Investigating the effects of miR-526b and miR-655 on doxorubicin sensitivity in breast cancer
Bibliographic record
Abstract
Doxorubicin is an effective treatment for breast cancer, but drug resistance poses a significant challenge. Emerging regulators of drug resistance include microRNAs, small non-coding RNAs. Two miRNAs, miR-526b and miR-655, promote aggressive breast cancer phenotypes like cell proliferation, cancer stem cell upregulation, altered response to oxidative and metabolic stress and help tumor metastasis by activating COX-2/EP4/PI3K pathways. However, their impact on the effects of chemotherapeutic treatments remains unexplored. This study investigates the impact of overexpressing miR-526b and miR-655 on doxorubicin responses in breast cancer, focusing on pro-survival, apoptotic, and DNA-damage response pathways. We confirm the role of PI3K/Akt signalling in promoting cell survival and resistance in response to doxorubicin in vitro, though key differences exist between each cell line. High-throughput analysis of doxorubicin-exposed breast cancer cell lines, revealing novel mechanisms of miR-526b and miR-655. Overexpression of miR-526b and miR-655 may alter doxorubicin efficacy through mechanisms like DNA damage response changes, metabolic reprogramming, and immune pathway activation. Further investigation may uncover new therapeutic strategies to improve treatment efficacy and patient outcomes.
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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 | 0.001 |
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".