The oncofetal protein IMP1 regulates the transcriptomic landscape to drive early events in pancreatic cancer progression and growth
Bibliographic record
Abstract
ABSTRACT Background & Aims Pancreatic ductal adenocarcinoma (PDAC) has a dismal 5-year survival rate of 12% - the lowest of all malignancies. This is partially due to late diagnosis, as early stages of the disease, including the process of acinar to ductal metaplasia (ADM) are not presently detectable. Insulin-like growth factor 2 mRNA binding protein (IMP)1 is an oncofetal protein implicated in cancer progression. Here, we aimed to determine its role in the early stages of PDAC development and in the maintenance of the malignant phenotype. Methods IMP1 expression was analyzed in surgical PDAC specimens and in pancreatic tissue derived from KPC mice. Murine ductal organoids expressing the Kras G12D mutant were treated with the IMP1 inhibitor BTYNB and RNAseq performed. The function of IMP1 targets was analyzed in an ADM model and the effect of IMP1 silencing on the growth of PDAC cells was evaluated in vivo . Results We found high expression of IMP1 in precancerous lesions of human and murine PDAC, but not in the normal pancreas. Blockade of IMP1 function impeded murine ADM and ductal organoid growth and profoundly altered the transcriptional landscape of the organoids, reducing the expression of cytokine-cytokine receptor interactors, cell adhesion and cell invasion mediators such as Card11, Gkn3 , Il13ra2 , Mmp9 , and Vcam1 . Gastrokine-3 and IL-13 in turn, enhanced the ADM process. Finally, IMP1 silencing in PDAC cells inhibited their metastatic outgrowth in mice. Conclusions IMP1 is a master regulator of early events in PDAC progression and a potential biomarker and target for this disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".