Clinical Significance of Circulating miR-1273g-3p and miR-122-5p in Pancreatic Cancer
Bibliographic record
Abstract
The burden of pancreatic cancer (PanC) requires innovation in the current diagnostic approach. This study aimed to uncover new circulating microRNAs (miRNAs) that would distinguish patients with PanC from healthy subjects (HS) compared with the cancer antigen 19-9 (CA 19-9), and predict patients’ clinical phenotypes and outcomes. MiRNA expression profiles in plasma were investigated by using a two-stage process. In a discovery phase, miRNA levels were analyzed using the GeneChip™ miRNA 4.0 Affymetrix assay in 10 pools of plasma samples from PanC patients and HS; in a validation phase, significantly altered miRNAs were re-tested in independent cohorts of cancer patients and controls by droplet digital dPCR (ddPCR). The diagnostic performance of the resulting miRNAs was compared to CA 19-9 determinations, and the associations of miRNAs plasma levels with patients’ clinical phenotypes and outcomes were also taken into account. Bioinformatics selection of miRNAs differentially expressed in plasma uncovered miR-18a-5p, miR-122-5p, miR-1273g-3p, and miR-6126 as candidate oncogenic miRNAs in PanC. The ddPCR technology confirmed the significant over-expression of miR-122-5p, miR-1273g-3p, and miR-6126 in PanC compared to HS, in line with the trend of the CA 19-9 levels. Plasma levels of miR-1273g-3p, in combination with CA 19-9, showed a higher power in distinguishing PanC patients from HS compared to the CA 19-9 tested alone, with a gain in both sensitivity and negative predictive value indicating a low false-negative rate (SE=90.2% and NPV=92.3% vs. SE=82.1% and NPV=87.9%). None of the oncogenic miRNAs were able to distinguish between a neoplastic and a proliferative/inflammatory disease of the pancreas and were not able to stratify subjects according to the clinical risk for the disease. The only valuable association in PanC patients was found between miR-1273g-3p and tumor stage, and increased miR-122-5p levels emerged as independent negative prognostic factor for PanC patients (HR=1.58, 95% CI= 1.03-2.43, p= 0.037). Our data highlighted a role for circulating miR-1273g-3p and miR-122-5p as new diagnostic and prognostic biomarkers for PanC.
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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) | 0.000 | 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".