The DEAD-box RNA helicase DDX28 suppresses cell migration and 3D growth and invasion in MDA-MB-231 cells by altering bioenergetics
Bibliographic record
Abstract
Hypoxia is a common characteristic of the tumor microenvironment leading to aggressive phenotypes. A major response to hypoxia is through the induction of gene programs by the hypoxia-inducible factors (HIF). Previously, we showed that the DEAD-box RNA helicase DDX28 negatively regulates hypoxic eIF4E2-directed translation through its interaction with HIF-2α. We hypothesized that DDX28 is a tumor suppressor that represses the oncogenic HIF-2α axis. Here, we overexpress DDX28 in MDA-MB-231 breast cancer and U87MG glioblastoma cells that have very low and normal endogenous levels of DDX28, respectively, compared with noncancerous HEK293. We show that DDX28 suppresses cell migration, spheroid growth, and invasion in MDA-MB-231, but not U87MG cells. However, suppression is not through the HIF-2α gene program, but through DDX28 impacting cellular bioenergetics. DDX28 levels altered how cells utilized mitochondrial respiration and glycolysis for ATP generation. Furthermore, the pharmacological inhibition of these processes specifically reversed the effects of DDX28 overexpression. This study shows that low endogenous DDX28 levels promote hypoxic migration, and growth/invasion in three-dimensional structures in cells that have a bioenergetic profile that favors glycolysis such as MDA-MB-231.
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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".