Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co‐expression network analysis

This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low‐grade glioma (LGG) and identify hub genes. A co‐expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co‐expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.     

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     

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     

Prognostic alternative splicing signature in cervical squamous cell carcinoma

Basing on alternative splicing events (ASEs) databases, the authors herein aim to explore potential prognostic biomarkers for cervical squamous cell carcinoma (CESC). mRNA expression profiles and relevant clinical data of 223 patients with CESC were obtained from The Cancer Genome Atlas (TCGA). Correlated genes, ASEs and percent‐splice‐in (PSI) were downloaded from SpliceSeq, respectively. The PSI values of survival‐associated alternative splicing events (SASEs) were used to construct the basis of a prognostic index (PI). A protein–protein interaction (PPI) network of genes related to SASEs was generated by STRING and analysed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Consequently, 41,776 ASEs were discovered in 19,724 genes, 2596 of which linked with 3669 SASEs. The PPI network of SASEs related genes revealed that TP53 and UBA52 were core genes. The low‐risk group had a longer survival period than high‐risk counterparts, both groups being defined according to PI constructed upon the top 20 splicing events or PI on the overall splicing events. The AUC value of ROC reached up to 0.88, demonstrating the prognostic potential of PI in CESC. These findings suggested that ASEs involve in the pathogenesis of CESC and may serve as promising prognostic biomarkers for this female malignancy.Inspec keywords: gynaecology, molecular biophysics, genomics, proteins, cellular biophysics, genetics, medical computing, cancer, ontologies (artificial intelligence), RNAOther keywords: protein‐protein interaction network, CESC pathogenesis, gene ontology, Kyoto‐encyclopedia‐of‐genes‐and‐genomes, SASEs related genes, PPI network, survival‐associated alternative splicing events, PSI values, percent‐splice‐in, Cancer Genome Atlas, mRNA expression profiles, prognostic biomarkers, alternative splicing events databases, cervical squamous cell carcinoma, prognostic alternative splicing signature     

Construction and characterization of rectal cancer‐related lncRNA‐mRNA ceRNA network reveals prognostic biomarkers in rectal cancer

Rectal cancer is an important cause of cancer‐related deaths worldwide. In this study, the differentially expressed (DE) lncRNAs/mRNAs were first identified and the correlation level between DE lncRNAs and mRNAs were calculated. The results showed that genes of highly correlated lncRNA‐mRNA pairs presented strong prognosis effects, such as GPM6A, METTL24, SCN7A, HAND2‐AS1 and PDZRN4. Then, the rectal cancer‐related lncRNA‐mRNA network was constructed based on the ceRNA theory. Topological analysis of the network revealed that the network was maintained by hub nodes and a hub subnetwork was constructed, including the hub lncRNA MIR143HG and MBNL1‐SA1. Further analysis indicated that the hub subnetwork was highly related to cancer pathways, such as ‘Focal adhesion’ and ‘Wnt signalling pathway’. Hub subnetwork also had significant prognosis capability. A closed lncRNA‐mRNA module was identified by bilateral network clustering. Genes in modules also showed high prognosis effects. Finally, a core lncRNA‐TF crosstalk network was identified to uncover the crosstalk and regulatory mechanisms of lncRNAs and TFs by integrating ceRNA crosstalks and TF binding affinities. Some core genes, such as MEIS1, GLI3 and HAND2‐AS1 were considered as the key regulators in tumourigenesis. Based on the authors’ comprehensive analysis, all these lncRNA‐mRNA crosstalks provided promising clues for biological prognosis of rectal cancer.     

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     

Identification of self‐regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data

Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self‐regulatory interactions. The values of gene co‐expressions could help in identifying co‐regulations among them. The proposed approach aims at computing the differences in variances of co‐expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co‐variances using principal component analysis (PCA) to predict an asymmetric and non‐diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time‐series RTX therapy, Arabidopsis thaliana, DREAM‐3, and DREAM‐8 datasets, in comparison with existing state‐of‐the‐art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self‐regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.Inspec keywords: molecular biophysics, principal component analysis, genetics, biology computing, reverse engineeringOther keywords: controlling regulatory agents, interacting genes, unlinked network components, self‐regulatory interactions, gene pair regulatory interactions, self‐regulatory network motifs, reverse engineering gene regulatory networks, microarray gene expression data, diversified computational approaches, symmetric interaction, diagonal interaction, gene pairs, gene co‐expressions, co‐expressed genes, mean expressions, gene regulatory expressions, asymmetric gene regulatory interactions     

Identification and analysis of circRNA–miRNA–mRNA regulatory network in hepatocellular carcinoma

