ERCC1 expression as a molecular marker of cisplatin resistance in human cervical tumor cells
Bibliographic record
Abstract
Cisplatin is a valuable adjuvant to radiotherapy for the treatment of cervical cancer. Because the advantage of combining cisplatin with radiotherapy is likely to be attributable to additive cell killing by these 2 agents, such protocols should primarily benefit patients who have inherently cisplatin-sensitive tumors. Development of a molecular assay to rapidly evaluate the cisplatin responsiveness of cervical tumors would thus be extremely valuable. We investigated whether high pre-treatment mRNA levels of the ERCC1 nucleotide excision repair gene are predictive of cisplatin resistance in early-passage human cervical cancer cells, as they are in several other tumor types. Expression of the ERCC1 gene at the mRNA and protein levels was established by Northern and Western blotting, respectively, in a panel of single-cell-derived cervical carcinoma cell lines that exhibited a wide range of inherent sensitivity to cisplatin. There was a significant (p </= 0.011) correlation between ERCC1 mRNA levels and cisplatin resistance in these cell lines. However, there was no obvious relationship between ERCC1 protein levels and cisplatin resistance. Thus, the association between high ERCC1 mRNA levels and cisplatin resistance might be an epiphenomenon. Nonetheless, pre-treatment ERCC1 mRNA levels may be a useful molecular marker for identifying cervical tumors likely to be refractory to cisplatin, and further investigation in clinical biopsy material is warranted.
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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 | 0.001 | 0.001 |
| 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".