miR-30e* is an independent subtype-specific prognostic marker in breast cancer
Bibliographic record
Abstract
BACKGROUND: Breast cancer clinical outcome is affected by tumor molecular features, and the identification of subtype-specific prognostic biomarkers is relevant for breast cancer translational research. Gene expression signatures proved to be able to complement prognostic information provided by classical clinico-pathological features. Recently, microRNAs (miRNAs) have been causally linked to tumorigenesis and cancer progression and have been associated with patient outcome, also in breast cancer. METHODS: MicroRNAs associated with the development of distant metastasis were identified in a cohort of 92 ESR1+/ERBB2- lymph node-negative breast cancers from patients not receiving adjuvant treatment. Results were confirmed and further investigated in a total of 1246 miRNA and gene expression profiles of the Molecular Taxonomy of Breast Cancer International Consortium data set. Moderated t-test, univariable and multivariable Cox regression models were used for statistical analyses. RESULTS: miR-30e* was identified as independent protective prognostic factor in lymph node-negative untreated patients with ESR1+/ERBB2- tumours and retained a significant association with a good prognosis in treated patients with the same tumor subtype as well as in the ERBB2+ subtype, but not in ESR1-/ERBB2- tumours. CONCLUSIONS: We highlighted a relevant and subtype-specific role in breast cancer for miR-30e* and demonstrated that adding miRNA markers to gene signatures and clinico-pathological features can help for a better prognostication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".