<i>p53</i>mutations associated with aging-related rise in cancer incidence rates
Bibliographic record
Abstract
TP53's role as guardian of the genome diminishes with age, as the probability of mutation increases. Previous studies have shown an association between p53 gene mutations and cancer. However, the role of somatic TP53 mutations in the steep rise in cancer rates with aging has not been investigated at a population level. This relationship was quantified using the International Agency for Research on Cancer (IARC) TP53 and GLOBOCAN cancer databases. The power function exponent of the cancer rate was calculated for 5-y age-standardized incidence or mortality rates for up to 25 cancer sites occurring in adults of median age 42 to 72 y. Linear regression analysis of the mean percentage of a cancer's TP53 mutations and the corresponding cancer exponent was conducted for four populations: worldwide, Japan, Western Europe, and the United States. Significant associations (P ≤ 0.05) were found for incidence rates but not mortality rates. Regardless of the population studied, positive associations were found for all cancer sites, with more significant associations for solid tumors, excluding the outlier prostate cancer or sex-related tumors. Worldwide and Japanese populations yielded P values as low as 0.002 and 0.005, respectively. For the United States, a significant association was apparent only when analysis utilized the Surveillance, Epidemiology, and End Results (SEER) database. This study found that TP53 mutations accounts for approximately one-quarter and one-third of the aging-related rise in the worldwide and Japanese incidence of all cancers, respectively. These significant associations between TP53 mutations and the rapid rise in cancer incidence with aging, considered with previously published literature, support a causal role for TP53 according to the Bradford-Hill criteria. However, questions remain concerning the contribution of TP53 mutations to neoplastic development and the role of factors such as genetic instability, obesity, and gene deficiencies other than TP53 that reduce p53 activity.
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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 | 0.002 |
| 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) | 0.001 | 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".