EEF2K silencing inhibits tumour progression through repressing SPP1 and synergises with BET inhibitors in melanoma
Bibliographic record
Abstract
BACKGROUND: Despite the remarkable breakthroughs achieved in the management of metastatic melanoma using immunotherapy and targeted therapies, long-term clinical efficacy is often compromised due to dose-limiting toxicity and innate or acquired resistance. Therefore, it is of vital importance to further explore the molecular mechanisms underlying melanoma progression and identify new targeted therapeutic approaches. METHODS: The function of eukaryotic elongation factor-2 kinase (EEF2K) in melanoma were investigated in vitro and in vivo. RNA-seq and chromatin immunoprecipitation (ChIP) assay were undertaken to explore the mechanisms. The antitumor effect of bromodomain and extra terminal domain (BET) inhibitors combined with cytarabine were assessed in melanoma both in vitro and in vivo. RESULTS: EEF2K silencing markedly attenuated the malignant phenotypes of melanoma cells, including proliferation, migration, invasion and metastasis. In contrast, EEF2K overexpression promoted melanoma cell proliferation, migration and invasion. Mechanistically, we demonstrated that EEF2K upregulates the phosphorylation of STAT3 (p-STAT3) at Tyr705, which binds to the promoter region of SPP1 and enhances its transcription, thus facilitating melanoma progression. Transfection-induced re-expression of SPP1 partly negated the inhibitory effect of EEF2K silencing on melanoma, whereas inhibition of SPP1 or STAT3 significantly abolished the efficacy of EEF2K on melanoma cells. Intriguingly, EEF2K silencing combined with BET inhibitor treatment further inhibited cell proliferation and promoted apoptosis in melanoma. We further screened the US FDA-approved antitumour drug library and identified cytarabine as a potential clinically applicable EEF2K inhibitor that could synergise with BET inhibitors in melanoma treatment. CONCLUSION: EEF2K/p-STAT3/SPP1 may be a novel oncogenic pathway in melanoma progression, which could be a target for novel combination therapy for melanoma.
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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 | 0.001 |
| 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".