MiR-93 enhances angiogenesis and metastasis by targeting LATS2
Bibliographic record
Abstract
Here we report that miR-93, a miRNA in the miR-106B~25 cluster, a paralog of the miR-17-92 cluster, was significantly upregulated in human breast carcinoma tissues. We stably expressed miR-93 in the MT-1 human breast carcinoma cell line and found that tumors formed by the miR-93 cells contained more blood vessels than those formed by the control cells. Co-culture experiments indicated that the MT-1 cells displayed a high activity of adhesion with endothelial cells and could form larger and more tube-like structures with endothelial cells. Lung metastasis assays were performed in a mouse metastatic model, and it was found that expression of miR-93 promoted tumor cell metastasis to lung tissue. In cell culture, expression of miR-93 enhanced cell survival and invasion. We examined the potential target that mediated miR-93's effects and found that the large tumor suppressor, homology 2 (LATS2) was a target of miR-93. Higher levels of LATS2 were associated with cell death in the tumor mass. Silencing LATS2 expression promoted cell survival, tube formation and invasion, while ectopic expression of LATS2 decreased cell survival and invasion. These findings demonstrated that miR-93 promoted tumor angiogenesis and metastasis by suppressing LATS2 expression. Our results suggest that the inhibition of miR-93 function may be a feasible approach to repress tumor metastasis.
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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".