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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Extending pathways and processes using molecular interaction networks to analyse cancer genome data
Enrico Glaab1, Anaïs Baudot, Natalio Krasnogor
1Nottingham University, UK.
Background:
Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.
Results:
We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes.
Conclusions:
The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.
Insights
This study introduces a method to expand cellular pathways using protein interaction networks, aiding in the understanding of cancer-associated gene mutations and their functional roles.
Area of Science:
- Systems Biology
- Genomics
- Cancer Research
Background:
- Cellular signaling pathways are crucial in cancer development but are increasingly understood as interconnected networks, not isolated cascades.
- Recent functional genomics data reveal numerous protein interactions, challenging traditional pathway models.
- Integrating pathway databases with interaction networks offers a robust approach to studying complex biological processes in cancer.
Purpose of the Study:
- To develop a methodology for extending predefined cellular pathways and processes.
- To identify novel pathway components and regulators using protein-protein interaction networks.
- To analyze the enrichment of extended pathways in pancreatic mutated genes.
Main Methods:
- Mapping predefined protein sets onto a protein-protein interaction network.
- Extending pathways by incorporating densely interconnected interaction partners.
- Analyzing network topology and molecular functions of added proteins.
- Assessing pathway enrichment in pancreatic mutated genes.
Main Results:
- Extended pathways reveal proteins with distinct network features and functions, suggesting new roles in cellular communication.
- Significant associations were found between mutated genes and specific cellular processes.
- The method facilitated the analysis of previously unannotated cancer-associated genes.
Conclusions:
- The developed method effectively extends cellular pathways by integrating canonical knowledge with large-scale interaction data.
- This approach enhances the understanding of cancer-associated gene functions.
- Synergistic use of pathway knowledge and interaction data provides deeper insights into cancer biology.
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