Genome‐wide DNA methylation patterns in pancreatic ductal adenocarcinoma reveal epigenetic deregulation of SLIT‐ROBO, ITGA2 and MET signaling
Bibliographic record
Abstract
The importance of epigenetic modifications such as DNA methylation in tumorigenesis is increasingly being appreciated. To define the genome-wide pattern of DNA methylation in pancreatic ductal adenocarcinomas (PDAC), we captured the methylation profiles of 167 untreated resected PDACs and compared them to a panel of 29 adjacent nontransformed pancreata using high-density arrays. A total of 11,634 CpG sites associated with 3,522 genes were significantly differentially methylated (DM) in PDAC and were capable of segregating PDAC from non-malignant pancreas, regardless of tumor cellularity. As expected, PDAC hypermethylation was most prevalent in the 5' region of genes (including the proximal promoter, 5'UTR and CpG islands). Approximately 33% DM genes showed significant inverse correlation with mRNA expression levels. Pathway analysis revealed an enrichment of aberrantly methylated genes involved in key molecular mechanisms important to PDAC: TGF-β, WNT, integrin signaling, cell adhesion, stellate cell activation and axon guidance. Given the recent discovery that SLIT-ROBO mutations play a clinically important role in PDAC, the role of epigenetic perturbation of axon guidance was pursued in more detail. Bisulfite amplicon deep sequencing and qRT-PCR expression analyses confirmed recurrent perturbation of axon guidance pathway genes SLIT2, SLIT3, ROBO1, ROBO3, ITGA2 and MET and suggests epigenetic suppression of SLIT-ROBO signaling and up-regulation of MET and ITGA2 expression. Hypomethylation of MET and ITGA2 correlated with high gene expression, which was associated with poor survival. These data suggest that aberrant methylation plays an important role in pancreatic carcinogenesis affecting core signaling pathways with potential implications for the disease pathophysiology and therapy.
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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 | 0.000 |
| 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) | 0.001 | 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".