This study was to identify important circRNA–miRNA–mRNA (ceRNAs) regulatory mechanisms in hepatocellular carcinoma (HCC). The circRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE97332","term_id":"97332"}}GSE97332 and miRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE57555","term_id":"57555"}}GSE57555 were used for analyses. Functional enrichment analysis for miRNA and target gene was conducted using cluster Profiler. Survival analysis was conducted through R package Survival. The ceRNAs and drug–gene interaction networks were constructed. The ceRNAs network contained five miRNAs including hsa‐miR‐25‐3p, hsa‐miR‐3692‐5p, hsa‐miR‐4270, hsa‐miR‐331‐3p, and hsa‐miR‐125a‐3p. Among the network, hsa‐miR‐25‐3p targeted the most genes, hsa‐miR‐3692‐5p and hsa‐miR‐4270 were targeted by more circRNAs than other miRNAs, hsa‐circ‐0034326 and hsa‐circ‐0011950 interacted with three miRNAs. Furthermore, target genes, including NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were obtained in drug–gene interaction network. Survival analysis showed NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were significantly associated with prognosis of HCC. NRAS, ITGA5, and SMAD2 were significantly enriched in proteoglycans in cancer. Moreover, hsa‐circ‐0034326 and hsa‐circ‐0011950 might function as ceRNAs to play key roles in HCC. Furthermore, miR‐25‐3p, miR‐3692‐5p, and miR‐4270 might be significant for HCC development. NRAS, ITGA5, SEC14L2, SLC12A5, and SMAD2 might be prognostic factors for HCC patients via proteoglycans in cancer pathway. Taken together, the findings will provide novel insight into pathogenesis, selection of therapeutic targets and prognostic factors for HCC.Inspec keywords: cancer, cellular biophysics, patient diagnosis, bioinformatics, tumours, biochemistry, molecular biophysics, genetics, drugs, RNAOther keywords: ITGA5, SMAD2, hsa‐circ‐0034326, SEC14L2, SLC12A5, target gene, survival analysis, drug–gene interaction network, miRNAs, hsa‐miR‐25‐3p, hsa‐miR‐3692‐5p, hsa‐miR‐4270, hsa‐miR‐331‐3p, hsa‐miR‐125a‐3p, hsa‐circ‐0011950, SLC7A1, pathogenesis, therapeutic targets, prognostic factors, circRNA‐miRNA‐mRNA regulatory network, current 125.0 A     

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     

Weighted gene co expression network analysis (WGCNA) with key pathways and hub‐genes related to micro RNAs in ischemic stroke

Ischemic stroke (IS) is one of the major causes of death and disability worldwide. However, the specific mechanism of gene interplay and the biological function in IS are not clear. Therefore, more research into IS is necessary. Dataset {"type":"entrez-geo","attrs":{"text":"GSE110993","term_id":"110993"}}GSE110993 including 20 ischemic stroke (IS) and 20 control specimens are used to establish both groups and the raw RNA‐seq data were analysed. Weighted gene co‐expression network analysis (WGCNA) was used to screen the key micro‐RNA modules. The centrality of key genes were determined by module membership (mm) and gene significance (GS). The key pathways were identified by enrichment analysis with Kyoto Protocol Gene and Genome Encyclopedia (KEGG), and the key genes were validated by protein‐protein interactions network. Result: Upon investigation, 1185 up‐ and down‐regulated genes were gathered and distributed into three modules in response to their degree of correlation to clinical traits of IS, among which the turquoise module show a trait‐correlation of 0.77. The top 140 genes were further identified by GS and MM. KEGG analysis showed two pathways may evolve in the progress of IS. Discussion: CXCL12 and EIF2a may be important biomarkers for the accurate diagnosis and treatment in IS.     

Identification of specific microRNA–messenger RNA regulation pairs in four subtypes of breast cancer

Four subtypes of breast cancer, luminal A, luminal B, basal‐like, human epidermal growth factor receptor‐enriched, have been identified based on gene expression profiles of human tumours. The goal of this study is to find whether the same groups'' genes would exhibit different networks among the four subtypes. Differential expressed genes between each of the four subtypes and the normal samples were identified. The overlaps between the four groups of differentially expressed genes were used to construct regulations networks for each of the four subtypes. Univariate and multivariate Cox regressions were employed to test the genes in the four regulation networks. This study demonstrated that the common genes in four subtypes showed different regulation. Also, the hsa‐miR‐182 and decorin pair performs different functions among the four subtypes of breast cancer. The result indicated that heterogeneity of breast cancer is not only reflected in the different expression patterns among different genes, but also in the different regulatory networks of the same group of genes.Inspec keywords: genetics, cellular biophysics, tumours, molecular biophysics, RNA, biochemistry, cancer, proteins, biology computingOther keywords: decorin pair performs different functions, breast cancer heterogeneity, regulatory networks, specific microRNA–messenger, regulation pairs, human epidermal growth factor receptor, gene expression profiles, differentially expressed genes, regulations networks, hsa‐miR‐182, decorin pair, human tumours     

