Age- and Disease-Related Methylation of Multiple Genes in Nonneoplastic Duodenum and in Duodenal Juice
Bibliographic record
Abstract
PURPOSE: Methylation of CpG islands contributes to gene silencing during cancer development, and although some methylation alterations are promising diagnostic markers of cancer, some CpG islands are also methylated in normal tissues. We have previously observed that some normally unmethylated CpG islands that undergo methylation in pancreatic cancers are normally methylated in the adjacent duodenum. Because duodenal methylation patterns are an important consideration when sampling pancreatic tissues for pancreatic cancer methylation alterations, we determined the DNA methylation patterns of 24 genes in the normal duodenum of patients with pancreatic disease and related these patterns to demographic factors. EXPERIMENTAL DESIGN: The nonneoplastic duodenal mucosa of 158 patients with pancreatic carcinoma and 41 patients with chronic pancreatitis was analyzed using methylation-specific PCR and combined bisulfite restriction analysis. Secretin-stimulated pancreatic/duodenal juice from 15 individuals undergoing endoscopic investigation for upper gastrointestinal disease was also analyzed. RESULTS: Low-level methylation was detectable by methylation-specific PCR in the nonneoplastic duodenum of many patients with pancreatic cancer and chronic pancreatitis as well as in the pancreaticoduodenal secretions of patients without pancreaticobiliary disease. For many genes, the prevalence of methylation increased with age and was more prevalent in patients with pancreatic cancer than in age-matched patients with chronic pancreatitis. Our results indicate that strategies to detect pancreatic cancer using aberrantly methylated genes should rely on analysis of pure pancreatic juice rather than on pancreatic juice collected within the duodenal lumen. Patients who develop pancreatic cancer may have a greater propensity to methylate CpG islands than age-matched controls.
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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 | 0.000 | 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".