Fold change based approach for identification of significant network markers in breast,lung and prostate cancer

Cancer belongs to a class of highly aggressive diseases and a leading cause of death in the world. With more than 100 types of cancers, breast, lung and prostate cancer remain to be the most common types. To identify essential network markers (NMs) and therapeutic targets in these cancers, the authors present a novel approach which uses gene expression data from microarray and RNA‐seq platforms and utilises the results from this data to evaluate protein–protein interaction (PPI) network. Differentially expressed genes (DEGs) are extracted from microarray data using three different statistical methods in R, to produce a consistent set of genes. Also, DEGs are extracted from RNA‐seq data for the same three cancer types. DEG sets found to be common in both platforms are obtained at three fold change (FC) cut‐off levels to accurately identify the level of change in expression of these genes in all three cancers. A cancer network is built using PPI data characterising gene sets at log‐FC (LFC)>1, LFC>1.5 and LFC>2, and interconnection between principal hub nodes of these networks is observed. Resulting network of hubs at three FC levels highlights prime NMs with high confidence in multiple cancers as validated by Gene Ontology functional enrichment and maximal complete subgraphs from CFinder.Inspec keywords: cancer, proteins, RNA, bioinformatics, statistical analysis, genetics, molecular biophysics, ontologies (artificial intelligence), lungOther keywords: cancer network, PPI data, gene sets, multiple cancers, Gene Ontology functional enrichment, prostate cancer, gene expression data, RNA‐seq platforms, protein–protein interaction network, DEG, microarray data, RNA‐seq data, cancer types, lung cancer, diseases, breast cancer, network markers, differentially expressed genes, fold change based approach, CFinder, statistical methods     

Discovery of significant pathways in breast cancer metastasis via module extraction and comparison

Discovering significant pathways rather than single genes or small gene sets involved in metastasis is becoming more and more important in the study of breast cancer. Many researches have shed light on this problem. However, most of the existing works are relying on some priori biological information, which may bring bias to the models. The authors propose a new method that detects metastasis‐related pathways by identifying and comparing modules in metastasis and non‐metastasis gene co‐expression networks. The gene co‐expression networks are built by Pearson correlation coefficients, and then the modules inferred in these two networks are compared. In metastasis and non‐metastasis networks, 36 and 41 significant modules are identified. Also, 27.8% (metastasis) and 29.3% (non‐metastasis) of the modules are enriched significantly for one or several pathways with p ‐value <0.05. Many breast cancer genes including RB1, CCND1 and TP53 are included in these identified pathways. Five significant pathways are discovered only in metastasis network: glycolysis pathway, cell adhesion molecules, focal adhesion, stathmin and breast cancer resistance to antimicrotubule agents, and cytosolic DNA‐sensing pathway. The first three pathways have been proved to be closely associated with metastasis. The rest two can be taken as a guide for future research in breast cancer metastasis.Inspec keywords: cancer, genetics, genomics, DNA, molecular biophysics, adhesion, cellular biophysicsOther keywords: breast cancer metastasis, module extraction, gene sets, metastasis‐related pathways, nonmetastasis gene coexpression networks, Pearson correlation coefflcients, breast cancer genes, RB1, CCND1, TP53, glycolysis pathway, cell adhesion molecules, focal adhesion, stathmin, breast cancer resistance, antimicrotubule agents, cytosolic DNA‐sensing pathway, breast cancer metastasis     

Degree‐adjusted algorithm for prioritisation of candidate disease genes from gene expression and protein interactome

Computational methods play an important role in the disease genes prioritisation by integrating many kinds of data sources such as gene expression, functional annotations and protein–protein interactions. However, the existing methods usually perform well in predicting highly linked genes, whereas they work quite poorly for loosely linked genes. Motivated by this observation, a degree‐adjusted strategy is applied to improve the algorithm that was proposed earlier for the prediction of disease genes from gene expression and protein interactions. The authors also showed that the modified method is good at identifying loosely linked disease genes and the overall performance gets enhanced accordingly. This study suggests the importance of statistically adjusting the degree distribution bias in the background network for network‐based modelling of complex diseases.Inspec keywords: biochemistry, bioinformatics, diseases, genetics, genomics, medical computing, physiological models, proteins, statistical analysis, proteomicsOther keywords: degree‐adjusted algorithm, candidate disease genes prioritisation, gene expression, protein interactome, computational method, functional annotation, protein–protein interaction, highly linked genes prediction, disease genes prediction, loosely linked disease genes identification, degree distribution bias statistical adjustment, complex disease network‐based modelling     

Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co‐expression analysis

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival‐related individual genes (or miRNAs) and miRNA ‐mRNA pairs in GBM using a multi‐step approach. First, the weighted gene co‐expression network analysis and survival analysis are applied to identify survival‐related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival‐related miRNA ‐mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient''s survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin‐1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA ‐mRNA regulatory networks that comprised 38 survival‐related miRNA ‐mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.Inspec keywords: RNA, molecular biophysics, genetics, cancerOther keywords: signature regulatory network, glioblastoma prognosis, mRNA coexpression analysis, miRNA coexpression analysis, glioblastoma multiforme, brain tumour, microRNAs, pathogenesis, genome‐wide regulatory networks, miRNA‐mRNA pairs, weighted gene coexpression network analysis, survival analysis, GBM prognosis, integration analysis, neuropilin‐1 gene, module turquoise, molecular regulatory mechanisms     

Identify asthma genes across three phases based on protein–protein interaction network

Asthma is a common inflammatory disease that is generally caused by genetic mutations or environmental factors. Recently, the emerging of omics data provides an alternative way to understand asthma. In this study, the authors present a new framework to detect asthma disease genes based on protein–protein interaction network (PPIN) and gene expression. Specifically, they construct PPINs for different stages of asthma, and detect those interactions occurred in the specific stages. By investigating the proteins in these stage‐specific interactions, they find they are more likely related to asthma, and the functional enrichment analysis indicate that the pathways enriched in the differential interactions are related to the progress of asthma. Moreover, some proteins in the differential interactions have been previously reported to be related to asthma in the literature, implying the usefulness of the proposed approach.Inspec keywords: genomics, proteins, molecular biophysics, lung, pneumodynamics, diseases, genetics, molecule‐molecule reactions, molecule‐molecule collisionsOther keywords: asthma gene identification, three‐phase gene identification, protein–protein interaction network, common inflammatory disease, genetic mutation‐caused disease, environmental factors, asthma‐associated omics data, asthma disease gene detection, PPIN construction, asthma gene expression, asthma stages, stage‐specific interaction proteins, asthma stage‐specific interactions, asthma‐related interactions, functional enrichment analysis, asthma progress‐related differential interactions, differential interaction proteins     

Signalling pathway impact analysis based on the strength of interaction between genes

Signalling pathway analysis is a popular approach that is used to identify significant cancer‐related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (−1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer‐related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.Inspec keywords: genetics, cancer, biology computing, perturbation theory, biological organs, data analysisOther keywords: gene interaction strength, cancer‐related pathways, differentially expressed genes, biological experiments, signal perturbation propagation, signalling pathway impact analysis methods, Pearson correlation coefficient, mutual information, colorectal cancer dataset analysis, pancreatic cancer dataset, PSPIA, MSPIA     

Construction and investigation of breast‐cancer‐specific ceRNA network based on the mRNA and miRNA expression data

It has been proved and widely acknowledged that messenger RNAs can talk to each other by competing for a limited pool of miRNAs. The competing endogenous RNAs are called as ceRNAs. Although some researchers have recently used ceRNAs to do biological function annotations, few of them have investigated the ceRNA network on specific disease systematically. In this work, using both miRNA expression data and mRNA expression data of breast cancer patient as well as the miRNA target relations, the authors proposed a computational method to construct a breast‐cancer‐specific ceRNA network by checking whether the shared miRNA sponges between the gene pairs are significant. The ceRNA network is shown to be scale‐free, thus the topological characters such as hub nodes and communities may provide important clues for the biological mechanism. Through investigation on the communities (the dense clusters) in the network, it was found that they are related to cancer hallmarks. In addition, through function annotation of the hub genes in the network, it was found that they are related to breast cancer. Moreover, classifiers based on the discriminative hubs can significantly distinguish breast cancer patients’ risks of distant metastasis in all the three independent data sets.Inspec keywords: cancer, genetics, medical computing, molecular biophysics, RNAOther keywords: breast‐cancer specific ceRNA network construction, miRNA expression data, mRNA expression data, gene pairs, computational method, dense clusters, cancer hallmarks, biological mechanism, discriminative hub genes     

In silico identification of potential targets and drugs for non‐small cell lung cancer

