Novel prognostic and predictive microRNA targets for triple‐negative breast cancer
Bibliographic record
Abstract
Triple-negative breast cancers (TNBCs) account for ∼25% of all invasive carcinomas and represent a large subset of aggressive, high-grade tumors. Despite current research focused on understanding the genetic landscape of TNBCs, reliable prognostic and predictive biomarkers remain limited. Although dysregulated microRNAs (miRNAs) have emerged as key players in many cancer types, the role of miRNAs in TNBC disease progression is unclear. We performed miRNA profiling of 51 TNBCs by next-generation sequencing to reveal differentially expressed miRNAs. A total of 228 miRNAs were identified. Three miRNAs (miR-224-5p, miR-375, and miR-205-5p) separated the tumors based on basal status. Six miRNAs (high let-7d-3p, miR-203b-5p, and miR-324-5p; low miR-30a-3p, miR-30a-5p, and miR-199a-5p) were significantly associated with decreased overall survival (OS) and 5 miRNAs (high let-7d-3p; low miR-30a-3p, miR-30a-5p, miR-30c-5p, and miR-128-3p) with decreased relapse-free survival (RFS). On multivariate analysis, high expression of let-7d-3p and low expression of miR-30a were independent predictors of decreased OS and RFS. High expression of miR-95-3p was significantly associated with decreased OS and RFS in patients treated with anthracycline-based chemotherapy. Five miRNAs (let-7d-3p, miR-30a-3p, miR-30c-5p, miR-128-3p, and miR-95-3p) were validated by quantitative RT-PCR. Our findings unveil novel prognostic and predictive miRNA targets for TNBC, including a miRNA signature that predicts patient response to anthracycline-based chemotherapy. This may improve clinical management and/or lead to the development of novel therapies.-Turashvili, G., Lightbody, E. D., Tyryshkin, K., SenGupta, S. K., Elliott, B. E., Madarnas, Y., Ghaffari, A., Day, A., Nicol, C. J. B. Novel prognostic and predictive microRNA targets for triple-negative breast cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".