Pri-miR526b and Pri-miR655 Are Potential Blood Biomarkers for Breast Cancer
Bibliographic record
Abstract
We reported that two microRNAs, miR526b and miR655, are oncogenic in breast cancer (BC). Overexpression of these two miRNAs in poorly metastatic BC cells promotes aggressive BC phenotypes in vitro and in vivo. High expression of each miRNA was associated with poor patient survival. In this pilot biomarker study, we report for the first time that miRNA precursor RNAs (pri-miRNAs) are robust and sensitive biomarkers for BC, detectable in both human blood plasma and biopsy tissues. Pri-miRNA detection and quantification do not require a special enrichment procedure, thus reducing specimen quantity. Blood plasma samples from 90 malignant tumor-bearing patients and 20 benign lesion-bearing participants (control) were analyzed for pri-miRNA expression with a quantitative real-time polymerase chain reaction. Results revealed that normalized expressions of plasma pri-miR526b and pri-miR655 are significantly upregulated in malignancy compared to benign plasmas (p = 0.002 and p = 0.03, respectively). Both pri-miRNAs showed more prominent results to distinguish stage I plasmas from benign plasmas (p = 0.001 for pri-miR526b and p = 0.0001 for pri-miR655). We have also validated pri-miRNA expression in independent tumor bank tissues, showing significant upregulation of both pri-miRNAs in BC; thus, pri-miRNAs are robust markers. The diagnostic relevance of pri-miRNAs was computed with the area under the curve (AUC). Pri-miR526b is a sensitive biomarker to distinguish cancer from control plasmas (sensitivity of 86%; AUC = 71.47%, p = 0.0027) with a positive predictive value of 88.89%; however, pri-miR655 did not show significant sensitivity. Furthermore, pri-miR526b could also significantly distinguish tumors as early as stage I from control (sensitivity of 75%; AUC = 72.71%, p = 0.0037). Therefore, pri-miR526b can be used as an early diagnostic biomarker. The expression of both pri-miRNAs was significantly high in ER-positive and HER2-negative subgroups of BC; hence, these biomarkers might play a role in the management of endocrine therapy designs. Additionally, with a case–control cohort study, we identified that high expression of pri-miR526b in the blood is also a risk factor associated with breast cancer (OR = 4.3, CI = 1.39–13.34, p = 0.01). Pri-miRNAs could be considered novel breast cancer blood biomarkers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".