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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
Integrating genome and functional genomics data to reveal perturbed signaling pathways in ovarian cancers
1Dept. Biomedical Informatics, Univ. Pittsburgh, PA 15232.
Abstract:
Cancers are genetic diseases, driven by somatic mutations that perturb cellular signaling systems. In this study, we aim to reveal the signal transduction pathways that are perturbed by mutations in ovarian cancer. Our approach searches for genetic mutations that lead to a common cellular response, e.g., differential expression of a set of functional related genes. To this end, we first developed a knowledge mining approach to identify functional expression modules; we then developed a graph-based data mining approach to identify mutations that are highly related to the functional modules, as a means to re-constitute signal pathways. Our results indicate that unification of knowledge mining with data mining significantly enhance identification of potential signaling pathways in ovarian cancers.
Insights
This study identifies key signal transduction pathways altered by genetic mutations in ovarian cancer. By combining knowledge and data mining, researchers can better understand cancer signaling and discover potential therapeutic targets.
Area of Science:
- Oncology
- Genetics
- Bioinformatics
Background:
- Cancers originate from genetic mutations that disrupt cellular signaling.
- Ovarian cancer progression is linked to perturbed signal transduction pathways.
- Identifying these pathways is crucial for understanding cancer development.
Purpose of the Study:
- To uncover signal transduction pathways affected by mutations in ovarian cancer.
- To develop a computational approach for identifying mutation-driven pathway alterations.
- To enhance the discovery of ovarian cancer-specific signaling disruptions.
Main Methods:
- Developed a knowledge mining technique to identify functional gene expression modules.
- Employed a graph-based data mining approach to link mutations with expression modules.
- Integrated knowledge and data mining for pathway reconstruction.
Main Results:
- Successfully identified functional expression modules related to cellular responses.
- Linked specific genetic mutations to these functional modules.
- Demonstrated that the combined approach significantly improves the identification of perturbed signaling pathways in ovarian cancer.
Conclusions:
- Unifying knowledge and data mining offers a powerful strategy for discovering cancer-related signaling pathways.
- This integrated approach can reveal novel insights into the molecular mechanisms of ovarian cancer.
- The findings pave the way for identifying new therapeutic targets in ovarian cancer treatment.
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