The DNA double-stranded break repair protein endo-exonuclease as a therapeutic target for cancer
Bibliographic record
Abstract
DNA repair mechanisms are crucial for the maintenance of genomic stability and are emerging as potential therapeutic targets for cancer. In this study, we report that the endo-exonuclease, a protein involved in the recombination repair process of the DNA double-stranded break pathway, is overexpressed in a variety of cancer cells and could represent an effective target for developing anticancer drugs. We identify a dicationic diarylfuran, pentamidine, which has been used clinically to treat opportunistic infections and is an inhibitor of the endo-exonuclease as determined by enzyme kinetic assay. In clonogenic and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays as well as in the in vivo Lewis lung carcinoma mouse tumor model, pentamidine is shown to possess the ability to selectively kill cancer cells. The LD50 of pentamidine on cancer cells maintained in vitro is correlated with the endo-exonuclease enzyme activity. Tumor cell that has been treated with pentamidine is reduced in the endo-exonuclease as compared with the untreated control. Furthermore, pentamidine synergistically potentiates the cytotoxic effect of DNA strand break and cross-link-inducing agents such as mitomycin C, etoposide, and cisplatin. In addition, we used the small interfering RNA for the mouse homologue of the endo-exonuclease to down-regulate the level of endo-exonuclease in the mouse myeloma cell line B16F10. Down-regulation of the endo-exonuclease sensitizes the cell to 5-fluorouracil. These studies suggested the endo-exonuclease enzyme as a novel potential therapeutic target for cancer.
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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 | 0.000 |
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".