Dataset Information

Random Matrix Analysis for Gene Interaction Networks in Cancer Cells.

ABSTRACT: Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Although cancer is considered to originate from the topological alteration of a huge molecular interaction network in cellular systems, the theoretical study to investigate such complex networks is still insufficient. It is necessary to predict the behavior of a huge complex interaction network from the behavior of a finite size network. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices of gene networks in human cancer cells. The interaction matrices are computed using the Cancer Network Galaxy (TCNG) database which is a repository of gene interactions inferred by a Bayesian network model. 256 NCBI GEO entries regarding gene expressions in human cancer cells have been used for the inference. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have more than ~38,000 edges. In the opposite case, when the networks have smaller numbers of edges, the distribution P(s) becomes the Poisson distribution. We investigate relevance of P(s) both to the sparseness of the networks and to edge frequency factor which is the reliance (likelihood) of the inferred gene interactions.

SUBMITTER: Kikkawa A

PROVIDER: S-EPMC6045654 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Random Matrix Analysis for Gene Interaction Networks in Cancer Cells.

Kikkawa Ayumi A

Scientific reports 20180713 1

Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Although cancer is considered to originate from the topological alteration of a huge molecular interaction network in cellular systems, the theoretical study to investigate such complex networks is still insufficient. It is necessary to predict the behavior of a huge complex interaction network from the behavior of a finite size network. Based on the random matrix th ...[more]

PMID: 30006574

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Project description:BackgroundLarge-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory.ResultsApplication of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene co-expression network. Moreover, the cellular roles of 215 functionally unknown genes from yeast, E. coli and S. oneidensis are predicted by the gene co-expression networks using guilt-by-association principle, many of which are supported by existing information or our experimental verification, further demonstrating the reliability of this approach for gene function prediction.ConclusionOur rigorous analysis of gene expression microarray profiles using RMT has showed that the transition of NNSD of correlation matrix of microarray profile provides a profound theoretical criterion to determine the correlation threshold for identifying gene co-expression networks.

Project description:The extracellular matrix has a major effect upon the malignant properties of bladder cancer cells both in vitro in 3-dimensional culture and in vivo. Comparing gene expression of several bladder cancer cells lines grown under permissive and suppressive conditions in 3-dimensional growth on cancer-derived and normal-derived basement membrane gels respectively and on plastic in conventional tissue culture provides a model system for investigating the interaction of malignancy and extracellular matrix. Understanding how the extracellular matrix affects the phenotype of bladder cancer cells may provide important clues to identify new markers or targets for therapy.Five bladder cancer cell lines and one immortalized, but non-tumorigenic, urothelial line were grown on Matrigel, a cancer-derived ECM, on SISgel, a normal-derived ECM, and on plastic, where the only ECM is derived from the cells themselves. The transcriptomes were analyzed on an array of 1186 well-annotated cancer derived cDNAs containing most of the major pathways for malignancy. Hypervariable genes expressing more variability across cell lines than a set expressing technical variability were analyzed further. Expression values were clustered, and to identify genes most likely to represent biological factors, statistically over-represented ontologies and transcriptional regulatory elements were identified.Approximately 400 of the 1186 total genes were expressed 2 SD above background. Approximately 100 genes were hypervariable in cells grown on each ECM, but the pattern was different in each case. A core of 20 were identified as hypervariable under all 3 growth conditions, and 33 were hypervariable on both SISgel and Matrigel, but not on plastic. Clustering of the hypervariable genes showed very different patterns for the same 6 cell types on the different ECM. Even when loss of cell cycle regulation was identified, different genes were involved, depending on the ECM. Under the most permissive conditions of growth where the malignant phenotype was fully expressed, activation of AKT was noted. TGFbeta1 signaling played a major role in the response of bladder cancer cells to ECM. Identification of TREs on genes that clustered together suggested some clustering was driven by specific transcription factors.The extracellular matrix on which cancer cells are grown has a major effect on gene expression. A core of 20 malignancy-related genes were not affected by matrix, and 33 were differentially expressed on 3-dimensional culture as opposed to plastic. Other than these genes, the patterns of expression were very different in cells grown on SISgel than on Matrigel or even plastic, supporting the hypothesis that growth of bladder cancer cells on normal matrix suppresses some malignant functions. Unique underlying regulatory networks were driving gene expression and could be identified by the approach outlined here.

Dataset Information

Random Matrix Analysis for Gene Interaction Networks in Cancer Cells.

Publications

Random Matrix Analysis for Gene Interaction Networks in Cancer Cells.

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