Hypoxia Selectively Increases a SMAD3 Signaling Axis to Promote Cancer Cell Invasion
Bibliographic record
Abstract
Transforming growth factor β (TGFβ) plays a paradoxical role in cancer, first inhibiting then promoting its progression, a duality that poses a real challenge for the development of effective TGFβ-targeted therapies. The major TGFβ downstream effectors, SMAD2 and SMAD3, display both distinct and overlapping functions and accumulating evidence suggests that their activation ratio may contribute to the dual effect of TGFβ. However, the mechanisms responsible for their selective activation remain poorly understood. Here, we provide experimental evidence that hypoxia induces the pro-invasive arm of TGFβ signaling through a selective increase in SMAD3 interaction with SMAD-Anchor for Receptor Activation (SARA). This event relies on HDAC6-dependent SMAD3 bioavailability, as well as increased SARA recruitment to EEA1+ endosomes. A motility gene expression study indicated that SMAD3 selectively increased the expression of ITGB2 and VIM, two genes that were found to be implicated in hypoxia-induced cell invasion and associated with tumor progression and metastasis in cohorts of cancer patients. Furthermore, CAM xenograft assays show the significant benefit of selective inhibition of the SMAD3 signaling pathway as opposed to global TGFβ inhibition in preventing tumor progression. Overall, these results suggest that fine-tuning of the pro-invasive HDAC6-SARA-SMAD3 axis could be a better strategy towards effective cancer treatments.
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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".