Triple negative breast cancer: microRNA expression profile and novel discriminators according to BRCA1 status
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) represents 15% of breast carcinomas. More than 80% of women with a breast cancer associated with a breast cancer type 1 (BRCA1) mutation develop a TNBC. microRNAs (miRNAs) play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as actors of tumor onset, behavior, and progression. However, an extensive microRNA profile has not yet been determined for TNBC. Taqman low-density arrays (TLDAs) were used to screen the expression level of 667 miRNAs in TNBC versus normal breast tissues. Our TLDA results revealed 20 differentially expressed miRNAs among which 14 (10 upregulated and four downregulated) were confirmed by an individual quantitative real-time polymerase chain reaction. Interestingly, a novel link between BRCA1 status and miRNA expression level was identified through miR-96 and miR-10b that were very important discriminators between TNBC with mutated BRCA1 and TNBC with wild type BRCA1. This study promises discoveries of new pathological pathways at work in this dreadful disease and clearly warrants validation in large prospective studies with the aim of identifying novel biomarkers for diagnosis and targets for clinical interventions.
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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 | 0.000 |
| 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) | 0.001 | 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".