Blood biomarkers reflect the effects of obesity and inflammation on the human breast transcriptome
Bibliographic record
Abstract
Obesity is a risk factor for the development of post-menopausal breast cancer. Breast white adipose tissue (WAT) inflammation, which is commonly found in women with excess body fat, is also associated with increased breast cancer risk. Both local and systemic effects are probably important for explaining the link between excess body fat, adipose inflammation and breast cancer. The first goal of this cross-sectional study of 196 women was to carry out transcriptome profiling to define the molecular changes that occur in the breast related to excess body fat and WAT inflammation. A second objective was to determine if commonly measured blood biomarkers of risk and prognosis reflect molecular changes in the breast. Breast WAT inflammation was assessed by immunohistochemistry. Bulk RNA-sequencing was carried out to assess gene expression in non-tumorous breast. Obesity and WAT inflammation were associated with a large number of differentially expressed genes and changes in multiple pathways linked to the development and progression of breast cancer. Altered pathways included inflammatory response, complement, KRAS signaling, tumor necrosis factor α signaling via NFkB, interleukin (IL)6-JAK-STAT3 signaling, epithelial mesenchymal transition, angiogenesis, interferon γ response and transforming growth factor (TGF)-β signaling. Increased expression of several drug targets such as aromatase, TGF-β1, IDO-1 and PD-1 were observed. Levels of various blood biomarkers including high sensitivity C-reactive protein, IL6, leptin, adiponectin, triglycerides, high-density lipoprotein cholesterol and insulin were altered and correlated with molecular changes in the breast. Collectively, this study helps to explain both the link between obesity and breast cancer and the utility of blood biomarkers for determining risk and prognosis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".