MICAL2 Promotes Pancreatic Cancer Growth and Metastasis
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid cancers; thus, identifying more effective therapies is a major unmet need. In this study, we characterized the super-enhancer (SE) landscape of human PDAC to identify drivers of the disease that might be targetable. This analysis revealed MICAL2 as an SE-associated gene in human PDAC, which encodes the flavin monooxygenase enzyme that induces actin depolymerization and indirectly promotes serum response factor transcription by modulating the availability of serum response factor coactivators such as myocardin-related transcription factors (MRTF-A and MRTF-B). MICAL2 was overexpressed in PDAC, and high-MICAL2 expression correlated with poor patient prognosis. Transcriptional analysis revealed that MICAL2 upregulates KRAS and epithelial-mesenchymal transition signaling pathways, contributing to tumor growth and metastasis. In loss- and gain-of-function experiments in human and mouse PDAC cells, MICAL2 promoted both ERK1/2 and AKT activation. Consistent with its role in actin depolymerization and KRAS signaling, loss of MICAL2 also inhibited macropinocytosis. MICAL2, MRTF-A, and MRTF-B influenced PDAC cell proliferation and migration and promoted cell-cycle progression in vitro. Importantly, MICAL2 supported in vivo tumor growth and metastasis. Interestingly, MRTF-B, but not MRTF-A, phenocopied MICAL2-driven phenotypes in vivo. This study highlights the multiple ways in which MICAL2 affects PDAC biology and provides a foundation for future investigations into the potential of targeting MICAL2 for therapeutic intervention. Significance: Characterization of the epigenomic landscape of pancreatic cancer to identify early drivers of tumorigenesis uncovered MICAL2 as a super-enhancer-associated gene critical for tumor progression that represents a potential pharmacologic target.
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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".