β‐Catenin (<i>CTNNB1</i>) gene amplification: A new mechanism of protein overexpression in cancer
Bibliographic record
Abstract
beta-Catenin nuclear translocation is frequently observed in different types of malignancies, including gastric cancer. In gastric cancer, however, the molecular mechanisms leading to accumulation of this protein in the nucleus remain unknown. In this setting, beta-catenin (CTNNB1) mutations have been reported, but studies of mutation frequency have yielded conflicting results. Mutations or silencing of other partners of beta-catenin (i.e., APC and AXIN) are also considered rare genetic events in gastric tumorigenesis. Gene amplification is a common mechanism of activation and/or overexpression of oncogenes in gastric and other cancers. In this study, we investigated whether gene amplification is a possible mechanism of beta-catenin activation in gastric cancer by determining its presence in 49 patients with gastric cancer and two gastric-derived cell lines (KATO III and ST2957). Using fluorescence in situ hybridization, we identified beta-catenin amplification in one of the tumor samples as well as in KATO III cells. beta-Catenin immunostaining revealed nuclear translocation of the protein in both cases. In the KATO III cells, beta-catenin overexpression was confirmed by quantitative real-time PCR and Western blot analyses and beta-catenin gene amplification by Southern blot analysis and multiplex ligation probe amplification. In the KATO III cell line, no correlation was found between beta-catenin nuclear translocation and increased expression of the WNT1 target gene CCND1 (cyclin D1). Our data suggest that gene amplification is a possible mechanism of beta-catenin overexpression in cancer.
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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".