RETRACTED ARTICLE: Reduced expression of AMPK-β1 during tumor progression enhances the oncogenic capacity of advanced ovarian cancer
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
AMP-activated protein kinase (AMPK) is a key energy sensor that is involved in regulating cell metabolism. Our previous study revealed that the subunits of the heterotimeric AMPK enzyme are diversely expressed during ovarian cancer progression. However, the impact of the variable expression of these AMPK subunits in ovarian cancer oncogenesis remains obscure. Here, we provide evidence to show that reduced expression of the AMPK-β1 subunit during tumor progression is associated with the increased oncogenic capacity of advanced ovarian cancer cells. Immunohistochemical analysis revealed that AMPK-β1 levels were reduced in advanced-stage (P = 0.008), high-grade (P = 0.013) and metastatic ovarian cancers (P = 0.008). Intriguingly, down-regulation of AMPK-β1 was progressively reduced from tumor stages 1 to 3 of ovarian cancer. Functionally, enforced expression of AMPK-β1 inhibited ovarian-cancer-cell proliferation, anchorage-independent cell growth, cell migration and invasion. Conversely, depletion of AMPK-β1 by siRNA enhanced the oncogenic capacities of ovarian cancer cells, suggesting that the loss of AMPK-β1 favors the aggressiveness of ovarian cancer. Mechanistically, enforced expression of AMPK-β1 increased AMPK activity, which, in turn, induced cell-cycle arrest via inhibition of AKT/ERK signaling activity as well as impaired cell migration/invasion through the suppression of JNK signaling in ovarian cancer cells. Taken together, these findings suggest that the reduced expression of AMPK-β1 confers lower AMPK activity, which enhances the oncogenic capacity of advanced-stage ovarian cancer.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.018 |
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".