Gene modules and non-coding RNAs involved in pancreatic tumorigenesis through acinar ductal metaplasia 
Bibliographic record
Abstract
Abstract Background: Acinar ductal metaplasia (ADM) is a recently identified precursor lesion that can progress through pancreatic ductal intraepithelial neoplasia (PanIN) to pancreatic ductal adenocarcinoma (PDAC). However, the genetic alterations and the transcriptional regulators at work during the process of ADM-driven PDAC tumorigenesis are largely unknown. We applied a multidimensional integration strategy to unveil the gene modules and non-coding RNAs that drive the ADM-PanIN-PDAC process. Methods: GSE40895 and the microarray datasets were integrated to unmask the regulators linked to ADM, PanIN and PDAC. Based on the differentially expressed genes and protein–protein interaction (PPI) networks for each stage, overlapping and crosstalk gene modules in ADM-PanIN-PDAC were identified using the search tool for the retrieval of interacting genes (STRING) and Cytoscape. The functions of these modules were elucidated by gene ontology (GO) analysis. The expression levels of hub genes and survival analysis were investigated in human PDAC via gene expression profiling interactive analysis (GEPIA). The MiRDB database was used to predict potential non-coding RNAs (ncRNAs) capable of regulating overlap and crosstalk genes.Results: We found several bridging ADM gene modules (e.g. SMARCA1 and H2AFZ), PanIN gene modules (e.g. HDAC11 and SMARCA2) and PDAC gene modules (e.g. OLFR239 and CLIP3). They were enriched in nucleosome assembly, chromatin organization and G-protein coupled receptor signalling pathways by GO analysis. MicroRNAs (e.g. mmu-miR-335-5p and mmu-miR-669n) and lncRNAs (e.g. H19 and Gm14207) took part in this ample crosstalk by regulating the gene expression. Conclusions: SMARCA1, SMARCA2 and CLIP3 were identified as novel crosstalk genes and potential prognostic biomarkers for ADM-driven PDAC carcinogenesis. After validation in clinical and functional studies, transcriptional regulatory non-coding RNAs targeting crosstalk and overlapping genes could represent effective targets for early PDAC intervention.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".