial ovarian cancer cells. In this study, we examined the regulation of ALK7 by miRNAs and demonstrate that miR-376c targets ALK7. Ectopic expression of miR-376c significantly increased cell proliferation and survival, enhanced spheroid formation and blocked Nodal-induced apoptosis. Interestingly, overexpression of miR-376c blocked cisplatin-induced cell death, whereas anti-miR-376c enhanced the effect of cisplatin. These effects of miR-376c were partially compensated by the overexpression of ALK7. Moreover, in serous carcinoma samples taken from ovarian cancer patients who responded well to chemotherapy, strong ALK7 staining and low miR-376c expression was detected. By contrast, ALK7 expression was weak and miR-376c levels were high in samples from patients who responded poorly to chemotherapy. Finally, treatment with cisplatin led to an increase in expression of mRNA encoding Nodal and ALK7 but a decrease in miR-376c levels. Taken together, these results demonstrate that the Nodal-ALK7 pathway is involved in cisplatin-induced cell death in ovarian cancer cells and that miR-376c enhances proliferation, survival and chemoresistance by targeting, at least in part, ALK7.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".