HMGA2 and protein leucine methylation drive pancreatic cancer lineage plasticity
Bibliographic record
Abstract
Basal pancreatic ductal adenocarcinoma (PDAC) has the worst overall survival and is the only subtype that serves as an independent poor prognostic factor. We identify elevated levels of LIN28B and its downstream target, HMGA2, in basal PDAC. Notably, LIN28B significantly accelerates KRAS-driven PDAC progression in a mouse model. Here, we show that HMGA2 promotes basal PDAC pathogenesis by enhancing mRNA translation downstream of LIN28B. Mechanistically, HMGA2 suppresses leucine carboxyl methyltransferase 1 (LCMT1) at the chromatin level, reducing PP2A methylation and activity. This leads to increased phosphorylation of S6K and eIF4B, boosting mRNA translation. Additionally, HMGA2 downregulates B56α (PPP2R5A), disrupting functional PP2A holoenzyme assembly and further sustaining phosphorylated S6K levels. Impaired PP2A function mimics HMGA2’s effects, reinforcing increased mRNA translation and basal lineage features. This work uncovers a critical link between the LIN28B/HMGA2 axis, protein synthesis, and PDAC lineage specificity via LCMT1-mediated PP2A methylation and B56α-PP2A disruption. Basal Pancreatic Ductal Adenocarcinoma (PDAC) is more aggressive than the classical subtype of pancreatic cancer. Here the authors report that RNA-binding protein LIN28B and its target HMGA2 drive basal PDAC pathogenesis by reducing PP2A methylation and activity, resulting in enhanced protein synthesis and aggressive features.
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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 | 0.001 |
| 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".