Using random walks to identify cancer-associated modules in expression data

SEMtree: tree-based structure learning methods with structural equation models

MOTIVATION

With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework.

RESULTS

Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance.

AVAILABILITY AND IMPLEMENTATION

SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Role of Bile-Derived Extracellular Vesicles in Hepatocellular Proliferation after Partial Hepatectomy in Rats

Although liver regeneration has been extensively studied, the effects of bile-derived extracellular vesicles (bile EVs) on hepatocytes has not been elucidated. We examined the influence of bile EVs, collected from a rat model of 70% partial hepatectomy (PH), on hepatocytes. We produced bile-duct-cannulated rats. Bile was collected over time through an extracorporeal bile duct cannulation tube. Bile EVs were extracted via size exclusion chromatography. The number of EVs released into the bile per liver weight 12 h after PH significantly increased. Bile EVs collected 12 and 24 h post-PH, and after sham surgery (PH12-EVs, PH24-EVs, sham-EVs) were added to the rat hepatocyte cell line, and 24 h later, RNA was extracted and transcriptome analysis performed. The analysis revealed that more upregulated/downregulated genes were observed in the group with PH24-EVs. Moreover, the gene ontology (GO) analysis focusing on the cell cycle revealed an upregulation of 28 types of genes in the PH-24 group, including genes that promote cell cycle progression, compared to the sham group. PH24-EVs induced hepatocyte proliferation in a dose-dependent manner in vitro, whereas sham-Evs showed no significant difference compared to the controls. This study revealed that post-PH bile Evs promote the proliferation of the hepatocytes, and genes promoting cell cycles are upregulated in hepatocytes.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain (i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; (ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; (iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.

Temporal and sequential order of nonoverlapping gene networks unraveled in mated female Drosophila

Mating triggers successive waves of temporal transcriptomic changes within independent gene networks in female Drosophila, suggesting a recruitment of interconnected modules that vanish in late life.

In this study, we reanalyzed available datasets of gene expression changes in female Drosophila head induced by mating. Mated females present metabolic phenotypic changes and display behavioral characteristics that are not observed in virgin females, such as repulsion to male sexual aggressiveness, fidelity to food spots selected for oviposition, and restriction to the colonization of new niches. We characterize gene networks that play a role in female brain plasticity after mating using AMINE, a novel algorithm to find dysregulated modules of interacting genes. The uncovered networks of altered genes revealed a strong specificity for each successive period of life span after mating in the female head, with little conservation between them. This finding highlights a temporal order of recruitment of waves of interconnected genes which are apparently transiently modified: the first wave disappears before the emergence of the second wave in a reversible manner and ends with few consolidated gene expression changes at day 20. This analysis might document an extended field of a programmatic control of female phenotypic traits by male seminal fluid.

DUSP9, a Dual-Specificity Phosphatase with a Key Role in Cell Biology and Human Diseases

Mitogen-activated protein kinases (MAPKs) are essential for proper cell functioning as they regulate many molecular effectors. Careful regulation of MAPKs is therefore required to avoid MAPK pathway dysfunctions and pathologies. The mammalian genome encodes about 200 phosphatases, many of which dephosphorylate the MAPKs and bring them back to an inactive state. In this review, we focus on the normal and pathological functions of dual-specificity phosphatase 9 (DUSP9)/MAP kinase phosphatases-4 (MKP-4). This cytoplasmic phosphatase, which belongs to the threonine/tyrosine dual-specific phosphatase family and was first described in 1997, is known to dephosphorylate ERK1/2, p38, JNK and ASK1, and thereby to control various MAPK pathway cascades. As a consequence, DUSP9 plays a major role in human pathologies and more specifically in cardiac dysfunction, liver metabolic syndromes, diabetes, obesity and cancer including drug response and cell stemness. Here, we recapitulate the mechanism of action of DUSP9 in the cell, its levels of regulation and its roles in the most frequent human diseases, and discuss its potential as a therapeutic target.

High-dimensional covariance matrices tests for analyzing multi-tumor gene expression data

By collecting multiple sets per subject in microarray data, gene sets analysis requires characterize intra-subject variation using gene expression profiling. For each subject, the data can be written as a matrix with the different subsets of gene expressions (e.g. multiple tumor types) indexing the rows and the genes indexing the columns. To test the assumption of intra-subject (tumor) variation, we present and perform tests of multi-set sphericity and multi-set identity of covariance structures across subjects (tumor types). We demonstrate by both theoretical and empirical studies that the tests have good properties. We applied the proposed tests on The Cancer Genome Atlas (TCGA) and tested covariance structures for the gene expressions across several tumor types.

