The prognostic signature of the somatic mutations in Ewing sarcoma: from a network view
Bibliographic record
Abstract
BACKGROUND: Ewing sarcoma is a malignant bone tumor mainly affecting teenagers and young adults. Its main driver mutation, the EWS-FLI1 fusion gene, has been identified more than 20 years ago, whereas its other somatic mutations have been just recently reported. METHODS: In this study, we organized the somatic mutations from 216 Ewing sarcoma cases into 216 individual protein-protein interaction networks by using interactome information. These mutation networks were then classified into five different clusters based on their structural similarities. The prognostic effect of mutation genes was evaluated according to their network features. RESULTS: The cases in cluster two exhibited remarkably high metastasis and mortality rates, and STAG2, TP53 and TTN were the three most significantly mutated genes in this cluster. Microarray data demonstrate that the expression of STAG2, TP53 and TTN are down-regulated in the EWS-FLI1-knockdown Ewing sarcoma cells. However, the mutation effect analysis shows that the somatic mutations in TTN are less damaging than those in STAG2 and TP53. The analyses of functional network modules further revealed that STAG2, TP53 and their interacting gene partners participate in the oncogenic-related biological modules such as cell cycle and regulation of transcription from RNA polymerase II promoter while TTN, TP53 and their interacting gene partners constitute the modules less relevant to oncogenesis. The results of Gene Ontology analyses demonstrated that the uniquely mutated genes associated with poor prognosis in Clusters 1, 4 and 5 were distinctively enriched in epidermal growth factor-related functions and phosphoproteins. CONCLUSIONS: Our study identified the highly lethal mutation combination cases and characterized the possible prognostic genes in Ewing sarcoma from a network perceptive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".