Comparative proteome analysis of human epithelial ovarian cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Epithelial ovarian cancer is a devastating disease associated with low survival prognosis mainly because of the lack of early detection markers and the asymptomatic nature of the cancer until late stage. Using two complementary proteomics approaches, a differential protein expression profile was carried out between low and highly transformed epithelial ovarian cancer cell lines which realistically mimic the phenotypic changes observed during evolution of a tumour metastasis. This investigation was aimed at a better understanding of the molecular mechanisms underlying differentiation, proliferation and neoplastic progression of ovarian cancer. RESULTS: The quantitative profiling of epithelial ovarian cancer model cell lines TOV-81D and TOV-112D generated using iTRAQ analysis and two-dimensional electrophoresis coupled to liquid chromatography tandem mass spectrometry revealed some proteins with altered expression levels. Several of these proteins have been the object of interest in cancer research but others were unrecognized as differentially expressed in a context of ovarian cancer. Among these, series of proteins involved in transcriptional activity, cellular metabolism, cell adhesion or motility and cytoskeleton organization were identified, suggesting their possible role in the emergence of oncogenic pathways leading to aggressive cellular behavior. CONCLUSION: The differential protein expression profile generated by the two proteomics approaches combined to complementary characterizations studies will open the way to more exhaustive and systematic representation of the disease and will provide valuable information that may be helpful to uncover the molecular mechanisms related to epithelial ovarian cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it