Genome‐wide transcriptome analyses reveal p53 inactivation mediated loss of miR‐34a expression in malignant peripheral nerve sheath tumours
Bibliographic record
Abstract
Malignant peripheral nerve sheath tumours (MPNSTs) are aggressive soft tissue tumours that occur either sporadically or in patients with neurofibromatosis type 1. The malignant transformation of the benign neurofibroma to MPNST is incompletely understood at the molecular level. We have determined the gene expression signature for benign and malignant PNSTs and found that the major trend in malignant transformation from neurofibroma to MPNST consists of the loss of expression of a large number of genes, rather than widespread increase in gene expression. Relatively few genes are expressed at higher levels in MPNSTs and these include genes involved in cell proliferation and genes implicated in tumour metastasis. In addition, a gene expression signature indicating p53 inactivation is seen in the majority of MPNSTs. Subsequent microRNA profiling of benign and malignant PNSTs indicated a relative down-regulation of miR-34a in most MPNSTs compared to neurofibromas. In vitro studies using the cell lines MPNST-14 (NF1 mutant) and MPNST-724 (from a non-NF1 individual) show that exogenous expression of p53 or miR-34a promotes apoptotic cell death. In addition, exogenous expression of p53 in MPNST cells induces miR-34a and other miRNAs. Our data show that p53 inactivation and subsequent loss of expression of miR-34a may significantly contribute to the MPNST development. Collectively, our findings suggest that deregulation of miRNAs has a potential role in the malignant transformation process in peripheral nerve sheath tumours.
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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".