Lung cancer is one of the leading causes of death in both the USA and Taiwan, and it is thought that the cause of cancer could be because of the gain of function of an oncoprotein or the loss of function of a tumour suppressor protein. Consequently, these proteins are potential targets for drugs. In this study, differentially expressed genes are identified, via an expression dataset generated from lung adenocarcinoma tumour and adjacent non‐tumour tissues. This study has integrated many complementary resources, that is, microarray, protein‐protein interaction and protein complex. After constructing the lung cancer protein‐protein interaction network (PPIN), the authors performed graph theory analysis of PPIN. Highly dense modules are identified, which are potential cancer‐associated protein complexes. Up‐ and down‐regulated communities were used as queries to perform functional enrichment analysis. Enriched biological processes and pathways are determined. These sets of up‐ and down‐regulated genes were submitted to the Connectivity Map web resource to identify potential drugs. The authors'' findings suggested that eight drugs from DrugBank and three drugs from NCBI can potentially reverse certain up‐ and down‐regulated genes'' expression. In conclusion, this study provides a systematic strategy to discover potential drugs and target genes for lung cancer.Inspec keywords: cellular biophysics, lung, cancer, drugs, genetics, tumours, lab‐on‐a‐chip, proteins, molecular biophysics, graph theory, query processing, medical computingOther keywords: down‐regulated gene expression, up‐regulated gene expression, potential target genes, DrugBank, potential drugs, connectivity map Web resource, biological processes, functional enrichment analysis, up‐regulated communities, down‐regulated communities, cancer‐associated protein complexes, k‐communities, highly‐dense modules, PPIN, graph theory analysis, lung cancer protein‐protein interaction network, MIPS, BioGrid, ArrayExpress, microarray, nontumour tissues, human lung adenocarcinoma tumour, bioconductor package, tumour suppressor protein, oncoprotein, nonsmall cell lung cancer, in silico identification     

Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma

Identification of oncogenic genes from a large sample number of genomic data is a challenge. In this study, a well‐established latent factor model, Bayesian factor and regression model, are applied to predict unknown colon cancer related genes from colon adenocarcinoma genomic data. Four important latent factors were addressed by the latent factor model, focusing on characterisation of heterogeneity of expression patterns of specific oncogenic genes by using microarray data of 174 colon cancer patients. Based on the fact that variables included in the same latent factor have some common characteristics and known cancer related genes in Online Mendelian Inheritance in Man, the authors found that the four latent factors can be employed to predict unknown colon cancer related genes that were never reported in the literature. The authors validated 15 identified genes by checking their somatic mutations of the same patients from DNA sequencing data.Inspec keywords: Bayes methods, biological organs, cancer, DNA, genetics, genomics, lab‐on‐a‐chip, medical diagnostic computing, molecular biophysics, physiological models, regression analysisOther keywords: latent factor analysis, oncogenic genes, colon adenocarcinoma, genomic data, Bayesian factor, colon cancer related genes, heterogeneity, expression patterns, DNA microarray data, Online Mendelian Inheritance in Man, somatic mutations, DNA sequencing data     

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer‐related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)‐based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP‐based prediction models. Prediction performance evaluations and comparisons between the authors’ GEP models and three representative machine learning methods, support vector machine, multi‐layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross‐data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.Inspec keywords: lung, cancer, medical diagnostic computing, patient diagnosis, genetic algorithms, feature selection, learning (artificial intelligence), support vector machines, multilayer perceptrons, radial basis function networks, reliability, sensitivity analysisOther keywords: lung cancer prediction, cancer‐related death, cancer diagnosis, gene profiles, gene expression programming‐based model, gene selection, GEP‐based prediction models, prediction performance evaluations, representative machine learning methods, support vector machine, multilayer perceptron, radial basis function neural network, real microarray lung cancer datasets, cross‐data set validation, reliability, receiver operating characteristic curve     

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non‐smokers with lung adenocarcinoma

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non‐coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non‐smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co‐expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA–mRNA pairs which were significantly dysregulated in non‐smokers with lung adenocarcinoma. Also two pairs (miR‐133b/Protein Kinase C Zeta (PRKCZ) and miR‐557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors’ understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.Inspec keywords: cancer, molecular biophysics, patient diagnosis, tumours, RNA, proteins, lung, genetics, medical diagnostic computing, molecular configurationsOther keywords: miRNAs expression profiles, mRNAs expression profiles, smokers, nonsmokers, integrative analysis, lung adenocarcinoma, microRNAs, disease specific biomarkers, noncoding regulators, genetic abnormalities, weighted gene coexpression network analysis     

