TP53 mutations andMDM2 gene amplification in squamous-cell carcinomas of the esophagus in South Thailand
Bibliographic record
Abstract
Squamous-cell carcinoma of the esophagus (SCCE) shows geographic variations in incidence that are thought to reflect the etiological involvement of environmental or dietary risk factors. Mutations of TP53 are frequent in SCCE, and there is evidence that both the frequency and type of these mutations may differ from one geographic area to the other. Although SCCE is relatively rare in most parts of Thailand, the province of Songkhla (south Thailand) has been described as a high-risk area for SCCE. We have analyzed 56 SCCE cases from this area for TP53 mutations by denaturing gradient gel electrophoresis (DGGE, exons 5-8) and direct DNA sequencing. The same tumors were also analyzed for MDM2 gene amplification by differential PCR. TP53 mutations were detected in 23 cases (41%). In contrast, clear amplification of MDM2 was detected in only 2 cases (4%), both of which contained wild-type TP53. Comparison with published results from other geographic areas of high SCCE incidence revealed that the spectrum of TP53 mutations in south Thailand is similar to that observed in central China (Henan Province) but clearly differs from that of SCCE from western Europe (Normandy, France; northern Italy), with more G:T transversions and fewer mutations affecting A and T base pairs. These results suggest that SCCE from south Thailand and from central China may involve similar risk factors.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".