MAGNET: MicroArray Gene expression and Network Evaluation Toolkit

Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer

We have identified nine highly connected and differentially expressed gene subnetworks between aggressive primary tumors and metastatic lesions in endometrial carcinomas. We implemented a novel pipeline combining gene set and network approaches, which here allows integration of protein-protein interactions and gene expression data. The resulting subnetworks are significantly associated with disease progression across tumor stages from complex atypical hyperplasia, primary tumors to metastatic lesions. The nine subnetworks include genes related to metastasizing features such as epithelial-mesenchymal transition (EMT), hypoxia and cell proliferation. TCF4 and TWIST2 were found as central genes in the subnetwork related to EMT. Two of the identified subnetworks display statistically significant association to patient survival, which were further supported by an independent validation in the data from The Cancer Genome Atlas data collection. The first subnetwork contains genes related to cell proliferation and cell cycle, while the second contains genes involved in hypoxia such as HIF1A and EGLN3. Our findings provide a promising context to elucidate the biological mechanisms of metastasis, suggest potential prognostic markers and further identify therapeutic targets. The pipeline R source code is freely available, including permutation tests to assess statistical significance of the identified subnetworks.

Identifying Gene Interaction Networks

In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. This chapter features two case studies, both of which utilize Cytoscape, an open source and user-friendly network visualization and analysis tool. In the first example, we demonstrate the basic functionalities of Cytoscape by building an interaction network from a publicly available database, analyzing its topological features, and performing gene ontology enrichment. For the second section, we constructed a network from scratch starting with an experimental gene expression dataset. From there, we implement more advanced visual annotations of the network and perform subnetwork enrichment. The methods described are applicable to larger networks that can be collected from various resources.

Targeting Androgen Receptor (AR)→IL12A Signal Enhances Efficacy of Sorafenib plus NK Cells Immunotherapy to Better Suppress HCC Progression

Gender disparity has long been considered as a key to fully understand hepatocellular carcinoma (HCC) development. At the same time, immunotherapy related to IL12 still need more investigation before being applied in clinical settings. The aim of this study is to investigate the influence of the androgen receptor (AR) on natural killer (NK) cell-related innate immune surveillance in liver cancer, and provide a novel therapeutic approach to suppress HCC via altering IL12A. By using in vitro cell cytotoxicity test and in vivo liver orthotopic xenograft mouse model, we identified the role of AR in modulating NK cell cytotoxicity. Luciferase report assay and chromatin immunoprecipitation assay were applied for mechanism dissection. IHC was performed for sample staining. Our results showed AR could suppress IL12A expression at the transcriptional level via direct binding to the IL12A promoter region that resulted in repressing efficacy of NK cell cytotoxicity against HCC, and sorafenib treatment could enhance IL12A signals via suppressing AR signals. These results not only help to explain the AR roles in the gender disparity of HCC but also provide a potential new therapy to better suppress HCC via combining sorafenib with NK cell-related immunotherapy. Mol Cancer Ther; 15(4); 731-42. ©2016 AACR.

MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring

Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association.

Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.

A network approach to clinical intervention in neurodegenerative diseases

Network biology has become a powerful tool to dissect the molecular mechanisms triggering neurodegeneration. Recent developments in network biology have led to the discovery of disease-causing genes, diagnostic biomarkers, and therapeutic targets for several neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's diseases. Network-based approaches have provided the molecular rationale for the relationship among cancer, diabetes, and neurodegenerative diseases, and have uncovered unexpected links between apparently unrelated diseases. Here, we summarize the recent advances in network biology to untangle the molecular underpinnings giving rise to the most prevalent neurodegenerative diseases. We propose that network analysis provides a feasible and practical tool for identifying biologically meaningful biomarkers and potential therapeutic targets for clinical intervention in neurodegenerative diseases.

PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data

Interactions among transcriptional factors (TFs), cofactors and other proteins or enzymes can affect transcriptional regulatory capabilities of eukaryotic organisms. Post-translational modifications (PTMs) cooperate with TFs and epigenetic alterations to constitute a hierarchical complexity in transcriptional gene regulation. While clearly implicated in biological processes, our understanding of these complex regulatory mechanisms is still limited and incomplete. Various online software have been proposed for uncovering transcriptional and epigenetic regulatory networks, however, there is a lack of effective web-based software capable of constructing underlying interactive organizations between post-translational and transcriptional regulatory components. Here, we present an open web server, post-translational hierarchical gene regulatory network (PTHGRN) to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN utilizes a graphical Gaussian model with partial least squares regression-based methodology, and is able to integrate protein–protein interactions, ChIP-seq and gene expression data and to capture essential regulation features behind high-throughput data. The server provides an integrative platform for users to analyze ready-to-use public high-throughput Omics resources or upload their own data for systems biology study. Users can choose various parameters in the method, build network topologies of interests and dissect their associations with biological functions. Application of the software to stem cell and breast cancer demonstrates that it is an effective tool for understanding regulatory mechanisms in biological complex systems. PTHGRN web server is publically available at web site http://www.byanbioinfo.org/pthgrn.

INsPeCT: INtegrative Platform for Cancer Transcriptomics

The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChIP-seq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at http://inspect.braembl.org.au.

ToP: a trend-of-disease-progression procedure works well for identifying cancer genes from multi-state cohort gene expression data for human colorectal cancer

Significantly expressed genes extracted from microarray gene expression data have proved very useful for identifying genetic biomarkers of diseases, including cancer. However, deriving a disease related inference from a list of differentially expressed genes has proven less than straightforward. In a systems disease such as cancer, how genes interact with each other should matter just as much as the level of gene expression. Here, in a novel approach, we used the network and disease progression properties of individual genes in state-specific gene-gene interaction networks (GGINs) to select cancer genes for human colorectal cancer (CRC) and obtain a much higher hit rate of known cancer genes when compared with methods not based on network theory. We constructed GGINs by integrating gene expression microarray data from multiple states--healthy control (Nor), adenoma (Ade), inflammatory bowel disease (IBD) and CRC--with protein-protein interaction database and Gene Ontology. We tracked changes in the network degrees and clustering coefficients of individual genes in the GGINs as the disease state changed from one to another. From these we inferred the state sequences Nor-Ade-CRC and Nor-IBD-CRC both exhibited a trend of (disease) progression (ToP) toward CRC, and devised a ToP procedure for selecting cancer genes for CRC. Of the 141 candidates selected using ToP, ∼50% had literature support as cancer genes, compared to hit rates of 20% to 30% for standard methods using only gene expression data. Among the 16 candidate cancer genes that encoded transcription factors, 13 were known to be tumorigenic and three were novel: CDK1, SNRPF, and ILF2. We identified 13 of the 141 predicted cancer genes as candidate markers for early detection of CRC, 11 and 2 at the Ade and IBD states, respectively.

Identifying gene interaction networks

In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. As a case study, we used Cytoscape, an open source and easy-to-use network visualization and analysis tool to first gather and visualize a small network. We have analyzed this network's topological features and have looked at functional enrichment of the network nodes by integrating the gene ontology database. The methods described are applicable to larger networks that can be collected from various resources.