MiR-181a-5p inhibits uveal melanoma development by targeting GNAQ and AKT3.
Bibliographic record
Abstract
Uveal melanoma (UM) is the most common primary intraocular malignant tumor type in adults. Even after the treatment of the ocular tumor, the prognosis of patients with metastasis remains poor. Hence, an urgent unmet need exists to identify novel approaches to treat advanced UM. Previous studies have revealed G subunit alpha Q and alpha 11 (GNAQ/11) mutations in more than 85% of patients with UM, thus indicating the importance of GNAQ and downstream signaling pathways in UM occurrence. Here, we demonstrate that microRNA (miR)-181a-5p, a small non-coding RNA, effectively inhibited the viability, proliferation, and colony formation but induced apoptosis of UM cells. Furthermore, silencing GNAQ or AKT3 mimicked the anti-UM effects of miR-181a-5p, whereas overexpression of GNAQ or AKT3 rescued the anti-UM effects induced by miR-181a-5p. In addition, miR-181a-5p had a stronger effect in decreasing the viability of GNAQ mutant than GNAQ wild-type cells. Moreover, miR-181a-5p suppressed the total expression and phosphorylation of members of the ERK and PI3K/AKT/mTOR signaling pathways. Importantly, miR-181a-5p potently inhibited the growth of UM xenografts in nude mice. MiR-181a-5p also decreased the expression of Ki67, GNAQ, and AKT3, and induced the expression of cleaved-caspase3 in UM tumors. These results suggest that miR-181a-5p inhibits UM development by targeting GNAQ and AKT3.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".