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Subnetwork state functions define dysregulated subnetworks in cancer.

ABSTRACT: Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples. When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize fast heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation. Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype. Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks. We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than existing algorithms. Comprehensive cross-classification experiments show that subnetworks identified by Crane outperform those identified by additive algorithms in predicting metastasis of colorectal cancer (CRC).

SUBMITTER: Chowdhury SA

PROVIDER: S-EPMC3123978 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Subnetwork state functions define dysregulated subnetworks in cancer.

Chowdhury Salim A SA Nibbe Rod K RK Chance Mark R MR Koyutürk Mehmet M

Journal of computational biology : a journal of computational molecular cell biology 20110301 3

Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples. When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes th ...[more]

PMID: 21385033

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Project description:Breast invasive carcinoma (BRCA) is the most malignant and leading cause of death in women. Global efforts are ongoing for improvement in early detection, prevention, and treatment. In this milieu, a comprehensive analysis of RNA-sequencing data of 1097 BRCA samples and 114 normal adjacent tissues is done to identify dysregulated genes in major molecular classes of BRCA in various clinical stages. Significantly enriched pathways in distinct molecular classes of BRCA have been identified. Pathways such as interferon signaling, tryptophan degradation, granulocyte adhesion & diapedesis, and catecholamine biosynthesis were found to be significantly enriched in Estrogen/Progesterone Receptor positive/Human Epidermal Growth Factor Receptor 2 negative, pathways such as RAR activation, adipogenesis, the role of JAK1/2 in interferon signaling, TGF-β and STAT3 signaling intricated in Estrogen/Progesterone Receptor negative/Human Epidermal Growth Factor Receptor 2 positive and pathways as IL-1/IL-8, TNFR1/TNFR2, TWEAK, and relaxin signaling were found in triple-negative breast cancer. The dysregulated genes were clustered based on their mutation frequency which revealed nine mutated clusters, some of which were well characterized in cancer while others were less characterized. Each cluster was analyzed in detail which led to the identification of NLGN3, MAML2, TTN, SYNE1, ANK2 as candidate genes in BRCA. They are central hubs in the protein-protein-interaction network, indicating their important regulatory roles. Experimentally, the Real-Time Quantitative Reverse Transcription PCR and western blot confirmed our computational predictions in cell lines. Further, immunohistochemistry corroborated the results in ~ 100 tissue samples. We could experimentally show that the NLGN3 & ANK2 have tumor-suppressor roles in BRCA as shown by cell viability assay, transwell migration, colony forming and wound healing assay. The cell viability and migration was found to be significantly reduced in MCF7 and MDA-MB-231 cell lines in which the selected genes were over-expressed as compared to control cell lines. The wound healing assay also demonstrated a significant decrease in wound closure at 12 h and 24 h time intervals in MCF7 & MDA-MB-231 cells. These findings established the tumor suppressor roles of NLGN3 & ANK2 in BRCA. This will have important ramifications for the therapeutics discovery against BRCA.

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Subnetwork state functions define dysregulated subnetworks in cancer.

Publications

Subnetwork state functions define dysregulated subnetworks in cancer.

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