Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

Structural attributes of nucleotide sequences in promoter regions of supercoiling-sensitive genes: how to relate microarray expression data with genomic sequences

The level of supercoiling in the chromosome can affect gene expression. To clarify the basis of supercoiling sensitivity, we analyzed the structural features of nucleotide sequences in the vicinity of promoters for the genes with expression enhanced and decreased in response to loss of chromosomal supercoiling in E. coli. Fourier analysis of promoter sequences for supercoiling-sensitive genes reveals the tendency in selection of sequences with helical periodicities close to 10 nt for relaxation-induced genes and to 11 nt for relaxation-repressed genes. The helical periodicities in the subsets of promoters recognized by RNA polymerase with different sigma factors were also studied. A special procedure was developed for study of correlations between the intensities of periodicities in promoter sequences and the expression levels of corresponding genes. Significant correlations of expression with the AT content and with AT periodicities about 10, 11, and 50 nt indicate their role in regulation of supercoiling-sensitive genes.Comment: 38 pages, 12 figure