Ranking differential hubs in gene co-expression networks

Identifying the genes that change their expressions between two conditions (such as normal versus cancer) is a crucial task that can help in understanding the causes of diseases. Differential networking has emerged as a powerful approach to detect the changes in network structures and to identify the differentially connected genes among two networks. However, existing differential network-based methods primarily depend on pairwise comparisons of the genes based on their connectivity. Therefore, these methods cannot capture the essential topological changes in the network structures. In this paper, we propose a novel algorithm, DiffRank, which ranks the genes based on their contribution to the differences between the two networks. To achieve this goal, we define two novel structural scoring measures: a local structure measure (differential connectivity) and a global structure measure (differential betweenness centrality). These measures are optimized by propagating the scores through the network structure and then ranking the genes based on these propagated scores. We demonstrate the effectiveness of DiffRank on synthetic and real datasets. For the synthetic datasets, we developed a simulator for generating synthetic differential scale-free networks, and we compared our method with existing methods. The comparisons show that our algorithm outperforms these existing methods. For the real datasets, we apply the proposed algorithm on several gene expression datasets and demonstrate that the proposed method provides biologically interesting results.     

Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression

Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks.Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples.We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer''s disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.     

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.     

Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer

Understanding cancer cell signal transduction is a promising lead for uncovering therapeutic targets and building treatment-specific markers for epithelial ovarian cancer. To brodaly assay the many known transmembrane receptor systems, previous studies have employed gene expression data measured on high-throughput microarrays. Starting with the knowledge of validated ligand-receptor pairs (LRPs), these studies postulate that correlation of the two genes implies functional autocrine signaling. It is our goal to consider the additional weight of evidence that prognosis (progression-free survival) can bring to prioritize ovarian cancer specific signaling mechanism. We survey three large studies of epithelial ovarian cancers, with gene expression measurements and clinical information, by modeling survival times both categorically (long/short survival) and continuously. We use differential correlation and proportional hazards regression to identify sets of LRPs that are both prognostic and correlated. Of 475 candidate LRPs, 77 show reproducible evidence of correlation; 55 show differential correlation. Survival models identify 16 LRPs with reproduced, significant interactions. Only two pairs show both interactions and correlation (PDGFAPDGFRA and COL1A1CD44) suggesting that the majority of prognostically useful LRPs act without positive feedback. We further assess the connectivity of receptors using a Gaussian graphical model finding one large graph and a number of smaller disconnected networks. These LRPs can be organized into mutually exclusive signaling clusters suggesting different mechanisms apply to different patients. We conclude that a mix of autocrine and endocrine LRPs influence prognosis in ovarian cancer, there exists a heterogenous mix of signaling themes across patients, and we point to a number of novel applications of existing targeted therapies which may benefit ovarian cancer.     

Temporal expression and functional analysis of long non‐coding RNAs in colorectal cancer initiation

Long non‐coding RNAs (lncRNAs) have potential applications in clinical diagnosis and targeted cancer therapies. However, the expression profile of lncRNAs in colorectal cancer (CRC) initiation is still unclear. In this study, the expression profiles of lncRNAs and mRNAs were determined by microarray at specific tumour stages in an AOM/DSS‐induced primary colon cancer model. The temporal expression of lncRNAs was analysed by K‐means clustering. Additionally, weighted correlation network analysis (WGCNA) and gene ontology analysis were performed to construct co‐expression networks and establish functions of the identified lncRNAs and mRNAs. Our results suggested that 4307 lncRNAs and 5798 mRNAs are deregulated during CRC initiation. These differential expression genes (DEGs) exhibited a clear correlation with the differential stage of tumour initiation. WGCNA results suggested that a series of hub lncRNAs are involved in regulating cell stemness, colon inflammation, oxidative stress response and cell death at each stage. Among them, lncRNA H19 was up‐regulated in colon tumours and correlated with poor patient prognosis. Collectively, we have been the first to demonstrate the temporal expression and function of lncRNAs in CRC initiation. These results provide novel diagnosis and therapy targets for CRC.     

Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues.     

Identification of differentially expressed genes associated with HER-2/neu overexpression in human breast cancer cells.

Amplification and resulting overexpression of the HER-2/ neu proto-oncogene is found in approximately 30% of human breast and 20% of human ovarian cancers. To better understand the molecular events associated with overexpression of this gene in human breast cancer cells, differential hybridization was used to identify genes whose expression levels are altered in cells overexpressing this receptor. Of 16 000 clones screened from an overexpression cell cDNA library, a total of 19 non-redundant clones were isolated including seven whose expression decreases (C clones) and 12 which increase (H clones) in association with HER-2/ neu overexpression. Of these, five C clones and 11 H clones have been confirmed to be differentially expressed by northern blot analysis. This group includes nine genes of known function, three previously sequenced genes of relatively uncharacterized function and four novel genes without a match in GenBank. Examination of the previously characterized genes indicates that they represent sequences known to be frequently associated with the malignant phenotype, suggesting that the subtraction cloning strategy used identified appropriate target genes. In addition, differential expression of 12 of 16 (75%) cDNAs identified in the breast cancer cell lines are also seen in HER-2/ neu -overexpressing ovarian cancer cells, indicating that they represent generic associations with HER-2/ neu overexpression. Finally, up-regulation of two of the identified cDNAs, one novel and one identified but as yet uncharacterized gene, was confirmed in human breast cancer specimens in association with HER-2/ neu overexpression. Further characterization of these genes may yield insight into the fundamental biology and pathogenetic effects of HER-2/ neu overexpression in human breast and ovarian cancer cells.     

Effects of neonatal hypothyroidism on rat brain gene expression.

To define at the molecular biological level the effects of thyroid hormone on brain development we have examined cDNA clones of brain mRNAs and identified several whose expression is altered in hypothyroid animals during the neonatal period. Clones were identified with probes prepared by subtractive or differential hybridization, and those corresponding to mRNAs altered in hypothyroidism were further studied by Northern blot analysis. Using RNA prepared from whole brains, no effect of hypothyroidism was found on the expression of the astroglial gene coding for glial fibrillary acidic protein. Among genes of neuronal expression, no significant alterations were found in the steady state levels of mRNAs coding for neuron-specific enolase, microtubule-associated protein-2, Tau, or nerve growth factor. N-CAM mRNA increased slightly in hypothyroid brains. In contrast a 2- to 3-fold decrease was found in the mRNA coding for a novel neuronal gene, RC3. This is the first neuronal gene known to be significantly altered at the mRNA level by thyroid hormone deprivation. The abundance of the mRNAs for the major myelin proteins proteolipid protein, myelin basic protein, and myelin-associated glycoprotein, expressed by oligodendrocytes, were also decreased in hypothyroid brains. Developmental studies on RC3 and myelin-associated glycoprotein expression indicated that the corresponding mRNAs accumulate in the brain of normal rats during the first 15-20 days of neonatal life. A similar accumulation occurred in hypothyroid brains, but at much reduced levels. The results demonstrate that thyroid hormone controls the steady state levels of particular mRNAs during brain development.     

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co‐expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co‐expression of lncRNAs and protein‐coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA‐PCG differential co‐expression network (DCN). DCN was characterized as a scale‐free, small‐world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA‐PCG subnetworks were identified from the DCN by integrating both differential expression and differential co‐expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.     

Novel reference gene RPN1 for normalization of quantitative data in lung and kidney cancer

Quantitative methods of gene expression analysis in tumors require accurate data normalization, which allows comparison of different mRNA/cDNA samples with unknown concentration. For this purpose reference genes with stable expression level (such as GAPDH, ACTB, HPRT1, TBP) are used. The choice of appropriate reference genes is still actual because well-known reference genes are not suitable for certain cancer types frequently and their unreasonable use without additional tests lead to wrong conclusions. We have developed the bioinformatic approach and selected a new potential reference gene RPN1 for lung and kidney tumors. This gene is located at the long arm of chromosome 3. Our method includes mining of the dbEST and Oncomine databases and functional analysis of genes. The RPN1 was selected from 1500 candidate housekeeping genes. Using comparative genomic hybridization with NotI-microarrays we found no methylation, deletions and/or amplifications at the RPN1-containing locus in 56 non-small cell lung and 42 clear cell renal cancer samples. Using RT-qPCR we showed low variability of RPN1 mRNA level comparable to those of reference genes GAPDH and GUSB in lung and kidney cancer. The mRNA levels of two target genes coding hyalouronidases--HYAL1 and HYAL2--were estimated and normalized relative to pair RPN1--GAPDH genes for lung cancer and RPN1--GUSB for kidney cancer. These combinations were shown to be optimal for obtaining accurate and reproducible data. All obtained results allow us to suggest RPN1 as novel reference gene for quantitative data normalization in gene expression studies for lung and kidney cancers.     

CNAmet: an R package for integrating copy number, methylation and expression data

SUMMARY: Gene copy number and DNA methylation alterations are key regulators of gene expression in cancer. Accordingly, genes that show simultaneous methylation, copy number and expression alterations are likely to have a key role in tumor progression. We have implemented a novel software package (CNAmet) for integrative analysis of high-throughput copy number, DNA methylation and gene expression data. To demonstrate the utility of CNAmet, we use copy number, DNA methylation and gene expression data from 50 glioblastoma multiforme and 188 ovarian cancer primary tumor samples. Our results reveal a synergistic effect of DNA methylation and copy number alterations on gene expression for several known oncogenes as well as novel candidate oncogenes. AVAILABILITY: CNAmet R-package and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/CNAmet.     

Differential coexpression analysis using microarray data and its application to human cancer

MOTIVATION: Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer. RESULTS: We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed 2 distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth and enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer.     

Network analysis of skin tumor progression identifies a rewired genetic architecture affecting inflammation and tumor susceptibility

Background  

Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown.     

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell''s state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato''s Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.