Direct Comparison of Metastasis-Related miRNAs Expression Levels in Circulating Tumor Cells, Corresponding Plasma, and Primary Tumors of Breast Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Circulating tumor cells (CTCs) and microRNAs (miRNAs) are important in liquid biopsies in which peripheral blood is used to characterize the evolution of solid tumors. We evaluated the expression levels of miR-21, miR-146a, miR-200c, and miR-210 in CTCs of breast cancer patients with verified metastasis and compared their expression levels in corresponding plasma and primary tumors. METHODS: Expression levels of the miRNAs were quantified by quantitative reverse transcription PCR (RT-qPCR) in (a) 89 primary breast tumors and 30 noncancerous breast tissues and (b) CTCs and corresponding plasma of 55 patients with metastatic breast cancer and 20 healthy donors. For 30 of these patients, CTCs, corresponding plasma, and primary tumor tissues were available. RESULTS: In formalin-fixed, paraffin-embedded tissues, these miRNAs were differentially expressed between primary breast tumors and noncancerous breast tissues. miR-21 (P < 0.001) and miR-146a (P = 0.001) were overexpressed, whereas miR-200c (P = 0.004) and miR-210 (P = 0.002) were underexpressed. In multivariate analysis, miR-146a overexpression was significantly [hazard ratio 2.969 (1.231-7.157), P = 0.015] associated with progression-free survival. In peripheral blood, all miRNAs studied were overexpressed in both CTC and corresponding plasma. There was a significant association between miR-21 expression levels in CTCs and plasma for 36 of 55 samples (P = 0.008). In plasma, ROC curve analysis revealed that miR-21, miR-146a, and miR-210 could discriminate patients from healthy individuals. CONCLUSIONS: Metastasis-related miRNAs are overexpressed in CTCs and corresponding plasma; miR-21 expression levels highly correlate in CTCs and plasma; and miR-21, miR-146a, and miR-210 are valuable plasma biomarkers for discriminating patients from healthy individuals.
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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) | 0.001 | 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".