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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
Integrative network analysis to identify aberrant pathway networks in ovarian cancer
Li Chen1, Jianhua Xuan, Jinghua Gu
1The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA. lchen06@vt.edu
Abstract:
Ovarian cancer is often called the 'silent killer' since it is difficult to have early detection and prognosis. Understanding the biological mechanism related to ovarian cancer becomes extremely important for the purpose of treatment. We propose an integrative framework to identify pathway related networks based on large-scale TCGA copy number data and gene expression profiles. The integrative approach first detects highly conserved copy number altered genes and regards them as seed genes, and then applies a network-based method to identify subnetworks that can differentiate gene expression patterns between different phenotypes of ovarian cancer patients. The identified subnetworks are further validated on an independent gene expression data set using a network-based classification method. The experimental results show that our approach can not only achieve good prediction performance across different data sets but also identify biological meaningful subnetworks involved in many signaling pathways related to ovarian cancer.
Insights
This study introduces a new computational method to find gene networks in ovarian cancer. The approach helps identify biological pathways crucial for understanding and treating this
Area of Science:
- Genomics
- Bioinformatics
- Cancer Biology
Background:
- Ovarian cancer, known as the 'silent killer,' presents diagnostic and prognostic challenges due to difficulties in early detection.
- Understanding the underlying biological mechanisms of ovarian cancer is critical for developing effective therapeutic strategies.
Purpose of the Study:
- To develop an integrative computational framework for identifying pathway-related gene networks in ovarian cancer.
- To leverage large-scale The Cancer Genome Atlas (TCGA) copy number alteration (CNA) data and gene expression profiles for network discovery.
Main Methods:
- The proposed framework identifies highly conserved CNA genes as seed nodes.
- A network-based approach is employed to detect subnetworks differentiating ovarian cancer patient phenotypes.
- Subnetworks are validated using a network-based classification method on an independent gene expression dataset.
Main Results:
- The integrative framework achieved robust prediction performance across different datasets.
- The method successfully identified biologically meaningful subnetworks implicated in ovarian cancer signaling pathways.
- The identified subnetworks demonstrate potential for improving ovarian cancer patient stratification and treatment.
Conclusions:
- The developed integrative framework is effective for identifying key biological networks in ovarian cancer.
- This approach enhances the understanding of ovarian cancer's molecular mechanisms and aids in early detection and prognosis.
- The findings provide a foundation for targeted therapies and improved patient outcomes in ovarian cancer treatment.
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