Cisplatin increases B‐cell‐lymphoma‐2 expression <i>via</i> activation of protein kinase C and Akt2 in endometrial cancer cells
Bibliographic record
Abstract
Human carcinomas often show resistance to cisplatin and Bcl-2 is associated with resistance to cisplatin. However, Bcl-2 regulation on cisplatin treatment in human cancers is unknown. Here, we show a novel mechanism by which cisplatin treatment promotes resistance by increasing the expression of Bcl-2 mRNA. Bcl-2 mRNA and protein expression was increased in cisplatin-resistant endometrial cancer cell lines (KLE and HEC-1-A), but not in cisplatin-sensitive cell line (Ishikawa). Cisplatin-mediated increase in Bcl-2 expression was blocked by combination with either actinomycin D or cycloheximide. In addition, Bcl-2 inhibition by HA14-1 led to increased cisplatin-induced apoptosis in KLE and HEC-1-A, whereas Bcl-2 overexpression in Ishikawa led to decreased cisplatin-induced apoptosis. Inhibition of protein kinase C (PKC) activity prevented cisplatin-dependant increase in Bcl-2 mRNA, and induced apoptosis in KLE cells. Furthermore, PKC inhibition was associated with decreased Akt and NF-κB activity. Cells stably expressing shRNA for Akt isoforms revealed that Akt2 was involved in cisplatin-dependant increase in Bcl-2 and apoptosis. Overexpression of Akt2 in Akt2-deficient cells led to increased Bcl-2 expression on cisplatin treatment. Our data suggest a novel regulation pathway of Bcl-2 by cisplatin, via the activation of PKC and Akt2, which has a profound impact on resistance to cisplatin-induced apoptosis in endometrial cancer 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 | 0.000 |
| 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".