Prognostic Significance of Metastasis-Related MicroRNAs in Early Breast Cancer Patients with a Long Follow-up
Bibliographic record
Abstract
BACKGROUND: Stability of microRNAs (miRNAs) in formalin-fixed paraffin-embedded (FFPE) tissues enables their reliable analysis in archived FFPE tissue samples, which are an invaluable source for the evaluation of novel biomarkers. Especially in breast cancer, for which late relapses occur in many cases, analysis of miRNAs in FFPE tissues holds great potential, because it can lead to the discovery of novel biomarkers suitable for future routine clinical diagnostics for breast cancer. We investigated the prognostic significance of 6 metastasis-related miRNAs that can critically regulate various stages of migration and invasion and play critical roles in the multistep metastatic process. METHODS: We quantified the expression of 6 mature miRNAs (namely miR-21, miR-205, miR-10b, miR-210, miR-335, and let-7a) by reverse-transcription quantitative PCR in FFPE tissues of 84 patients with early breast cancer and a long follow-up and 13 cancer-free breast tissue FFPE samples that were used as the control group. We further correlated individual miRNA over- or underexpression with the disease-free interval (DFI) and overall survival (OS). RESULTS: Univariate analysis revealed that both miR-21 and miR-205 were significantly associated with DFI and only miR-205 with OS. Multivariate analysis demonstrated that miR-205 and miR-21 were independent factors associated with early disease relapse, whereas only miR-205 overexpression was associated with OS. CONCLUSIONS: Our results clearly indicate that deregulation of metastasis-associated miRNAs in primary tumors is associated with clinical outcome in patients with early breast cancer and can differentiate patients with higher risk in well-characterized subgroups.
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 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 | 0.001 |
| 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 | 0.000 | 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".