Dual-Specificity Phosphatase 9 Regulates Cellular Proliferation and Predicts Recurrence After Surgery in Hepatocellular Carcinoma

Hepatocellular carcinoma (CC) is a common and deadly cancer with complex molecular pathogenesis. Little is known about dual-specificity phosphatases (DUSPs) in HCC. We investigated DUSP9 expression in human HCC, associations between DUSP9 and patient outcomes, and effects of altered DUSP9 expression on HCC biology. We studied public data sets as well as 196 patients at our institution who had HCC resections. Quantitative real-time reverse transcription polymerase chain reaction and western blot demonstrated that DUSP9 expression was increased >10-fold in HCC compared to adjacent liver and healthy controls (P = 0.005). Kaplan-Meier and multivariable regression analyses revealed that higher DUSP9 expression was associated with shorter disease-free survival (high DUSP9, 1.6; 95% confidence interval, 0.9-2.3 vs. low DUSP9, 3.4; 95% confidence interval, 1.8-5.0 years; P = 0.04) and increased risk of recurrence (hazard ratio 1.55; 95% confidence interval, 1.01-2.67; P = 0.05) after resection. DUSP9 complementary DNA (cDNA) was cloned using rapid amplification of cDNA ends, revealing two DUSP9 isoforms in human HCC cells. Studies of transcriptional regulation using promoter-luciferase reporter constructs suggested that DUSP9 transcription is regulated by E26 transformation-specific transcription factors. Proliferation of hepatic cells in vitro was enhanced by lentiviral-mediated overexpression of DUSP9. In contrast, DUSP9 knockout HCC cells generated using clustered regularly interspaced short palindromic repeats (CRISPR) demonstrated decreased HCC proliferation and doxorubicin resistance in vitro and impaired xenograft growth in vivo. RNA sequencing, gene set enrichment, and network/pathway analysis revealed that DUSP9 knockout is associated with activation of protein kinase activity and apoptosis. Conclusion: DUSP9 regulates cell proliferation and predicts recurrence after surgery in HCC. DUSP9 may represent a novel prognostic candidate and therapeutic target. Additional studies are warranted to further explore the role and regulation of DUSP9 in HCC.

Modular Reorganization of Signaling Networks during the Development of Colon Adenoma and Carcinoma

Network science is an emerging tool in systems biology and oncology, providing novel, system-level insight into the development of cancer. The aim of this project was to study the signaling networks in the process of oncogenesis to explore the adaptive mechanisms taking part in the cancerous transformation of healthy cells. For this purpose, colon cancer proved to be an excellent candidate as the preliminary phase, and adenoma has a long evolution time. In our work, transcriptomic data have been collected from normal colon, colon adenoma, and colon cancer samples to calculating link (i.e., network edge) weights as approximative proxies for protein abundances, and link weights were included in the Human Cancer Signaling Network. Here we show that the adenoma phase clearly differs from the normal and cancer states in terms of a more scattered link weight distribution and enlarged network diameter. Modular analysis shows the rearrangement of the apoptosis- and the cell-cycle-related modules, whose pathway enrichment analysis supports the relevance of targeted therapy. Our work enriches the system-wide assessment of cancer development, showing specific changes for the adenoma state.

DUSP9-mediated reduction of pERK1/2 supports cancer stem cell-like traits and promotes triple negative breast cancer

