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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Discovery and analysis of consistent active sub-networks in cancers
Raj K Gaire1, Lorey Smith, Patrick Humbert
1NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia. rgaire@csse.unimelb.edu.au
BMC Bioinformatics
|February 2, 2013
Summary
CASNet identifies active subnetworks (ASNs) in disease gene expression using integrated networks and combined node/edge scores. This approach reveals conserved regulatory patterns across cancer subtypes, suggesting shared therapeutic strategies.
Area of Science:
- Computational Biology
- Bioinformatics
- Systems Biology
Background:
- Gene expression profiles reveal differences between diseased and healthy cells.
- Biological networks, particularly protein-protein interaction (PPI) networks, are used to identify active subnetworks (ASNs) for disease understanding.
- Current ASN discovery methods often rely on undirected PPI networks and node-centric approaches, limiting their effectiveness with integrated, comprehensive networks.
Purpose of the Study:
- To develop a novel method, CASNet, for identifying more meaningful and stable ASNs.
- To leverage integrated interaction networks (mixed graphs) and incorporate gene regulation directions.
- To utilize combined node and edge scoring for improved ASN detection.
Main Methods:
- CASNet employs integrated interaction networks, considering the directionality of gene regulations.
- It uses a combination of node and edge scores to evaluate genes and their interactions.
- The method formulates objective functions using mixed integer programming (MIP) to find optimal solutions.
- CASNet simplifies and extends previous methodologies, incorporating edge evaluations and reducing sensitivity to significance thresholds.
Main Results:
- CASNet discovers more meaningful and stable regulatory ASNs compared to existing approaches.
- Analysis of a breast cancer dataset identified conserved positive feedback loops in basal/triple-negative subtypes involving genes like AR, ESR1, MYC, E2F2, PGR, BCL2, and CCND1.
- A conserved ASN near IL6 was found between breast cancer basal and glioblastoma mesenchymal subtypes, indicating molecular similarities across different cancer types.
Conclusions:
- CASNet provides a more effective approach for identifying regulatory ASNs by utilizing integrated networks and combined scoring.
- Conserved molecular patterns across cancer subtypes suggest potential for shared therapeutic strategies.
- The findings highlight the potential for cross-cancer therapeutic development based on identified molecular similarities.
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