Crosstalk Between the <i>FGFR2</i> and <i>TP53</i> Genes in Breast Cancer: Data from an Association Study and Epistatic Interaction Analysis
Bibliographic record
Abstract
To evaluate the potential for gene-gene interaction effects in sporadic breast cancer (BC) risk, we studied combinations of the fibroblast growth factor receptor 2 (FGFR2) rs1219648 and tumor protein 53 (TP53) rs1042522, rs1625895, and rs17878362 polymorphisms in BC patients (n=388) and healthy persons (n=275). In addition to a single-locus effect manifested by the association of FGFR2 rs1219648 and TP53 rs1042522 polymorphisms with high BC risk, depending on menopause status (0.001<p<0.05), we showed a highly significant cooperation between the examined polymorphisms in FGFR2 and TP53 in the determination of susceptibility to the disease. Indeed, we found that combinations of FGFR2 minor and TP53 major genotypes were associated with a high risk of BC, particularly in the postmenopausal period (0.01<p<0.05). In contrast, combinations of the FGFR2 and TP53 major genotypes had a protective effect against BC, especially in premenopausal women (0.001<p<0.01). Of note, all observations were estrogen receptor (ER) dependent. The significant crosstalk between FGFR2 and TP53 polymorphisms was also confirmed by multifactor dimensionality reduction and ordered combinatorial partitioning approaches (0.001<p<0.05). Taken together, data from the present study demonstrate the age- and ER-specific interplay between TP53 and FGFR2 in BC.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".