Differential spatial distribution of HNF4α isoforms during dysplastic progression of intraductal papillary mucinous neoplasms of the pancreas
Bibliographic record
Abstract
Hepatocyte Nuclear Factor 4-alpha (HNF4α) comprises a nuclear receptor superfamily of ligand-dependent transcription factors that yields twelve isoforms in humans, classified into promoters P1 or P2-associated groups with specific functions. Alterations in HNF4α isoforms have been associated with tumorigenesis. However, the distribution of its isoforms during progression from dysplasia to malignancy has not been studied, nor has it yet been studied in intraductal papillary mucinous neoplasms, where both malignant and pre-malignant forms are routinely clinically identified. We examined the expression patterns of pan-promoter, P1-specific, and P2-specific isoform groups in normal pancreatic components and IPMNs. Pan-promoter, P1 and P2 nuclear expression were weakly positive in normal pancreatic components. Nuclear expression for all isoform groups was increased in low-grade IPMN, high-grade IPMN, and well-differentiated invasive adenocarcinoma. Poorly differentiated invasive components in IPMNs showed loss of all forms of HNF4α. Pan-promoter, and P1-specific HNF4α expression showed shifts in subnuclear and sub-anatomical distribution in IPMN, whereas P2 expression was consistently nuclear. Tumor cells with high-grade dysplasia at the basal interface with the stroma showed reduced expression of P1, while P2 was equally expressed in both components. Additional functional studies are warranted to further explore the mechanisms underlying the spatial and differential distribution of HNF4α isoforms in IPMNs.
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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 | 0.000 |
| 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) | 0.000 | 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".