Gaussian graphical model for identifying significantly responsive regulatory networks from time course high‐throughput data

With rapid accumulation of functional relationships between biological molecules, knowledge‐based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge‐based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge‐based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.Inspec keywords: Bayes methods, biology computing, circadian rhythms, Gaussian processes, genetics, genomics, graphs, molecular biophysics, proteinsOther keywords: Gaussian graphical model, responsive regulatory networks, time course high‐throughput data, biological molecules, dynamic gene expression proflling, circadian rhythm, consistency measurement, matching network structure, simulated time course microarray data, true time course microarray data, dynamic Bayesian network model, time course gene expression proflles, network architectures, documented regulatory networks, speciflc gene expression proflling data, phenotypes, proteins, functional linkages, databases, knowledge‐based networks     

Understanding the aristolochic acid toxicities in rat kidneys with regulatory networks

The natural products containing aristolochic acid (AA) have been widely used for acne, gastritis and so on. Recently, it is becoming accepted that AA may be responsible for acute and chronic renal failures as the side effects of Chinese herbs. However, it is unclear what happens in the cells after the AA treatment. In this study, the authors built a gene regulatory network as well as a microRNA–gene regulatory network to investigate the molecular dynamics induced by AA from a systematic perspective. With the regulatory networks, they detected some important pathways and biological processes that were affected by AA treatment, which can help explain the nephrotoxicity and carcinogenicity of AA. They found some important regulators and genes responding to AA treatment, and these genes have been reported to be related to the kidney functions, indicating their important roles in the toxicity of AA.Inspec keywords: kidney, toxicology, genetics, RNA, molecular biophysics, cancer, bioinformaticsOther keywords: aristolochic acid toxicities, rat kidneys, regulatory networks, acne, gastritis, acute renal failures, chronic renal failures, Chinese herbs, gene regulatory network, microRNA‐gene regulatory network, molecular dynamics, nephrotoxicity, carcinogenicity     

Systems analysis utilising pathway interactions identifies sonic hedgehog pathway as a primary biomarker and oncogenic target in hepatocellular carcinoma

The development and progression of cancer is associated with disruption of biological networks. Historically studies have identified sets of signature genes involved in events ultimately leading to the development of cancer. Identification of such sets does not indicate which biologic processes are oncogenic drivers and makes it difficult to identify key networks to target for interventions. Using a comprehensive, integrated computational approach, the authors identify the sonic hedgehog (SHH) pathway as the gene network that most significantly distinguishes tumour and tumour‐adjacent samples in human hepatocellular carcinoma (HCC). The analysis reveals that the SHH pathway is commonly activated in the tumour samples and its activity most significantly differentiates tumour from the non‐tumour samples. The authors experimentally validate these in silico findings in the same biologic material using Western blot analysis. This analysis reveals that the expression levels of SHH, phosphorylated cyclin B1, and CDK7 levels are much higher in most tumour tissues as compared to normal tissue. It is also shown that siRNA‐mediated silencing of SHH gene expression resulted in a significant reduction of cell proliferation in a liver cancer cell line, SNU449 indicating that SHH plays a major role in promoting cell proliferation in liver cancer. The SHH pathway is a key network underpinning HCC aetiology which may guide the development of interventions for this most common form of human liver cancer.Inspec keywords: bioinformatics, cancer, cellular biophysics, genetics, liver, molecular biophysics, RNA, systems analysis, tumoursOther keywords: biomedical informatics, human liver cancer, network underpinning HCC aetiology, liver cancer cell line, cell proliferation, SHH gene expression, siRNA‐mediated silencing, CDK7 levels, phosphorylated cyclin B1, Western blot analysis, in silico findings, SHH pathway, human hepatocellular carcinoma, tumour‐adjacent samples, gene network, integrated computational approach, oncogenic drivers, biologic processes, cancer development, biological networks, cancer progression, oncogenic target, primary biomarker, sonic hedgehog pathway, pathway interactions, systems analysis     

Gene regulatory network discovery using pairwise Granger causality

Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time‐series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pairwisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full‐model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell‐cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.Inspec keywords: biology computing, cancer, causality, cellular biophysics, genetics, genomics, time seriesOther keywords: gene regulatory network discovery, pairwise Granger causality, gene expression data, drug development, time‐series data, synthetic data, spurious causalities, full‐model Granger causality detection, vector autoregressive model, real human HeLa cell‐cycle dataset, Akaike information criterion, degree distributions, network hubs