Ras-dependent activation of BMAL2 regulates hypoxic metabolism in pancreatic cancer
Bibliographic record
Abstract
Summary KRAS is the archetypal oncogenic driver of pancreatic cancer. To identify new modulators of KRAS activity in human pancreatic ductal adenocarcinoma (PDAC), we performed regulatory network analysis on a large collection of expression profiles from laser capture microdissected samples of PDAC and benign controls. We discovered that BMAL2, a member of the PAS family of transcription factors, promotes tumor initiation, progression, and post-resection survival, and is highly correlated with KRAS activity. Functional analysis of BMAL2 target genes suggested a role in regulating the hypoxia response, a hallmark of PDAC. Knockout of BMAL2 in multiple human PDAC cell lines reduced cancer cell viability, invasion, and glycolysis, leading to broad dysregulation of cellular metabolism, particularly under hypoxic conditions. We find that BMAL2 directly regulates hypoxia-responsive target genes and is necessary for the stabilization of HIF1A under low oxygen conditions, while simultaneously destabilizing HIF2A. Notably, in vivo xenograft studies demonstrated that BMAL2 loss significantly impairs tumor growth and reduces tumor volume, underscoring its functional importance in tumor progression. We conclude that BMAL2 is a master transcriptional regulator of hypoxia responses in PDAC that works downstream of KRAS signaling, possibly serving as a long-sought molecular switch that distinguishes HIF1A- and HIF2A-dependent modes of hypoxic metabolism. Statement of Significance We annotate the landscape of KRAS-associated transcriptional drivers of pancreatic cancer initiation, progression, and overall survival, leading to the identification of BMAL2 as a novel regulator of hypoxic metabolism. BMAL2 helps execute the oncogenic transcriptional programs of KRAS and serves as a long-sought switch between HIF1A- and HIF2A-dependent modes of hypoxic metabolism.
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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) | 0.001 | 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".