SnoN expression is differently regulated in microsatellite unstable compared with microsatellite stable colorectal cancers
Bibliographic record
Abstract
BACKGROUND: SnoN is an important regulator of the transforming growth factor beta (TGFbeta) signalling pathway and has been shown to exhibit both tumour promotion and suppression activity. METHODS: To further explore the role of this complex molecule in colorectal tumorigenesis, we examined 52 paired normal and tumour colorectal specimens stratified by level of microsatellite instability; 18 with high-level microsatellite instability (MSI-H) and 34 microsatellite stable (MSS). SnoN transcript expression was quantitated by real-time PCR and analysed with respect to clinical indicators of prognosis. RESULTS: Within the MSI-H subgroup, SnoN was commonly either up-regulated (6/18, 33%) or down-regulated (7/18, 39%). A significantly different distribution of SnoN expression was observed in MSS cancers compared with MSI-H (P < or = 0.001). Whilst 17/34 (50%) of MSS tumours demonstrated up-regulation, none showed down-regulated expression. Within the MSI-H subgroup, up-regulation was significantly correlated with lack of repeat tract mutation in the TGFbetaRII gene (P < or = 0.025), suggesting that SnoN is more frequently up-regulated in the presence of functional TGFbeta signalling. CONCLUSION: Together these data support the notion that SnoN has both oncogenic and tumour suppressive properties depending on other genetic changes within the tumour, and that the MSI-H pathway of colorectal tumorigenesis presents an excellent model for the study of these opposing functions.
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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 | 0.001 | 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".