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Updated: Apr 22, 2026

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
The transcriptomic profile of ovarian cancer grading
Cindy Q Yao1, Francis Nguyen, Syed Haider
1Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Abstract:
Ovarian carcinoma is the leading cause of gynecological malignancy, with the serous subtype being the most commonly presented subtype. Recent studies have demonstrated that grade does not yield significant prognostic information, independent of TNM staging. As such, several different grading systems have been proposed to reveal morphological characteristics of these tumors, however each yield different results. To help address this issue, we performed a rigorous computational analysis to better understand the molecular differences that fundamentally explain the different grades and grading systems. mRNA abundance levels were analyzed across 334 total patients and their association with each grade and grading system were assessed. Few molecular differences were observed between grade 2 and 3 tumors when using the International Federation of Gynecology and Obstetrics (FIGO) grading system, suggesting their molecular similarity. In contrast, grading by the Silverberg system reveals that grades 1-3 are molecularly equidistant from one another across a spectrum. Additionally, we have identified a few candidate genes with good prognostic information that could potentially be used for classifying cases with similar morphological appearances.

