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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
Coordinately up-regulated genes in ovarian cancer
C D Hough1, K R Cho, A B Zonderman
1Laboratory of Cellular and Molecular Biology, National Institute on Aging, Baltimore, MD 21224, USA.
Abstract:
A better understanding of the molecular circuitry in normal ovarian tissues and in ovarian cancer will likely provide new targets for diagnosis and therapy. Recently, much has been learned about the genes expressed in ovarian cancer through studies with cDNA arrays and serial analysis of gene expression. However, these methods do not allow highly quantitative analysis of gene expression on a large number of specimens. Here, we have used quantitative real-time RT-PCR in a panel of 39 microdissected ovarian carcinomas of various subtypes to systematically analyze the expression of 13 genes, many of which were previously identified as up-regulated in a subset of ovarian cancers by serial analyses of gene expression. The genes analyzed are glutathione peroxidase 3 (GPX3), apolipoprotein J/clusterin, insulin-like growth factor-binding protein 2, epithelial cell adhesion molecule/GA733-2, Kop protease inhibitor, matrix gla protein, tissue inhibitor of metalloproteinase 3, folate receptor 1, S100A2, signal transducer and activator of transcription 1, secretory leukocyte protease inhibitor, apolipoprotein E, and ceruloplasmin. All of the genes were found overexpressed, some at extremely high levels, in the vast majority of ovarian carcinomas irrespective of the subtype. Interestingly, GPX3 was found at much higher levels in tumors with clear cell histology and may represent a biomarker for this subtype. Some of the genes studied here may thus represent targets for early detection ovarian cancer. The gene expression patterns were not associated with age at diagnosis, stage, or K-ras mutation status in ovarian cancer. We find that several genes are coordinately regulated in ovarian cancer, likely representing the fact that many genes are activated as part of common signaling pathways or that extensive cross-talk exists between several pathways in ovarian cancer. A statistical analysis shows that genes commonly up-regulated in ovarian cancer may result from the aberrant activation of a limited number of pathways, providing promising targets for novel therapeutic strategies.
Insights
Researchers analyzed 13 genes in ovarian cancer using quantitative real-time RT-PCR. Many genes were overexpressed, with glutathione peroxidase 3 (GPX3) potentially serving as a clear cell histology biomarker for ovarian cancer detection and therapy.
Area of Science:
- Molecular biology and oncology
- Gene expression analysis in cancer
Background:
- Understanding ovarian cancer molecular circuitry is crucial for developing new diagnostic and therapeutic targets.
- Previous gene expression studies (cDNA arrays, SAGE) lacked quantitative accuracy across many samples.
Purpose of the Study:
- To quantitatively analyze the expression of 13 specific genes in 39 microdissected ovarian carcinomas.
- To identify potential biomarkers for ovarian cancer subtypes and therapeutic targets.
Main Methods:
- Quantitative real-time RT-PCR was employed on a panel of 39 microdissected ovarian carcinomas.
- Expression levels of 13 pre-identified genes, including glutathione peroxidase 3 (GPX3), were systematically analyzed.
Main Results:
- All 13 analyzed genes were overexpressed in the majority of ovarian carcinomas, irrespective of subtype.
- Glutathione peroxidase 3 (GPX3) showed significantly higher levels in clear cell histology tumors, suggesting potential as a subtype biomarker.
- Coordinated regulation of several genes was observed, indicating common signaling pathway activation or pathway cross-talk in ovarian cancer.
Conclusions:
- Aberrant activation of a limited number of pathways likely drives the common gene up-regulation in ovarian cancer.
- These findings offer promising targets for novel ovarian cancer therapeutic strategies and early detection biomarkers.
- Gene expression patterns were not significantly associated with patient age, tumor stage, or K-ras mutation status.
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