Circulating miR-210 as a Novel Hypoxia Marker in Pancreatic Cancer
Bibliographic record
Abstract
MicroRNA are small noncoding transcripts involved in many cellular mechanisms, including tumorigenesis. miR-210, in particular, is induced by hypoxia and correlates with adverse outcomes in certain cancers. Because pancreatic adenocarcinomas exhibit extremely hypoxic signatures, we hypothesized that miR-210 may serve as a diagnostic marker for screening or surveillance for pancreatic cancer. Plasma samples were obtained from newly diagnosed pancreatic cancer patients and age-matched noncancer controls. miRNA was extracted directly from plasma and reverse-transcribed to complementary DNA. A known quantity of synthetic Caenorhabditis elegans miR-54 (celmiR-54) was added for normalization. miR-210 and cel-miR-54 were then measured using quantitative reverse transcription polymerase chain reaction. An initial cohort of 11 pancreatic cancer patients and 14 age-matched controls was used as the test set and a second cohort of 11 pancreatic cancer patients and 11 controls was used as the validating set in this study. miR-210 was reliably detected and quantified, with a statistically significant four-fold increase in expression in pancreatic cancer patients compared with normal controls (P < .00004) in the test set. This difference was confirmed in the validation group (P < .018). In summary, circulating miR-210 levels are elevated in pancreatic cancer patients and may potentially serve as a useful biomarker for pancreatic cancer diagnosis.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".