MicroRNA‐10b regulates tumorigenesis in neurofibromatosis type 1
Bibliographic record
Abstract
MicroRNAs (miRNAs) are frequently deregulated in human tumors, and play important roles in tumor development and progression. The pathological roles of miRNAs in neurofibromatosis type 1 (NF1) tumorigenesis are largely unknown. We demonstrated that miR-10b was up-regulated in primary Schwann cells isolated from NF1 neurofibromas and in cell lines and tumor tissues from malignant peripheral nerve sheath tumors (MPNSTs). Intriguingly, a significantly high level of miR-10b correlated with low neurofibromin expression was found in a neuroectodermal cell line: Ewing's sarcoma SK-ES-1 cells. Antisense inhibiting miR-10b in NF1 MPNST cells reduced cell proliferation, migration and invasion. Furthermore, we showed that NF1 mRNA was the target for miR-10b. Overexpression of miR-10b in 293T cells suppressed neurofibromin expression and activated RAS signaling. Antisense inhibition of miR-10b restored neurofibromin expression in SK-ES-1 cells, and decreased RAS signaling independent of neurofibromin in NF1 MPNST cells. These results suggest that miR-10b may play an important role in NF1 tumorigenesis through targeting neurofibromin and RAS signaling.
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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 | 0.001 |
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".