Eukaryotic elongation factor 2 kinase upregulates the expression of proteins implicated in cell migration and cancer cell metastasis
Bibliographic record
Abstract
Eukaryotic elongation factor 2 kinase (eEF2K) negatively regulates the elongation phase of mRNA translation and hence protein synthesis. Increasing evidence indicates that eEF2K plays an important role in the survival and migration of cancer cells and in tumor progression. As demonstrated by two‐dimensional wound‐healing and three‐dimensional transwell invasion assays, knocking down or inhibiting eEF2K in cancer cells impairs migration and invasion of cancer cells. Conversely, exogenous expression of eEF2K or knocking down eEF2 (the substrate of eEF2K) accelerates wound healing and invasion. Importantly, using LC‐HDMS E analysis, we identify 150 proteins whose expression is decreased and 73 proteins which are increased upon knocking down eEF2K in human lung carcinoma cells. Of interest, 34 downregulated proteins are integrins and other proteins implicated in cell migration, suggesting that inhibiting eEF2K may help prevent cancer cell mobility and metastasis. Interestingly, eEF2K promotes the association of integrin mRNAs with polysomes, providing a mechanism by which eEF2K may enhance their cellular levels. Consistent with this, genetic knock down or pharmacological inhibition of eEF2K reduces the protein expression levels of integrins. Notably, pharmacological or genetic inhibition of eEF2K almost completely blocked tumor growth and effectively prevented the spread of tumor cells in vivo . High levels of eEF2K expression were associated with invasive carcinoma and metastatic tumors. These data provide the evidence that eEF2K is a new potential therapeutic target for preventing tumor metastasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".