Prognostic Implications of microRNA-155, -133a, -21 and -205 in BreastCancer Patients’ Plasma
Bibliographic record
Abstract
BACKGROUND: Breast cancer, being a heterogenous disease at the intra-tumoral and intertumoral levels, presents challenges in following the progress of the disease. Tumour-secreted aberrantly expressed miRNAs obtained from peripheral blood represent a non-invasive alternative resource for detecting and monitoring the development of the disease. This study evaluates the expression of miR-155, miR-133a, miR-21 and miR-205 as non-invasive, prognostic and follow-up markers for breast cancer. METHODS: Plasma expression levels of miR-155, miR-133a, miR-21 and miR-205 were measured using real-time PCR in breast cancer patients (n=63) at presentation, healthy controls (n=25), and in post-treatment samples of 31 patients. A meta-analysis was performed using 43 studies identified from PubMed, Google Scholar and Scopus databases. Hedge's g values were used to calculate the overall effect size. RESULTS: Plasma miR-21 levels were higher in breast cancer patients at presentation compared to controls, while no difference was observed for miR-155, miR-133a and miR-205. These results were further supported by the meta-analysis. The altered levels of miR-155 during tamoxifen treatment indicated a potential role for miR-155 in monitoring treatment response. Further, high expressions of at least three miRNAs correlated with poor overall survival in the breast cancer patients. CONCLUSION: Plasma levels of miR-155, miR-133a, miR-21 and miR-205 may be useful as prognostic and follow-up markers for breast cancer with further validation in a large cohort of patients.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".