Subtype Specific Elevated Expression of Hyaluronidase-1 (HYAL-1) in Epithelial Ovarian Cancer
Bibliographic record
Abstract
BACKGROUND: Epithelial ovarian cancer (EOC) is morphologically heterogeneous being classified as serous, endometrioid, clear cell, or mucinous. Molecular genetic analysis has suggested a role for tumor suppressor genes located at chromosome 3p in serous EOC pathogenesis. Our objective was to evaluate the expression of HYAL1, located at chromosome 3p21.3, in these EOC subtypes, and to investigate its correlation with the expression of steroid hormone receptors. METHODOLOGY/PRINCIPAL FINDINGS: We determined the mRNA expression of HYAL1, estrogen receptor (ER)-α, ERβ and progesterone receptor (PR) in EOC tumor samples and cell lines using quantitative RT-PCR. We also examined the expression of these genes in a publicly available microarray dataset. HYAL-1 enzyme activity was measured in EOC cell lines and in plasma samples from patients. We found that HYAL1 mRNA expression was elevated in clear cell and mucinous EOC tissue samples, but not in serous and endometrioid samples, normal ovaries or benign tumors. Similar results were obtained by two different techniques and with tissue sample cohorts from two independent institutions. Concordantly, HYAL1 mRNA levels and enzymatic activity were elevated only in EOC cell lines derived from clear cell and mucinous subtypes. We also showed that HYAL1 mRNA was inversely correlated to that of ERα specifically in clear cell and mucinous EOCs. Additionally, ectopic expression of ERα in a clear cell EOC cell line (ER- and PR-negative) induced 50% reduction of HYAL1 mRNA expression, supporting a role of ERα in HYAL1 gene regulation. Significantly, HYAL-1 activity was also high in the plasma of patients with these EOC subtypes. CONCLUSIONS/SIGNIFICANCE: This is the first report showing high HYAL-1 levels in EOC and demonstrating HYAL1 gene repression by ERα. Our results identify Hyaluronidase-1 as a potential target/biomarker for clear cell and mucinous EOCs and especially in tumors with low ERα levels.
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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".