Abstract B41: REV7 is a possible prognostic predictor and a potential therapeutic target in human malignancy
Bibliographic record
Abstract
Abstract Human REV7 (also known as MAD2L2 and MAD2B) is involved in DNA repair, cell cycle regulation, gene transcription and carcinogenesis, and is a key protein in translesion DNA synthesis. Here, we present our study to evaluate the significance of REV7 expression in human malignancy and its possibility to be a molecular target for cancer therapy. REV7 expression was assessed in epithelial ovarian cancer (EOC) and diffuse large B-cell lymphoma (DLBCL) by immunohistochemical staining. REV7 expression was detected in the majority of EOCs (92.0%) with especially high levels of expression frequently observed in ovarian clear cell carcinomas (CCCs) (73.5%) compared with that of non-CCCs (53.4%). Enhanced immunoreactivity to REV7 was associated with poor prognosis represented by reduced progression-free survival in advanced stage (stages II to IV) EOC. REV7 expression was also assessed in DLBCL, the most common type of non-Hodgkin lymphoma, in which high REV7 expression was associated with significantly shorter overall survival and progression-free survival. The effects of REV7 knockdown on cell proliferation and chemosensitivity in CCC cells were also analyzed in vitro and in vivo. REV7 knockdown in CCC cells decreased cell proliferation without affecting cell cycle distribution. Additionally, the number of apoptotic cells and DNA damaged cells were increased after cisplatin treatment. In a nude mouse tumor xenograft model, inoculated REV7-knockdown tumors showed significantly reduced tumor volumes after cisplatin treatment compared with those of the control group. These findings indicate that high REV7 expression is associated with poor prognosis in EOC and DLBCL, and depletion of REV7 enhances sensitivity to cisplatin treatment in CCC, suggesting that REV7 is a candidate for molecular target in human malignancy. Citation Format: Yoshiki Murakumo, Kaoru Niimi, Sosei Okina. REV7 is a possible prognostic predictor and a potential therapeutic target in human malignancy [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr B41.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".