P53 Codon 72 and Endometrium Cancer
Bibliographic record
Abstract
Background: The possible role p53 codon 72 in endometrium cancer has been investigated in several human populations: a positive association with the Pro variant has been observed in Asiatic but not in Caucasian populations. We reasoned that polymorphisms associated with endometrium cancer may interact with p53 codon 72 influencing the degree of association between this polymorphism and cancer. Methods: Sixty nine women admitted to the hospital for endometrium cancer and 473 healthy subjects were studied in the White population of Rome. Verbal consent was obtained from these subjects to participate to the study that was approved by the Department. P53 codon 72, ADA1, ADA6 and PTPN22 genotypes were determined by DNA analysis Statistical analysis were performed by using commercial software (SPSS). Results: The joint genotype carrying the *Pro allele of p53 codon 72and the ADA1*2 allele, the joint genotype carrying the *Pro allele and ADA6 *1 allele and the joint genotype carrying the *Pro allele and *C/*C genotype of PTPN22 show a proportion greater in cancer than in controls. The proportion of *Pro allele carriers in endometrium cancer shows a positive correlation (p=0.019) with the number of genetic factors considered i.e. ADA1,ADA6, PTPN22. Conclusion: Our data suggest that the strength of association between the disease and p53 codon 72 depends on other genetic factors. Thus the different patterns of association between p53 codon 72 and endometrium cancer observed among human populations could be at least in part related to differences in allelic frequencies of these genetic 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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".