Enhanced gemcitabine cytotoxicity with knockdown of multidrug resistance protein genes in human cholangiocarcinoma cell lines
Bibliographic record
Abstract
BACKGROUND AND AIM: Cholangiocarcinoma (CCA) is an often fatal primary cancer of the liver that tends to be resistant to chemotherapy. Multidrug resistance proteins (MRPs) contribute to the chemoresistance of these tumors. The objectives of the study were to document MRP expression profiles in two representative human intrahepatic and extrahepatic CCA cells lines (HuCCT1 and KMBC, respectively) and gemcitabine-induced cytotoxicity prior to and following MRP knockdown. METHODS: Multidrug resistance protein mRNA and protein expression were documented by real-time reverse transcription-polymerase chain reaction and western blots, respectively. MRP knockdown was achieved with lentivirus small hairpin RNA constructs. RESULTS: Prior to gemcitabine exposure, MRP1, MRP2, MRP4, MRP5, and MRP6 mRNA were expressed in HuCCT1 cells and MRP1, MRP3, MRP4, and MRP5 in KMBC cells. Following gemcitabine exposure, MRP5 and MRP6 expressions were significantly upregulated in HuCCT1 cells and MRP5 in KMBC cells. In HuCCT1 cells, although MRP5 knockdown had no effect, MRP6 knockdown significantly increased gemcitabine-induced cytotoxicity. In KMBC cells, MRP5 knockdown significantly increased gemcitabine cytotoxicity. CONCLUSIONS: Inhibition of MRP6 expression in intra-hepatic and MRP5 in extra-hepatic should be explored as potential treatments for CCA in humans.
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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".