Abstract
The role played by the topological structure of biological networks in their dynamics and function is receiving increasing attention over the last decade as large-throughput experiments have provided large volumes of highly resolved data on the interactions between the components of such networks. This has provided new perspectives on systems diseases: for example, there has been a gradual shift in cancer research away from the study of individual molecules and of single gene mutations to the emerging consensus that it is a complex disease involving large-scale disruptions in the intracellular signaling network. One of the drawbacks of a systems- or network-based approach is the large number of cellular agents whose interactions need to be investigated. We tried to solve this problem by taking a mesoscopic view of the cancer diseases–genes network, whose modular organization we studied after projecting it onto two networks, one comprising only disease types and the other consisting of only genes related to one or more categories of cancer. Using community partitioning, we identified several modules in these networks. Projecting cancer gene clusters onto an abstract ‘modular space’ allows us to infer the relations between different tumor types. By classifying the functional role of particular genes in terms of their inter- and intra-modular connectivity, we identified a number of genes that play the key role of ‘connector hubs’ in the network. Using data from the human protein–protein interaction network we showed that genes that are ‘connector hubs’ or ‘global hubs’ are, in fact, much more likely to be related to cancer than other genes. More important from a therapeutic point of view, we showed that the connector hubs in the cancer gene network are involved in a significantly larger number of human signaling pathways associated with cancer than other types of cancer genes. Furthermore, the types of cancer linked to connector hub genes have significantly reduced survival rates compared with other types of cancer, thereby enhancing their importance in the search for potential therapeutic targets.
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Acknowledgements
The work was partly supported by the IMSc Complex Systems Project (XII Plan) funded by the Department of Atomic Energy, Government of India. We would like to thank Soumya Easwaran and Anand Pathak for their assistance in preparing figures, and Indrani Bose, Shaon Chakrabarti, Arjun Krishnan, Ramakrishna Ramaswamy, M S Santhanam and Somdatta Sinha for helpful discussions.
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Communicated by Mohit Kumar Jolly.
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This article is part of the Topical Collection: Emergent dynamics of biological networks.
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Supplementary Figure 1
Flowchart indicating schematically the methods employed for the analysis reported in the paper (TIF 3275 kb)
Supplementary Figure 2
Visual representation of the comparison between the modular decomposition of the TT-GWN obtained using Infomap method (top, 25 modules) with that obtained using spectral partitioning (bottom, 13 modules). The modules are represented as horizontal line segments, connected by bands which are colored according to the module (obtained using the Infomap method) from which they originate (TIF 641 kb)
Supplementary Figure 3
The composition of the modules of TT-GWN in terms of different cellular components (TIF 1650 kb)
Supplementary Figure 4
: The composition of the modules of TT-GWN in terms of different biological processes (TIF 1494)
Supplementary Figure 5
Overlap in terms of gene memberships of different cancer categories (shown along the abscissae) and (shown along the ordinate) different (a) human signaling pathways that are related to cancer [obtained from the National Cancer Institute (NCI) database], (b) cellular components and (c) biological processes, obtained from Gene Ontology (GO). Overlap (i,j) is measured by the fraction of genes in a particular cancer category j for which the corresponding proteins that they express occur in the (a) signaling pathway i, or (b) cellular component i, or (c) biological process i. The extent of overlap is represented using the colorbar shown at right (TIF 317 kb)
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Jesan, T., Sinha, S. Modular organization of gene–tumor association network allows identification of key molecular players in cancer. J Biosci 47, 60 (2022). https://doi.org/10.1007/s12038-022-00292-5
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DOI: https://doi.org/10.1007/s12038-022-00292-5