Uninterrupted Sedentary Behavior Downregulates <i>BRCA1</i> Gene Expression
Bibliographic record
Abstract
BRCA1 mutation carriers face a high lifetime risk of developing breast cancer. Physical activity induces broad transcriptional changes, and multiple studies have documented its beneficial effects across cancers. Because haploinsufficiency predisposes to breast cancer in these women, factors that increase BRCA1 levels may mitigate the effect of the mutation. Whether physical activity modulates BRCA1 expression and whether lifestyle factors could benefit women with a mutation remain unclear. The objective of this study was to systematically evaluate whether physical activity or sedentary behavior affects BRCA1 mRNA expression. Activity levels were assessed in 50 female participants (14 BRCA1 mutation carriers and 36 noncarriers) using the GT3X Actigraph accelerometer, and BRCA1 mRNA expression was quantified from peripheral blood lymphocytes using the Nanostring nCounter Analysis System. There was a significant negative correlation between the longest sedentary bout and BRCA1 mRNA expression (ρ = -0.32; P = 0.02). Women below the median for the longest sedentary bout had significantly higher BRCA1 mRNA levels compared with women above the median (161 vs. 132 counts; P = 0.04; one-sided Mann-Whitney U test). There was no significant relationship between mean metabolic equivalents of task rate or mean sedentary time and BRCA1 mRNA expression (Spearman correlation P ≥ 0.75; P ≥ 0.14; Mann-Whitney U test). These findings suggest that prolonged periods of sedentary behavior are associated with significantly lower BRCA1 mRNA expression. Whether this translates into a potentially more harmful effect in BRCA1 mutation carriers warrants further investigation.
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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 | 0.000 |
| 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) | 0.002 | 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".