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Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks
Le Yang1, Runpu Chen1, Thomas Melendy1
1Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY 14203, USA.
Background:
The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms.
Methods:
In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously.
Results:
To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.
Insights
This study introduces MultiFDRnet, a novel method for identifying cancer pathways using multiplex networks. It effectively detects perturbed subnetworks by integrating multiple protein-protein interaction networks for improved accuracy.
Area of Science:
- Computational Biology
- Bioinformatics
- Cancer Genomics
Background:
- Large-scale cancer genome studies aim to identify cancer driver genes and molecular pathways.
- Network-based methods use genomics data and protein-protein interaction (PPI) network topology to detect perturbed subnetworks as potential cancer pathways.
- Variability in PPI network structures and incompleteness in context-specific networks challenge existing subnetwork detection algorithms.
Purpose of the Study:
- To propose a novel method, MultiFDRnet, to address the limitations of existing subnetwork detection algorithms in cancer pathway identification.
- To develop a method that can effectively utilize multiple PPI networks and handle incomplete topological structures.
- To improve the accuracy and reliability of detecting cancer-related subnetworks.
Main Methods:
- Modeled a set of PPI networks as a multiplex network to preserve individual network topology and introduce inter-network dependencies.
- Developed MultiFDRnet to detect significantly perturbed subnetworks by simultaneously using all structural information from the multiplex network.
- Incorporated genomics data with the integrated topological information of multiple PPI networks.
Main Results:
- Benchmark analysis on simulated and real cancer data demonstrated the effectiveness of MultiFDRnet.
- The method successfully detected significantly perturbed subnetworks supported by multiple PPI networks.
- MultiFDRnet identified novel modular structures within context-specific PPI networks.
Conclusions:
- MultiFDRnet offers a robust approach for identifying cancer pathways by integrating information from multiple PPI networks.
- The multiplex network modeling effectively addresses challenges posed by varying and incomplete network structures.
- The method enhances the discovery of cancer-related molecular subnetworks and potential therapeutic targets.
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