Abstract POSTER-TECH-1130: Identification of putative genes involved in early steps of epithelial ovarian cancer pathogenesis
Bibliographic record
Abstract
Abstract Epithelial ovarian cancer (EOC) is a deadly q disease and the related statistics are alarming with 15000 lives claimed in USA each year. EOC is a subtle disease since the majority of patients at presentation are diagnosed with a higher stage disease characterized by an aggressive potential. Early diagnosis seems to be of importance since complete cure can reach up to 90% if the disease is diagnosed at an early stage. Therefore, biological markers to detect patients at an early stage of the disease progression are urgently needed. In this study, the focus was centered on the transcription factor E2F5 used as biomarker and highly expressed at early stages of EOC pathogenesis. Therefore, we hypothesize that downstream genes regulated by this transcription factor might be also involved in the early events leading to ovarian cancer and can therefore potentially serve as useful biomarkers. Chromatin Immuno-Precipitation (ChIP) was performed using two ovarian cancer cell lines SK-OV-3 and OVCAR-3 and an antibody against E2F5 transcription factor. The enriched chromatin was cloned and sequenced before using available databases for BLAST and identification of downstream regulated genes. A short list of relevant genes, all involved in cancer, was obtained and analysis for their putative role in early detection of EOC is ongoing. The selected genes will be also validated using frozen human ovarian tissues available in our institution. ChIP is a robust technology to identify genes regulated by E2F5 with a putative potential to play a crucial role in the early steps of ovarian cancer pathogenesis. Citation Format: Yahya Tamimi, Ikram Burney, Moza Al-Kalbani, Ritu Lakhtari, Roseline Godbout, Mansour Al-Moundhri. Identification of putative genes involved in early steps of epithelial ovarian cancer pathogenesis [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr POSTER-TECH-1130.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".