In Vivo miRNA Decoy Screen Reveals miR-124a as a Suppressor of Melanoma Metastasis
Bibliographic record
Abstract
Melanoma is a highly prevalent cancer with an increasing incidence worldwide and high metastatic potential. Brain metastasis is a major complication of the disease, as more than 50% of metastatic melanoma patients eventually develop intracranial disease. MicroRNAs (miRNAs) have been found to play an important role in the tumorigenicity of different cancers and have potential as markers of disease outcome. Identification of relevant miRNAs has generally stemmed from miRNA profiling studies of cells or tissues, but these approaches may have missed miRNAs with relevant functions that are expressed in subfractions of cancer cells. We performed an unbiased in vivo screen to identify miRNAs with potential functions as metastasis suppressors using a lentiviral library of miRNA decoys. Notably, we found that a significant fraction of melanomas that metastasized to the brain carried a decoy for miR-124a, a miRNA that is highly expressed in the brain/neurons. Additional loss- and gain-of-function in vivo validation studies confirmed miR-124a as a suppressor of melanoma metastasis and particularly of brain metastasis. miR-124a overexpression did not inhibit tumor growth in vivo, underscoring that miR-124a specifically controls processes required for melanoma metastatic growth, such as seeding and growth post-extravasation. Finally, we provide proof of principle of this miRNA as a promising therapeutic agent by showing its ability to impair metastatic growth of melanoma cells seeded in distal organs. Our efforts shed light on miR-124a as an antimetastatic agent, which could be leveraged therapeutically to impair metastatic growth and improve patient survival.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".