Epigenetic Silencing of β-Spectrin, a TGF-β Signaling/Scaffolding Protein in a Human Cancer Stem Cell Disorder
Bibliographic record
Abstract
Hereditary cancer syndromes provide powerful insights into dysfunctional signaling pathways that lead to sporadic cancers. Beckwith-Wiedemann syndrome (BWS) is a hereditary human cancer stem cell syndrome currently linked to deregulated imprinting at chromosome 11p15 and uniparental disomy. However, causal molecular defects and genetic models have remained elusive to date in the majority of cases. The non-pleckstrin homology domain β-spectrin (β2SP) (the official name for human is Spectrin, beta, nonerythrocytic 1 (SPTBN1), isoform 2; the official name for mouse is Spectrin beta 2 (Spnb2), isoform 2), a scaffolding protein, functions as a potent TGF-β signaling member adaptor in tumor suppression and development. Yet, the role of the β2SP in human tumor syndromes remains unclear. Here, we report that β2SP(+/-) mice are born with many phenotypic characteristics observed in BWS patients, suggesting that β2SP mutant mice phenocopy BWS, and β2SP loss could be one of the mechanisms associated with BWS. Our results also suggest that epigenetic silencing of β2SP is a new potential causal factor in human BWS patients. Furthermore, β2SP(+/-) mice provide an important animal model for BWS, as well as sporadic cancers associated with it, including lethal gastrointestinal and pancreatic cancer. Thus, these studies could lead to further insight into defects generated by dysfunctional stem cells and identification of new treatment strategies and functional markers for the early detection of these lethal cancers that otherwise cannot be detected at an early stage.
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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) | 0.001 | 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".