Breast cancer remains a complex disease resulting in high mortality in women. A subset of cancer stem cell (CSC)-like cells expressing aldehyde dehydrogenase 1 (ALDH1) and SOX2/OCT4 are implicated in aggressive biology of specific subtypes of breast cancer. Targeting these populations in breast tumors remain challenging. We examined xenografts from three poorly studied triple negative (TN) breast cancer cells (MDA-MB-468, HCC70 and HCC1806) as well as HMLE^HRASV12 for stem cell (SC)-specific proteins, proliferation pathways and dual-specific phosphatases (DUSPs) by quantitative real-time PCR (qRT-PCR), immunoblot analysis and immunohistochemistry. We found that pERK1/2 remained suppressed in TN xenografts examined at various stages of growth, while the levels of pp38 MAPK and pAKT was upregulated. We found that DUSP was involved in the suppression of pERK1/2, which was MEK1/2 independent. Our in vitro assays, using HMLE^HRASV12 xenografts as a positive control, confirmed increased phosphatase activity that specifically influenced pERK1/2 but not pp38MAPK or pJNK levels. Family members of DUSPs examined, showed increase in DUSP9 expression in TN xenografts. Increased DUSP9 expression in xenografts was consistently associated with upregulation of SC-specific proteins, ALDH1 and SOX2/OCT4. HRAS driven HMLE^HRASV12 xenografts as well as mammospheres from TN breast cancer cells showed inverse relationship between pERK1/2 and increased expression of DUSP9 and CSC traits. In addition, treatment in vitro, with MEK1/2 inhibitor, PD 98059, reduced pERK1/2 levels and increased DUSP9 and SC-specific proteins. Depletion of subsets of SOX2/OCT4 by fluorescence-activated cell sorting (FACS), as well as pharmacological and genetic reduction of DUSP9 levels influenced ALDH1 and SOX2/OCT4 expression and reduced mammosphere growth in vitro as well as tumor growth in vivo. Collectively our data support the possibility that DUSP9 contributed to stem cell-like cells that could influence TN breast tumor growth. Conclusion: Our study shows that subsets of TN breast cancers with MEK1/2 independent reduced pERK1/2 levels will respond less to MEK1/2 inhibitors, thereby questioning their therapeutic efficacy. Our study also demonstrates context-dependent DUSP9-mediated reduced pERK1/2 levels could influence stem cell-like traits in TN breast tumors. Therefore, targeting DUSP9 could be an attractive target for improved clinical outcome in a subset of basal-like breast cancers.

DCARS: differential correlation across ranked samples

MOTIVATION

Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest.

RESULTS

When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across several cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing several distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multi-layered and complex data.

AVAILABILITY AND IMPLEMENTATION

DCARS R package and sample data are available at https://github.com/shazanfar/DCARS. Publicly available data from The Cancer Genome Atlas (TCGA) was used using the TCGABiolinks R package. Supplementary Files and DCARS R package is available at https://github.com/shazanfar/DCARS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

Identification of Transcriptional Signatures of Colon Tumor Stroma by a Meta-Analysis

Background

The tumor stroma plays pivotal roles in influencing tumor growth, invasion, and metastasis. Transcriptional signatures of colon tumor stroma (CTS) are significantly associated with prognosis of colon cancer. Thus, identification of the CTS transcriptional features could be useful for colon cancer diagnosis and therapy.

Methods

By a meta-analysis of three CTS gene expression profiles datasets, we identified differentially expressed genes (DEGs) between CTS and colon normal stroma. Furthermore, we identified the pathways, upstream regulators, and protein-protein interaction (PPI) network that were significantly associated with the DEGs. Moreover, we analyzed the enrichment levels of immune signatures in CTS. Finally, we identified CTS-associated gene signatures whose expression was significantly associated with prognosis in colon cancer.

Results

We identified numerous significantly upregulated genes (such as CTHRC1, NFE2L3, SULF1, SOX9, ENC1, and CCND1) and significantly downregulated genes (such as MYOT, ASPA, KIAA2022, ARHGEF37, BCL-2, and PPARGC1A) in CTS versus colon normal stroma. Furthermore, we identified significantly upregulated pathways in CTS that were mainly involved in cellular development, immune regulation, and metabolism, as well as significantly downregulated pathways in CTS that were mostly metabolism-related. Moreover, we identified upstream TFs (such as SUZ12, NFE2L2, RUNX1, STAT3, and SOX2), kinases (such as MAPK14, CSNK2A1, CDK1, CDK2, and CDK4), and master metabolic transcriptional regulators (MMTRs) (such as HNF1A, NFKB1, ZBTB7A, GATA2, and GATA5) regulating the DEGs. We found that CD8+ T cells were more enriched in CTS than in colon normal stroma. Interestingly, we found that many of the DEGs and their regulators were prognostic markers for colon cancer, including CEBPB, PPARGC1, STAT3, MTOR, BCL2, JAK2, and CDK1.

Conclusions

The identification of CTS-specific transcriptional signatures may provide insights into the tumor microenvironment that mediates the development of colon cancer and has potential clinical implications for colon cancer diagnosis and treatment.

A Comprehensive Survey of Tools and Software for Active Subnetwork Identification

A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.

Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

BACKGROUND

Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles.

