Down-regulation of lncRNA-ATB inhibits epithelial–mesenchymal transition of breast cancer cells by increasing miR-141-3p expression
Bibliographic record
Abstract
Long noncoding RNA activated by transforming growth factor-beta (lnc-ATB) is abnormally expressed in a number of tumor types. The aim of this study was to investigate the expression of lnc-ATB and miR-141-3p, and to determine whether lnc-ATB can regulate epithelial-mesenchymal transition (EMT) by miR-141-3p in breast cancer. Here, we found that lnc-ATB was highly expressed, whereas there was low expression of miR-141-3p in breast cancer tissues and cells. Knockdown of lnc-ATB in two breast cancer cell lines (MDA-MB-231 and BT549) significantly increased miR-141-3p expression. Down-regulation of lnc-ATB resulted in a morphological change of breast cancer cells from spindle-like to a round shape, and in a remarkable inhibition of cell migration and invasion, which were reversed by miR-141-3p inhibitor. Furthermore, we demonstrated that lnc-ATB knockdown decreased ZEB1, ZEB2, N-cadherin, and vimentin expression, and promoted E-cadherin expression, while miR-141-3p inhibitor could reverse those effects. Moreover, we proved that miR-141-3p directly bound to the 3' untranslated region (UTR) of ZEB1 and ZEB2 and negatively regulated ZEB1 and ZEB2 expression. Taken together, our results show that knockdown of lnc-ATB significantly inhibits the EMT process of breast cancer cells by increasing the expression of miR-141-3p, indicating that lnc-ATB might serve as a novel therapeutic target for breast cancer.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".