Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Large-scale association analysis identifies new risk loci for coronary artery disease

Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r(2)

Functional and genetic analysis of the colon cancer network

Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into account. In this study, we infer a colon cancer network from a large-scale gene expression data set by using the method BC3Net. We provide a structural and a functional analysis of this network and also connect its molecular interaction structure with the chromosomal locations of the genes enabling the definition of cis- and trans-interactions. Furthermore, we investigate the interaction of genes that can be found in close neighborhoods on the chromosomes to gain insight into regulatory mechanisms. To our knowledge this is the first study analyzing the genome-scale colon cancer network.

The ETS family member GABPα modulates androgen receptor signalling and mediates an aggressive phenotype in prostate cancer

In prostate cancer (PC), the androgen receptor (AR) is a key transcription factor at all disease stages, including the advanced stage of castrate-resistant prostate cancer (CRPC). In the present study, we show that GABPα, an ETS factor that is up-regulated in PC, is an AR-interacting transcription factor. Expression of GABPα enables PC cell lines to acquire some of the molecular and cellular characteristics of CRPC tissues as well as more aggressive growth phenotypes. GABPα has a transcriptional role that dissects the overlapping cistromes of the two most common ETS gene fusions in PC: overlapping significantly with ETV1 but not with ERG target genes. GABPα bound predominantly to gene promoters, regulated the expression of one-third of AR target genes and modulated sensitivity to AR antagonists in hormone responsive and castrate resistant PC models. This study supports a critical role for GABPα in CRPC and reveals potential targets for therapeutic intervention.

Gaining biological perspectives from schistosome genomes

Characterization of the genomic basis underlying schistosome biology is an important strategy for the development of future treatments and interventions. Genomic sequence is now available for the three major clinically relevant schistosome species, Schistosoma mansoni, S. japonicum and S. haematobium, and this information represents an invaluable resource for the future control of human schistosomiasis. The identification of a biologically important, but distinct from the host, schistosome gene product is the ultimate goal for many research groups. While the initial elucidation of the genome of an organism is critical for most biological research, continued improvement or curation of the genome construction should be an ongoing priority. In this review we will discuss prominent recent findings utilizing a systems approach to schistosome biology, as well as the increased use of interference RNA (RNAi). Both of these research strategies are aiming to place parasite genes into a more meaningful biological perspective.

The Yin and Yang of vitamin D receptor (VDR) signaling in neoplastic progression: Operational networks and tissue-specific growth control

Substantive evidence implicates vitamin D receptor (VDR) or its natural ligand 1a,25-(OH)2 D3 in modulation of tumor growth. However, both human and animal studies indicate tissue-specificity of effect. Epidemiological studies show both inverse and direct relationships between serum 25(OH)D levels and common solid cancers. VDR ablation affects carcinogen-induced tumorigenesis in a tissue-specific manner in model systems. Better understanding of the tissue-specificity of vitamin D-dependent molecular networks may provide insight into selective growth control by the seco-steroid, 1a,25-(OH)2 D3. This commentary considers complex factors that may influence the cell- or tissue-specificity of 1a,25-(OH)2 D3/VDR growth effects, including local synthesis, metabolism and transport of vitamin D and its metabolites, vitamin D receptor (VDR) expression and ligand-interactions, 1a,25-(OH)2 D3 genomic and non-genomic actions, Ca2+ flux, kinase activation, VDR interactions with activating and inhibitory vitamin D responsive elements (VDREs) within target gene promoters, VDR coregulator recruitment and differential effects on key downstream growth regulatory genes. We highlight some differences of VDR growth control relevant to colonic, esophageal, prostate, pancreatic and other cancers and assess the potential for development of selective prevention or treatment strategies.

Information processing in the transcriptional regulatory network of yeast: Functional robustness

Background: Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism.

Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

Background: In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored.

Results: We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes.

Conclusions: Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes.