Downregulation of microRNAs 145-3p and 145-5p Is a Long-term Predictor of Postmenopausal Breast Cancer Risk: The ORDET Prospective Study
Bibliographic record
Abstract
BACKGROUND: miRNAs have been implicated in the regulation of key metabolic, inflammatory, and malignant pathways; hence, they might be considered both predictors and players of cancer development. METHODS: Using a case-control study design nested in the ORDET prospective cohort study, we addressed the possibility that specific mRNAs can serve as early predictors of breast cancer incidence in postmenopausal women. We compared leukocyte miRNA profiles of 133 incident postmenopausal breast cancer cases and profiles of 133 women who remained healthy over a follow-up period of 20 years. RESULTS: The analysis identified 20 differentially expressed miRNAs, 15 of which were downregulated. Of the 20 miRNAs, miR145-5p and miR145-3p, each derived from another arm of the respective pre-miRNA, were consistently and significantly downregulated in all the databases that we surveyed. For example, analysis of more than 1,500 patients (the UK Metabric cohort) indicated that high abundance of miR145-3p and miR145-5p was associated with longer, and for miR145-3p also statistically significant, survival. The experimental data attributed different roles to the identified miRNAs: Although the 5p isoform was associated with invasion and metastasis, the other isoform seems related to cell proliferation. CONCLUSIONS: These observations and the prospective design of our study lend support to the hypothesis that downregulation of specific miRNAs constitutes an early event in cancer development. This finding might be used for breast cancer prevention. IMPACT: The identification of the miRNAs as long-term biomarkers of breast cancer may have an impact on breast cancer prevention and early detection.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".