DNA Methylation Alterations in the Pancreatic Juice of Patients with Suspected Pancreatic Disease
Bibliographic record
Abstract
Molecular markers of pancreatic neoplasia could aid in the evaluation of visible pancreatic lesions and indicate neoplasia invisible to imaging. We evaluated methylation-specific PCR (MSP) assays that detect aberrantly methylated DNA for their use as markers of pancreatic neoplasia. Methylation analysis was done on pancreatic juice collected endoscopically or surgically from 155 individuals with suspected pancreatic disease: 56 patients had pancreatic ductal adenocarcinoma, 17 had intraductal papillary mucinous neoplasms, 26 had symptomatic chronic pancreatitis, 12 controls lacked evidence of pancreatic disease, and 44 were asymptomatic individuals at increased risk of developing familial pancreatic cancer undergoing screening for pancreatic neoplasia. Pancreatic juice DNA was analyzed for promoter methylation using conventional MSP assays for 17 genes. For six genes, pancreatic juice methylation was quantified using real-time quantitative MSP (QMSP; Cyclin D2, FOXE1, NPTX2, ppENK, p16, and TFPI2). Quantifying pancreatic juice methylation using QMSP with a cutoff of >1% methylated DNA could better predict pancreatic cancer than detecting methylation using conventional MSP. In the endoscopic group, 9 of 11 patients with pancreatic cancer, but none of 64 individuals without neoplasia had > or =1% methylation for two or more of the best five QMSP assays (82% sensitivity and 100% specificity; P < 0.0001). The prevalence of pancreatic juice methylation in patients with chronic pancreatitis was less than in patients with pancreatic cancer but higher than in controls and similar to high-risk individuals. The detection and quantification of aberrantly methylated DNA in pancreatic juice is a promising approach to the diagnosis of pancreatic cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".