Multi‐threshold and multi‐input DNA logic design style for profiling the microRNA biomarkers of real cancers

Early detection of cancer is very critical because it can reduce the treatment risk and cost. MicroRNAs (miRNAs) have been introduced in recent years as an efficient class of biomarkers for cancer early detection. Now, real‐time polymerase chain reaction has been used to profile the miRNA expression, which is costly, time consuming and low accuracy. Most recently, DNA logic gates are used to detect the miRNA expression level that is more accurate and faster than previous methods. The DNA‐based logic gates face with serious challenges such as the large complexity and low scalability. In this study, the authors proposed a methodology to design multi‐threshold and multi‐input DNA‐based logic gates in response to specific miRNA inputs in live mammalian cells. The proposed design style can simultaneously recognise multiple miRNAs with different rising and falling thresholds. The design style has been evaluated on the lung cancer biomarkers and the experimental results show the efficiency of the proposed method in terms of accuracy, efficiency and speed.Inspec keywords: DNA, logic design, biocomputing, RNA, molecular biophysics, logic gates, lung, genetics, cellular biophysics, cancer, biology computing, enzymes, biosensorsOther keywords: falling thresholds, specific miRNA inputs, multiinput DNA‐based logic gates, low scalability, DNA‐based logic gates face, miRNA expression level, DNA logic gates, low accuracy, time consuming, real‐time polymerase chain reaction, cancer early detection, treatment risk, cancers, microRNA biomarkers, multiinput DNA logic design style, multithreshold, lung cancer biomarkers     

Construction and analysis of single nucleotide polymorphism–single nucleotide polymorphism interaction networks

The study of gene regulatory network and protein–protein interaction network is believed to be fundamental to the understanding of molecular processes and functions in systems biology. In this study, the authors are interested in single nucleotide polymorphism (SNP) level and construct SNP–SNP interaction network to understand genetic characters and pathogenetic mechanisms of complex diseases. The authors employ existing methods to mine, model and evaluate a SNP sub‐network from SNP–SNP interactions. In the study, the authors employ the two SNP datasets: Parkinson disease and coronary artery disease to demonstrate the procedure of construction and analysis of SNP–SNP interaction networks. Experimental results are reported to demonstrate the procedure of construction and analysis of such SNP–SNP interaction networks can recover some existing biological results and related disease genes.Inspec keywords: biology computing, blood vessels, diseases, DNA, genetics, genomics, molecular biophysics, molecular configurations, polymorphism, proteins, RNAOther keywords: disease genes, coronary artery disease, datasets, Parkinson disease, complex diseases, pathogenetic mechanisms, genetic characters, systems biology functions, molecular processes, protein‐protein interaction network, gene regulatory network, single nucleotide polymorphism interaction networks     

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     

Modelling epigenetic regulation of gene expression in 12 human cell types reveals combinatorial patterns of cell‐type‐specific genes

The maintenance of the diverse cell types in a multicellular organism is one of the fundamental mysteries of biology. Modelling the dynamic regulatory relationships between the histone modifications and the gene expression across the diverse cell types is essential for the authors to understand the mechanisms of the epigenetic regulation. Here, the authors thoroughly assessed the histone modification enrichment profiles at the promoters and constructed quantitative models between the histone modification abundances and the gene expression in 12 human cell types. The author''s results showed that the histone modifications at the promoters exhibited remarkably cell‐type‐dependent variability in the cell‐type‐specific (CTS) genes. They demonstrated that the variable profiles of the modifications are highly predictive for the dynamic changes of the gene expression across all the cell types. Their findings revealed the close relationship between the combinatorial patterns of the histone modifications and the CTS gene expression. They anticipate that the findings and the methods they used in this study could provide useful information for the future studies of the regulatory roles of the histone modifications in the CTS genes.Inspec keywords: cellular biophysics, genetics, genomics, physiological models, proteinsOther keywords: CTS gene expression, variable profiles, cell‐type‐dependent variability, histone modification abundances, constructed quantitative models, promoters, histone modification enrichment profiles, dynamic regulatory relationship modelling, biology, multicellular organism, cell‐type‐specific genes, combinatorial patterns, human cell types, epigenetic regulation modelling