Enhancement of Breast Cancer Cell Aggressiveness by lncRNA H19 and its Mir-675 Derivative: Insight into Shared and Different Actions
Bibliographic record
Abstract
Breast cancer is a major public health problem and the leading world cause of women death by cancer. Both the recurrence and mortality of breast cancer are mainly caused by the formation of metastasis. The long non-coding RNA H19, the precursor of miR-675, is involved in breast cancer development. The aim of this work was to determine the implication but, also, the relative contribution of H19 and miR-675 to the enhancement of breast cancer metastatic potential. We showed that both H19 and miR-675 increase the invasive capacities of breast cancer cells in xenografted transgenic zebrafish models. In vitro, H19 and miR-675 enhance the cell migration and invasion, as well as colony formation. H19 seems to induce the epithelial-to-mesenchymal transition (EMT), with a decreased expression of epithelial markers and an increased expression of mesenchymal markers. Interestingly, miR-675 simultaneously increases the expression of both epithelial and mesenchymal markers, suggesting the induction of a hybrid phenotype or mesenchymal-to-epithelial transition (MET). Finally, we demonstrated for the first time that miR-675, like its precursor H19, increases the stemness properties of breast cancer cells. Altogether, our data suggest that H19 and miR-675 could enhance the aggressiveness of breast cancer cells through both common and different mechanisms.
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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".