A MYOD-SKP2 axis boosts tumorigenesis in fusion negative rhabdomyosarcoma by preventing differentiation through p57Kip2 targeting
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma (RMS) is a pediatric mesenchymal-derived malignancy encompassing Fusion Positive (FP)-RMS expressing PAX3/7-FOXO1 and Fusion Negative (FN)-RMS often mutated in the RAS pathway. RMS expresses the master myogenic transcription factor MYOD that, paradoxically, in the tumor context is essential for tumor cell growth and survival. We identify here SKP2, an oncogenic E3-ubiquitin ligase of the SCF/CRL1 complex, as a critical driver of tumorigenesis downstream of MYOD in FN-RMS. SKP2 is overexpressed in RMS at the highest levels among several adult and pediatric cancers and its expression is maintained by MYOD through an intronic enhancer within the gene, in loop with its promoter. Mechanistically, in FN-RMS cells SKP2 functions by directly targeting p27Kip1 and p57Kip2 promoting their degradation. SKP2 knockdown causes cell cycle arrest by enhancing p27Kip1 and promotes differentiation by increasing p57Kip2, which in turn stabilizes MYOD. This leads to MYOD and MYOG increase and unlocks a myogenic program resulting in de novo expression of terminal muscle differentiation markers and cell fusion. SKP2 depletion strongly affects stemness and anchorage-independence features and prevents tumor growth. The investigational NEDDylation inhibitor MLN4924 (Pevonedistat) hampers SKP2 functions restraining FN-RMS cell survival and tumor growth. Our results uncover a MYOD-SKP2 axis crucial for the crosstalk between transcriptional and post-translational mechanisms that contribute to FN-RMS tumorigenesis and broaden the understanding of MYOD function. Furthermore, they suggest inhibition of NEDDylation as a potential therapeutic approach in this tumor.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".