No evidence for mutations or altered expression of the <i>Suppressor of Fused</i> gene <i>(SUFU)</i> in primitive neuroectodermal tumours
Bibliographic record
Abstract
The sonic hedgehog (Shh) and the Wnt signalling pathways are involved in the development of medulloblastomas (MBs), the most frequent malignant brain tumours in children. Components of these two developmental and cancer-associated pathways, including (Patched) PTCH, SMOH, adenomatous polyposis coli (APC), beta-catenin and AXIN1 show somatic mutations in sporadic MBs. In this study we analysed SUFU (human Suppressor of Fused), which acts as a negative regulator of both the Shh and Wnt signalling pathways and therefore represents a putative tumour suppressor gene, to find out if it is also involved in the pathogenesis of sporadic MBs. We screened 145 primitive neuroectodermal tumours (PNETs) including 90 classic MBs, 42 of the desmoplastic variant and two medullomyoblastomas as well as 11 MB cell lines for mutations using single-strand conformational polymorphism (SSCP) and sequencing analysis. 18% of the MBs exhibited allelic losses on chromosome 10q. In contrast to a previous report, in which truncating mutations of SUFU have been identified in 9% of MBs, we were not able to identify somatic mutations of SUFU in our large tumour panel. We uncovered single nucleotide polymorphisms (SNPs) in exon 4, 8, 11 and in intron 2 in the SUFU gene. Expression analysis by competitive reverse transcription-polymerase chain reaction (RT-PCR) revealed no difference in SUFU mRNA levels of both MB subtypes and normal foetal or adult cerebellar tissues. Our results indicate that genetic alterations of the SUFU gene, do not contribute significantly to the molecular pathogenesis of MBs.
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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 | 0.001 | 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".