MétaCan
Menu
Back to cohort
Record W2596887331 · doi:10.1093/biolreprod/81.s1.562

Hyaluronidase-1 (Hyal-1) Is Differentially Expressed in Epithelial Ovarian Cancer of Distinct Histopathological Subtypes.

2009· article· en· W2596887331 on OpenAlexaff
Paule Héléna Yoffou, Diane Provencher, Anne‐Marie Mes‐Masson, Eurı́dice Carmona

Bibliographic record

VenueBiology of Reproduction · 2009
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsBiologySerous fluidOvarian cancerMetastasisCancer researchPerineural invasionCancerPathologyInternal medicineOncologyMedicine

Abstract

fetched live from OpenAlex

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic cancer in most Western countries. Because of its asymptomatic growth and the lack of effective screening methods, about 70% of all cases are diagnosed in an advanced stage. Although most of the patients respond to chemotherapy initially, recurrence rate is very high resulting in poor prognosis. Furthermore, EOCs are morphologically heterogeneous, and different histopathological subtypes have distinct molecular characteristics and diverse response to treatment. In the present work we investigated the expression pattern of hyaluronidase-1 (Hyal-1) in different histopathological EOC subtypes as well as in EOC cell lines. The HYAL1 gene (together with those of HYAL2 and HYAL3) is located on the short arm of chromosome 3 (3p), a region described to contain tumour suppressor genes implicated in lung and ovarian cancer pathogenesis. However, levels of both Hyal-1 and its substrate, hyaluronic acid (hyaluronan), have also been reported to be increased in bladder, prostate and head and neck cancers, and to be implicated in tumor progression and metastasis. However, up to date, no studies have been conducted to analyze the transcriptional status of this enzyme and its role in EOC. Herein, quantitative RT-PCR was performed in ovarian tumor samples classified as borderline, serous, endometrioid, mucinous or clear cell. Distinct grade and stage were also attributed according to the International Federation of Gynecology and Obstetrics (FIGO) criteria. Cell lines derived from different EOC subtypes or from normal ovarian epithelial cells (NOSE) were also included in these analyses. Our results show that Hyal-1 expression was significantly increased in clear cell and mucinous EOC samples when compared to borderline tumors. In contrast, lower levels of Hyal-1 expression were observed in serous samples, and no difference was observed between endometrioid and borderline tumors. Accordingly, higher mRNA expression levels and higher enzymatic activity were observed in EOC cell lines derived from clear cell tumors. Cell lines derived from serous tumors had no detectable enzymatic activity and low levels of mRNA expression. However, treatment of one of these cell lines with 5-Azacytidine (DNA methyltransferase inhibitor) restored its Hyal-1 expression and activity, indicating that transcription repression through epigenetic regulation is occurring in serous EOCs. No correlation could be made between Hyal-1 expression and ovarian tumor grade or stage. In conclusion, our results show that Hyal-1 is differentially expressed in distinct EOC subtypes, with high expression levels in mucinous and clear cell carcinomas and low in serous adenocarcinoma. It is important to mention that specificity and sensitivity of the CA-125 antigen is particularly not good for these two former EOC subtypes. Therefore, Hyal-1 could be a potential marker for these tumors; however, further validation using a bigger tissue/serum sample cohort is needed. Supported by NSERC, RRCancer and FRSQ. (poster)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.294
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueBiology of ReproductionSame topicOvarian cancer diagnosis and treatmentFrench-language works237,207