Selective Usage of D-Type Cyclins by Ewing’s Tumors and Rhabdomyosarcomas
Bibliographic record
Abstract
The genetic mechanisms that control proliferation of childhood musculoskeletal malignancies, notably Ewing's tumor (ET) and rhabdomyosarcoma (RMS), remain largely unknown. Most human cancers appear to overexpress at least one of the G1 cyclins (cyclins D1, D2, D3, E1, and E2) to bypass normal regulation of cell cycle G1 progression. We compared the gene expression profiles of 7 ET and 13 RMS primary tumor samples and found overexpression of cyclin D1 in all 7 ET samples. In contrast, RMS samples expressed higher levels of cyclin D2, cyclin D3, and cyclin E1. This was confirmed by quantitative reverse transcription-polymerase chain reaction and Western blot. The relative roles of RAS-extracellular signal-regulated kinase 1/2 and phosphatidylinositol 3'-kinase (PI3K)-AKT pathways in the regulation of D-type cyclin expression in these tumors were then assessed. Inhibition of either pathway reduced expression of cyclins D1, D2, and D3 in RMS lines, whereas only PI3K inhibitors blocked cyclin D1, D2, and D3 expression in ET lines. Furthermore, PI3K-AKT appeared to regulate D-type cyclin transcription in RMS lines through FKHR and FKHRL1. Finally, the role of the ET-associated EWS-FLI1 fusion gene in regulating D cyclin expression was studied. Inhibition of EWS-FLI1 expression in the TC71 ET line decreased cyclin D1 levels but increased cyclin D3 levels. In contrast, induction of EWS-FLI1 expression in the RD RMS cell line increased cyclin D1 expression but decreased cyclin D3 expression. Our results demonstrate distinct regulation of D-type cyclins in ET and RMS and indicate that EWS-FLI1 can modulate the expression of D-type cyclins independent of cellular backgrounds.
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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) | 0.000 | 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 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".