Do Placental Genes Affect Maternal Breast Cancer? Association between Offspring's <i>CGB5</i> and <i>CSH1</i> Gene Variants and Maternal Breast Cancer Risk
Bibliographic record
Abstract
The protective effect of full-term pregnancy against breast cancer is thought to be induced by two placental hormones: human chorionic gonadotropin and human chorionic somatotropin hormone (CSH) produced by the placental trophoblastic cells. We hypothesized that variants in placental genes encoding these hormones may alter maternal breast cancer risk subsequent to pregnancy. We conducted a case-control study to examine the association between polymorphisms in a woman's placental (i.e., her offspring's) homologous chorionic gonadotrophin beta5 (CGB5) and CSH1 genes and her post-pregnancy breast cancer risk. A total of 293 breast cancer cases and 240 controls with at least one offspring with available DNA were selected from the New York site of the Breast Cancer Family Registry. Three single nucleotide polymorphisms (SNP) in CGB5 and CSH1 genes were genotyped for 844 offspring of the cases and controls. Overall, maternal breast cancer risk did not significantly differ by the offspring's carrier status of the three SNPs. Among women with an earlier age at childbirth (younger than the median age of 26 years), those with a child carrying the variant C allele of CGB5 rs726002 SNP had an elevated breast cancer risk [odds ratio (OR), 2.09; 95% confidence interval (95% CI), 1.17-3.73]. Among women with a later age at childbirth, breast cancer risk did not differ by offspring's carrier status of CGB5 rs726002 SNP (OR, 0.90; 95% CI, 0.53-1.51; P for interaction=0.04). The findings suggest that placental CGB5 genotype may be predictive of maternal post-pregnancy breast cancer risk among women who give birth early in life.
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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 | 0.000 | 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) | 0.003 | 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".