The role of p53-microRNA 200-Moesin axis in invasion and drug resistance of breast cancer cells
Bibliographic record
Abstract
This study aimed to analyze the expression of microRNAs in relation to p53 status in breast cancer cells and to delineate the role of Moesin in this axis. We used three isogenic breast carcinoma cell lines MCF7 (with wild-type p53), 1001 (MCF7 with mutated p53), and MCF7-E6 (MCF7 in which p53 function was disrupted). MicroRNA expression was analyzed using microarray analysis and confirmed by real-time polymerase chain reaction. The 1001 clone with mutant p53 showed 22 upregulated and 25 downregulated microRNAs. The predicted targets of these 47 microRNAs were >700 human genes belonging to interesting functional groups such as stem cell development and maintenance. The most significantly downregulated microRNAs in the p53-mutant cell line were from the miR-200 family. We focused on miR-200c which targets many transcripts involved in epithelial-to-mesenchymal transition including Moesin. We found that Moesin was expressed in 1001 but not in its p53 wild-type parental MCF7 consistent with the observed mesenchymal features in the 1001, such as vimentin positivity, E-cadherin negativity, and ZEB1 positivity in addition to the morphological changes. After Moesin silencing, the p53-mutant cells 1001 reverted from mesenchymal-to-epithelial phenotype and showed subtle reduction in migration and invasion and loss of ZEB1 and SNAIL expression. Interestingly, Moesin silencing restored the 1001 sensitivity to Doxorubicin. These results indicate that loss of miR-200c, as a consequence of p53 mutation, can upregulate Moesin oncogene and thus promote carcinogenesis. Moesin may play a role in metastasis and drug resistance of breast cancer.
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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".