Tissue and plasma levels of galectins in patients with high grade serous ovarian carcinoma as new predictive biomarkers
Bibliographic record
Abstract
Abstract Galectins are moving closer to center stage in detecting glycosylation aberration in cancer cells. Here, we have investigated the expression of galectins in ovarian cancer (OC) and examined their potential as biomarkers in tissues and blood plasma samples of high grade serous ovarian carcinoma (HGSC) patients. In tissues, we found that increased protein expression of stromal gal-1 and epithelial gal-8/9 was associated with a poor response to treatment of HGSC patients. Gal-8/9 were both independent predictors of chemoresistance and overall survival (OS), respectively. This galectin signature increased the predictive value of the cancer antigen 125 (CA125) on 5-year disease-free survival (DFS), post-chemotherapy treatment and 5-year OS. In CA125 LOW patients, epithelial gal-9 was associated with a lower 5-year OS while stromal gal-1 and epithelial gal-8 were both associated with a lower 5-year DFS. Such negative predictive value of gal-8 and gal-9 was also found using plasma samples. In both cases, high plasma levels of gal-8 and gal-9 was associated with a lower OS and DFS. Overall, these data suggest that galectins may be promising biomarkers to identify subgroups of HGSC patients with poorer prognosis. Our study also contributes to better define the heterogeneity of the disease.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".