<scp>ITGAV</scp> and <scp>SMAD4</scp> influence the progression and clinical outcome of pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive and lethal malignancy with limited treatment options, a fact that underscores the urgent need for more effective therapies to improve patient outcomes. Preclinical studies have shown promise for αV integrin-targeted therapies; however, clinical trials have been disappointing, highlighting the need for further research. In this study, we demonstrate that integrin subunit alpha V (ITGAV) signals through both mothers against decapentaplegic homolog 4 (SMAD4)-dependent or SMAD4-independent pathways, depending on the genetic context. In SMAD4-positive PDAC cells, ITGAV contributes to the transforming growth factor-beta (TGF-β) signaling pathway to regulate proliferation, migration, and invasion. Conversely, in SMAD4-negative PDAC cells, ITGAV influences only proliferation and migration via activation of the mitogen-activated protein kinase (MAPK)/extracellular signal-related kinase (ERK) pathway. High levels of ITGAV are also associated with poor prognostic outcomes in SMAD4 wild-type patients but are not prognostic in SMAD4 mutant patients. Thus, ITGAV contributes to different patterns of PDAC progression. These findings suggest that stratifying PDAC patients based on both SMAD4 status and ITGAV expression could inform more effective integrin-targeted treatment strategies.
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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) | 0.002 | 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".