A set of microRNAs coordinately controls tumorigenesis, invasion, and metastasis
Bibliographic record
Abstract
MicroRNA-mediated gene regulation has been implicated in various diseases, including cancer. This study examined the role of microRNAs (miRNAs) during tumorigenesis and malignant progression of pancreatic neuroendocrine tumors (PanNETs) in a genetically engineered mouse model. Previously, a set of miRNAs was observed to be specifically up-regulated in a highly invasive and metastatic subtype of mouse and human PanNET. Using functional assays, we now implicate different miRNAs in distinct phenotypes: miR-137 stimulates tumor growth and local invasion, whereas the miR-23b cluster enables metastasis. An algorithm, Bio-miRTa, has been developed to facilitate the identification of biologically relevant miRNA target genes and applied to these miRNAs. We show that a top-ranked miR-137 candidate gene, Sorl1 , has a tumor suppressor function in primary PanNETs. Among the top targets for the miR-23b cluster, Acvr1c/ALK7 has recently been described to be a metastasis suppressor, and we establish herein that it is down-regulated by the miR-23b cluster, which is crucial for its prometastatic activity. Two other miR-23b targets, Robo2 and P2ry1 , also have demonstrable antimetastatic effects. Finally, we have used the Bio-miRTa algorithm in reverse to identify candidate miRNAs that might regulate activin B, the principal ligand for ALK7, identifying thereby a third family of miRNAs—miRNA-130/301—that is congruently up-regulated concomitant with down-regulation of activin B during tumorigenesis, suggestive of functional involvement in evasion of the proapoptotic barrier. Thus, dynamic up-regulation of miRNAs during multistep tumorigenesis and malignant progression serves to down-regulate distinctive suppressor mechanisms of tumor growth, invasion, and metastasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".