Decreased transcription-coupled nucleotide excision repair capacity is associated with increased p53- and MLH1-independent apoptosis in response to cisplatin
Bibliographic record
Abstract
BACKGROUND: One of the most commonly used classes of anti-cancer drugs presently in clinical practice is the platinum-based drugs, including cisplatin. The efficacy of cisplatin therapy is often limited by the emergence of resistant tumours following treatment. Cisplatin resistance is multi-factorial but can be associated with increased DNA repair capacity, mutations in p53 or loss of DNA mismatch repair capacity. METHODS: RNA interference (RNAi) was used to reduce the transcription-coupled nucleotide excision repair (TC-NER) capacity of several prostate and colorectal carcinoma cell lines with specific defects in p53 and/or DNA mismatch repair. The effect of small inhibitory RNAs designed to target the CSB (Cockayne syndrome group B) transcript on TC-NER and the sensitivity of cells to cisplatin-induced apoptosis was determined. RESULTS: These prostate and colon cancer cell lines were initially TC-NER proficient and RNAi against CSB significantly reduced their DNA repair capacity. Decreased TC-NER capacity was associated with an increase in the sensitivity of tumour cells to cisplatin-induced apoptosis, even in p53 null and DNA mismatch repair-deficient cell lines. CONCLUSION: The present work indicates that CSB and TC-NER play a prominent role in determining the sensitivity of tumour cells to cisplatin even in the absence of p53 and DNA mismatch repair. These results further suggest that CSB represents a potential target for cancer therapy that may be important to overcome resistance to cisplatin in the clinic.
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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) | 0.002 | 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".