Glycoproteomic identification of potential glycoprotein biomarkers in ovarian cancer proximal fluids
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is the leading cause of death among all gynecological disorders. Aberrant glycosylation, or more specifically, increased sialylation of proteins has been observed in ovarian cancer. Several sialyltransferase genes have been shown to be up-regulated at both the mRNA and protein levels in a number of cancers, including that of the ovary. ST6GAL1 (β-galactosamide α2,6-sialyltranferase 1) gene expression has previously been shown to be upregulated in ovarian cancers of all major subtypes. METHODS: We have identified the sialome (i.e., sialic acid containing glycoproteins) of biological fluids from ovarian cancer patients and ovarian cancer cell lines utilizing tandem mass spectrometry as a potential pool of novel biomarker candidates. The sialoglycopeptides from four ovarian cancer cell lines, pooled ascites (n=13) and ovarian cyst (n=14) fluids from ovarian cancer patients were enriched utilizing affinity to agarose-immobilized Elderberry lectin (Sambucus nigra agglutinin) and magnetic hydrazide beads folowing periodate-mediated oxidation of sialic acids. Benign ovarian cyst (n=10) and peritoneal effusion (n=20) fluids were analyzed in the same fashion to serve as controls. PNGase F deglycosylated peptides were identified using electrospray ionization-LTQ Orbitrap tandem mass spectrometry. RESULTS: In all of the samples analyzed in the glycoproteomic portion of the study, we have identified 579 glycosylation sites on 333 proteins. Of these, 13 were exclusively identified in biological fluids from ovarian cancer patients, and another eight were common to these fluids and the ovarian cancer cell line supernatants. CONCLUSIONS: The proteins identified in the present study could form the basis for future studies examining and quantifying their sialylation status as biomarkers of ovarian 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".