RESULTS

A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed "regulated exocytosis", "purine nucleotide binding", "pathways in cancer", and "cell cycle" between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated "early growth response protein 1 (EGR-1)", "hepatocyte nuclear factor 4α (HNF4A)", "mitogen-activated protein kinase 14 (MAP3K14)", and "cyclin-dependent kinase 1 (CDK1)" to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively.

CONCLUSION

Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

Informed walks: whispering hints to gene hunters inside networks' jungle

Background

Systemic approaches offer a different point of view on the analysis of several types of molecular associations as well as on the identification of specific gene communities in several cancer types. However, due to lack of sufficient data needed to construct networks based on experimental evidence, statistical gene co-expression networks are widely used instead. Many efforts have been made to exploit the information hidden in these networks. However, these approaches still need to capitalize comprehensively the prior knowledge encrypted into molecular pathway associations and improve their efficiency regarding the discovery of both exclusive subnetworks as candidate biomarkers and conserved subnetworks that may uncover common origins of several cancer types.

Methods

In this study we present the development of the Informed Walks model based on random walks that incorporate information from molecular pathways to mine candidate genes and gene-gene links. The proposed model has been applied to TCGA (The Cancer Genome Atlas) datasets from seven different cancer types, exploring the reconstructed co-expression networks of the whole set of genes and driving to highlighted sub-networks for each cancer type. In the sequel, we elucidated the impact of each subnetwork on the indication of underlying exclusive and common molecular mechanisms as well as on the short-listing of drugs that have the potential to suppress the corresponding cancer type through a drug-repurposing pipeline.

Conclusions

We have developed a method of gene subnetwork highlighting based on prior knowledge, capable to give fruitful insights regarding the underlying molecular mechanisms and valuable input to drug-repurposing pipelines for a variety of cancer types.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0473-6) contains supplementary material, which is available to authorized users.

D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery

Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases. The DMap web-application is available at: http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.

An unsupervised learning approach to find ovarian cancer genes through integration of biological data

Cancer is a disease characterized largely by the accumulation of out-of-control somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations has posed a challenge in modern cancer research. With the advanced development of microarray experiments and clinical studies, a large numbers of candidate cancer genes have been extracted and distinguishing informative genes out of them is essential. As a matter of fact, we proposed to find the informative genes for cancer by using mutation data from ovarian cancers in our framework. In our model we utilized the patient gene mutation profile, gene expression data and gene gene interactions network to construct a graphical representation of genes and patients. Markov processes for mutation and patients are triggered separately. After this process, cancer genes are prioritized automatically by examining their scores at their stationary distributions in the eigenvector. Extensive experiments demonstrate that the integration of heterogeneous sources of information is essential in finding important cancer genes.

Glycyrrhizin, silymarin, and ursodeoxycholic acid regulate a common hepatoprotective pathway in HepG2 cells

BACKGROUND

Glycyrrhizin, silymarin, and ursodeoxycholic acid are widely used hepatoprotectants for the treatment of liver disorders, such as hepatitis C virus infection, primary biliary cirrhosis, and hepatocellular carcinoma.

PURPOSE

The gene expression profiles of HepG2 cells responsive to glycyrrhizin, silymarin, and ursodeoxycholic acid were analyzed in this study.

METHODS

HepG2 cells were treated with 25 µM hepatoprotectants for 24 h. Gene expression profiles of hepatoprotectants-treated cells were analyzed by oligonucleotide microarray in triplicates. Nuclear factor-κB (NF-κB) activities were assessed by luciferase assay.

RESULTS

Among a total of 30,968 genes, 252 genes were commonly regulated by glycyrrhizin, silymarin, and ursodeoxycholic acid. These compounds affected the expression of genes relevant various biological pathways, such as neurotransmission, and glucose and lipid metabolism. Genes involved in hepatocarcinogenesis, apoptosis, and anti-oxidative pathways were differentially regulated by all compounds. Moreover, interaction networks showed that NF-κB might play a central role in the regulation of gene expression. Further analysis revealed that these hepatoprotectants inhibited NF-κB activities in a dose-dependent manner.

CONCLUSION

Our data suggested that glycyrrhizin, silymarin, and ursodeoxycholic acid regulated the expression of genes relevant to apoptosis and oxidative stress in HepG2 cells. Moreover, the regulation by these hepatoprotectants might be relevant to the suppression of NF-κB activities.

Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example.

Proteomics for systems toxicology

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration

Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca.