Role of the transcription factor Ets-1 in cisplatin resistance
Bibliographic record
Abstract
Cisplatin is a DNA damaging agent widely used as a chemotherapeutic agent. A major limitation of the use of this agent is the development of drug resistance within tumors. Several in vitro models exist which enable the investigation of resistance mechanisms, including 2008/C13* ovarian carcinoma cells. C13* cells are variants of 2008 cells, displaying cisplatin resistance following 13 consecutive cisplatin treatments. This model system has led to the identification of several mechanisms that play parts in the multifactorial nature of cisplatin resistance. In this study, we have examined the contribution of a transcription factor, Ets-1, to the cisplatin resistance of C13* cells. Ets-1 is up-regulated in C13* cells as compared with the cisplatin-sensitive 2008 cells and overexpression of this protein in 2008 cells led to a 7-fold increase in resistance. Further studies on a colorectal carcinoma cell line overexpressing Ets-1 indicated that this phenomenon is not cell specific-increased cisplatin resistance correlated to Ets-1 expression. The mechanism of cisplatin resistance elicited by Ets-1 is potentially via transcriptional activation of genes whose products have well-described functions in reducing cisplatin toxicity. Examples, identified via microarray analysis, include metallothioneins and DNA repair enzymes. This is the first report to our knowledge associating expression of Ets-1, a transcription factor whose expression often signals poor prognosis in various cancer types, to cisplatin resistance.
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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".