Rescue of paclitaxel sensitivity by repression of Prohibitin1 in drug-resistant cancer cells
Bibliographic record
Abstract
Paclitaxel has emerged as a front line treatment for aggressive malignancies of the breast, lung, and ovary. Successful therapy of cancer is frequently undermined by the development of paclitaxel resistance. There is a growing need to find other therapeutic targets to facilitate treatment of drug-resistant cancers. Using a proteomics approach, elevated levels of Prohibitin1 (PHB1) and GSTpi were found associated with paclitaxel resistance in discrete subcellular fractions of two drug-resistant sublines relative to their sensitive sublines. Immunofluorescence staining and fractionation studies revealed increased levels of PHB1 on the surface of resistant cell lines. Transiently silencing either PHB1 or GSTpi gene expression using siRNA in the paclitaxel-resistant cancer cell sublines partially sensitized these cells toward paclitaxel. Intriguingly, silencing PHB1 but not GSTpi resulted in activation of the intrinsic apoptosis pathway in response to paclitaxel. Similarly, stably silencing either PHB1 or GSTpi significantly improved paclitaxel sensitivity in A549TR cells both in vitro and in vivo. Our results indicate that PHB1 is a mediator of paclitaxel resistance and that this resistance may depend on the cellular localization of the protein. We suggest PHB1 as a potential target for therapeutic strategies for the treatment of drug-resistant tumors.
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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 | 0.001 |
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".