miR-138-5p induces aggressive traits by targeting Trp53 expression in murine melanoma cells, and correlates with poor prognosis of melanoma patients
Bibliographic record
Abstract
Deregulation of miRNAs contributes to the development of distinct cancer types, including melanoma, an aggressive form of skin cancer characterized by high metastatic potential and poor prognosis. The expression of a set of 580 miRNAs was investigated in a model of murine melanoma progression, comprising non-metastatic (4C11-) and metastatic melanoma (4C11+) cells. A significant increase in miR-138-5p expression was found in the metastatic 4C11+ melanoma cells compared to 4C11-, which prompted us to investigate its role in melanoma aggressiveness. Functional assays, including anoikis resistance, colony formation, collective migration, serum-deprived growth capacity, as well as in vivo tumor growth and experimental metastasis were performed in 4C11- cells stably overexpressing miR-138-5p. miR-138-5p induced an aggressive phenotype in mouse melanoma cell lines leading to increased proliferation, migration and cell viability under stress conditions. Moreover, by overexpressing miR-138-5p, low-growing and non-metastatic 4C11- cells became highly proliferative and metastatic in vivo, similar to the metastatic 4C11+ cells. Luciferase reporter analysis identified the tumor suppressor Trp53 as a direct target of miR-138-5p. Using data sets from independent melanoma cohorts, miR-138-5p and P53 expression were also found deregulated in human melanoma samples, with their levels negatively and positively correlated with prognosis, respectively. Our data shows that the overexpression of miR-138-5p contributes to melanoma metastasis through the direct suppression of Trp53.
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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".