Aberrant Expression of CYP2W1 in Pediatric Soft Tissue Sarcomas: Clinical Significance and Potential as a Therapeutic Target
Bibliographic record
Abstract
Pediatric soft-tissue sarcomas (STSs) are aggressive malignancies with poor prognoses, particularly in recurrent and metastatic cases. Standard therapies, such as cytotoxic chemotherapy, offer limited survival benefits and carry significant toxicities, underscoring the urgent need for innovative therapeutic approaches. CYP2W1, a tumor-specific monooxygenase enzyme, has emerged as a promising therapeutic target due to its aberrant expression in various cancers. However, its role in pediatric STSs remains poorly understood. This study evaluated CYP2W1 expression in 42 pediatric STS samples across seven histological subtypes using qPCR and Western blot analyses. High CYP2W1 expression was detected in 69% of tumor samples at the mRNA level and in 40.5% at the protein level, compared to absent or negligible expression in matched normal tissues (p < 0.001). Synovial sarcoma and rhabdomyosarcoma subtypes exhibited the highest CYP2W1 protein expression, at 70% and 62.5%, respectively. Furthermore, CYP2W1 expression was significantly associated with higher histological grade, advanced tumor stage, and a trend toward reduced overall survival (p = 0.082). These findings indicate that CYP2W1 is aberrantly expressed in a subset of pediatric STSs, contributing to tumor aggressiveness and highlighting its potential as a novel therapeutic target for these challenging malignancies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".