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BMC Syst Biol. 2016 Aug 26;10 Suppl 3:65. doi: 10.1186/s12918-016-0309-9.

Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

Cheng F1, Liu C2, Shen B3, Zhao Z4,5,6,7.

Author information

1
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
2
Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China.
3
Center for Systems Biology, Soochow University, Suzhou, China.
4
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA. zhongming.zhao@uth.tmc.edu.
5
Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN, USA. zhongming.zhao@uth.tmc.edu.
6
Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA. zhongming.zhao@uth.tmc.edu.
7
Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. zhongming.zhao@uth.tmc.edu.

Abstract

BACKGROUND:

Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy.

RESULTS:

In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and further contribute to tyrosine kinase inhibitor (e.g., gefitinib) resistance.

CONCLUSION:

In summary, we demonstrated that network properties such as network entropy and unbalanced motifs associated with tumor initiation, progression, and anticancer drug responses, suggesting new potential network-based prognostic and predictive measure in cancer.

KEYWORDS:

Cancer; Heterogeneity; Network entropy; Network modularity; Unbalanced motifs

PMID:
27585651
PMCID:
PMC5009528
DOI:
10.1186/s12918-016-0309-9
[Indexed for MEDLINE]
Free PMC Article

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