Role of aberrant glycosylation in ovarian cancer dissemination
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, and understanding the molecular changes associated with EOC etiology could lead to the identification of novel targets for more effective therapeutic interventions. Glycosylation represents a post-translational modification (PTM) of proteins playing a major role in various cellular functions. Moreover, glycosylation participates in major pathobiological events during tumor progression, as aberrant expression of glycan structures has been shown to contribute in alterations of specific cellular onco-phenotypes, including tumor cell proliferation, migration and invasion. This review aims to describe what is currently known about aberrant glycosylation in EOC, and more specifically, the contribution of aberrant O-linked glycosylation in EOC progression. We also discuss our findings about the altered GALNT3 overexpression in EOC and its involvement in disease dissemination through aberrant mucin O-glycosylation, as well as the potential to exploit the role of GALNT3 in understanding the general mechanisms of abnormal glycosylation implicated in EOC spreading. Further analyses in cancer glycobiology could significantly enhance our understanding of the molecular mechanisms of cancer progression, including EOC dissemination, and could lead to the identification of novel biomarkers/therapeutic targets for better management of this deadly disease. Biomedical Reviews 2014; 25: 83-92.
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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 | 0.001 | 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".