Perk-Dependent Translational Regulation Promotes Tumor Cell Adaptation and Angiogenesis in Response to Hypoxic Stress
Bibliographic record
Abstract
It has been well established that the tumor microenvironment can promote tumor cell adaptation and survival. However, the mechanisms that influence malignant progression have not been clearly elucidated. We have previously demonstrated that cells cultured under hypoxic/anoxic conditions and transformed cells in hypoxic areas of tumors activate a translational control program known as the integrated stress response (ISR). Here, we show that tumors derived from K-Ras-transformed Perk(-/-) mouse embryonic fibroblasts (MEFs) are smaller and exhibit less angiogenesis than tumors with an intact ISR. Furthermore, Perk promotes a tumor microenvironment that favors the formation of functional microvessels. These observations were corroborated by a microarray analysis of polysome-bound RNA in aerobic and hypoxic Perk(+/+) and Perk(-/-) MEFs. This analysis revealed that a subset of proangiogenic transcripts is preferentially translated in a Perk-dependent manner; these transcripts include VCIP, an adhesion molecule that promotes cellular adhesion, integrin binding, and capillary morphogenesis. Taken with the concomitant Perk-dependent translational induction of additional proangiogenic genes identified by our microarray analysis, this study suggests that Perk plays a role in tumor cell adaptation to hypoxic stress by regulating the translation of angiogenic factors necessary for the development of functional microvessels and further supports the contention that the Perk pathway could be an attractive target for novel antitumor modalities.
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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".