Ovarian cancer ascites increase Mcl-1 expression in tumor cells through ERK1/2-Elk-1 signaling to attenuate TRAIL-induced apoptosis
Bibliographic record
Abstract
BACKGROUND: Ascites may affect the progression of ovarian cancer (OC). In particular, soluble factors present in OC ascites can create a protective environment for tumor cells that promote de novo resistance to drug- and death receptor-induced apoptosis. However, the underlying molecular mechanisms responsible for ascites-induced drug resistance are not well characterized. METHODS: Using human OC cell lines and tissues microarrays of human OC biopsies, we assessed the mechanism by which OC ascites increase Mcl-1 expression using Western blots, chemical inhibitors of ERK and small-inhibitory RNA treatments. RESULTS: In the present study, we found that both Mcl-1 mRNA and protein levels were upregulated within 2 h upon treatment of OC cells with ascites obtained from women with advanced OC. In contrast, the expression of other Bcl-2 family antiapoptotic members such as Bcl-2 and Bcl-XL was not affected by ascites. An increase of Mcl-1 expression was consistently observed across different ascites from women with advanced serous OC. The knockdown of Mcl-1 significantly blocked ascites-induced Mcl-1 upregulation and ascites-mediated inhibition of TRAIL-induced apoptosis. Ascites induced a rapid phosphorylation of ERK1/2 and Elk-1 transcription factor. Furthermore, we found that ERK1/2 inhibition or Elk-1 knockdown was sufficient to block ascites-induced Mcl-1 expression. In high grade serous OC, we found a positive correlation between phosphorylated ERK1/2 and Mcl-1 expression. CONCLUSIONS: These results indicate that ascites-induced ERK1/2/Elk-1 signaling is critical for Mcl-1 expression and for the ascites-mediated attenuation of TRAIL-induced apoptosis. The ERK1/2/Elk-1/Mcl-1 pathway represents a novel mechanism by which ascites induce de novo TRAIL resistance in OC cells.
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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".