Involvement of microRNA-451 in resistance of the MCF-7 breast cancer cells to chemotherapeutic drug doxorubicin
Bibliographic record
Abstract
Many chemotherapy regiments are successfully used to treat breast cancer; however, often breast cancer cells develop drug resistance that usually leads to a relapse and worsening of prognosis. We have shown recently that epigenetic changes such as DNA methylation and histone modifications play an important role in breast cancer cell resistance to chemotherapeutic agents. Another mechanism of gene expression control is mediated via the function of small regulatory RNA, particularly microRNA (miRNA); its role in cancer cell drug resistance still remains unexplored. In the present study, we investigated the role of miRNA in the resistance of human MCF-7 breast adenocarcinoma cells to doxorubicin (DOX). Here, we for the first time show that DOX-resistant MCF-7 cells (MCF-7/DOX) exhibit a considerable dysregulation of the miRNAome profile and altered expression of miRNA processing enzymes Dicer and Argonaute 2. The mechanistic link of miRNAome deregulation and the multidrug-resistant phenotype of MCF-7/DOX cells was evidenced by a remarkable correlation between specific miRNA expression and corresponding changes in protein levels of their targets, specifically those ones that have a documented role in cancer drug resistance. Furthermore, we show that microRNA-451 regulates the expression of multidrug resistance 1 gene. More importantly, transfection of the MCF-7/DOX-resistant cells with microRNA-451 resulted in the increased sensitivity of cells to DOX, indicating that correction of altered expression of miRNA may have significant implications for therapeutic strategies aiming to overcome cancer cell resistance.
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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".