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     

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     

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     

Crosstalk between pathways enhances the controllability of signalling networks

The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow‐tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross‐pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors’ work provides a comprehensive understanding of the impact of network structures and properties on controllability.Inspec keywords: genetics, numerical analysis, control theoryOther keywords: signalling network controllability, interconnected pathways, bow‐tie architecture, structure controllability, biological mechanisms, cross‐pathway interactions, numerical simulations, biological networks, control theory, gene regulatory network     

Bifurcation analysis of insulin regulated mTOR signalling pathway in cancer cells

Insulin induced mTOR signalling pathway is a complex network implicated in many types of cancers. The molecular mechanism of this pathway is highly complex and the dynamics is tightly regulated by intricate positive and negative feedback loops. In breast cancer cell lines, metformin has been shown to induce phosphorylation at specific serine sites in insulin regulated substrate of mTOR pathway that results in apoptosis over cell proliferation. The author models and performs bifurcation analysis to simulate cell proliferation and apoptosis in mTOR signalling pathway to capture the dynamics both in the presence and absence of metformin in cancer cells. Metformin is shown to negatively regulate PI3K through AMPK induced IRS1 phosphorylation and this brings about a reversal of AKT bistablity in codimension‐1 bifurcation diagram from S‐shaped, related to cell proliferation in the absence of drug metformin, to Z‐shaped, related to apoptosis in the presence of drug metformin. The author hypothesises and explains how this negative regulation acts a circuit breaker, as a result of which mTOR network favours apoptosis of cancer cells over its proliferation. The implication of reversing the shape of bistable dynamics from S to Z or vice‐versa in biological networks in general is discussed.Inspec keywords: bifurcation, molecular biophysics, drugs, enzymes, biochemistry, cellular biophysics, cancer, biomedical materialsOther keywords: intricate positive feedback loops, negative feedback loops, breast cancer cell lines, insulin regulated substrate, cell proliferation, cancer cells, AMPK induced IRS1 phosphorylation, codimension‐1 bifurcation diagram, drug metformin, mTOR network, insulin regulated mTOR signalling pathway, bifurcation analysis, PI3K, AKT bistablity     

Analysis of healthy and tumour DNA methylation distributions in kidney‐renal‐clear‐cell‐carcinoma using Kullback–Leibler and Jensen–Shannon distance measures

DNA methylation is an epigenetic phenomenon in which methyl groups get bonded to the cytosines of the DNA molecule altering the expression of the associated genes. Cancer is linked with hypo or hyper‐methylation of specific genes as well as global changes in DNA methylation. In this study, the authors study the probability density function distribution of DNA methylation in various significant genes and across the genome in healthy and tumour samples. They propose a unique ‘average healthy methylation distribution’ based on the methylation values of several healthy samples. They then obtain the Kullback–Leibler and Jensen–Shannon distances between methylation distributions of the healthy and tumour samples and the average healthy methylation distribution. The distance measures of the healthy and tumour samples from the average healthy methylation distribution are compared and the differences in the distances are analysed as possible parameters for cancer. A classifier trained on these values was found to provide high values of sensitivity and specificity. They consider this to be a computationally efficient approach to predict tumour samples based on DNA methylation data. This technique can also be improvised to consider other differentially methylated genes significant in cancer or other epigenetic diseases.Inspec keywords: cancer, tumours, DNA, genetics, molecular biophysicsOther keywords: tumour DNA methylation distributions, kidney‐renal‐clear‐cell‐carcinoma, Kullback–Leibler distance measure, Jensen–Shannon distance measure, epigenetic phenomenon, methyl groups, cytosines, hyper‐methylation, probability density function distribution, average healthy methylation distribution     

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     

BBN conjugated GNPs: a new targeting contrast agent for imaging of breast cancer in radiology

Using of targeted contrast agents in X‐ray imaging of breast cancer can improve the accuracy of diagnosis, staging, and treatment planning by providing early detection and superior definition of tumour volume. This study demonstrates a new class of X‐ray contrast agents based on gold nanoparticles (GNPs) and bombesin (BBN) for imaging of breast cancer in radiology. GNPs were synthesised in spherical shape in the size range of 15 ± 2 nm and conjugated with BBN followed by coating with polyethyleneglycol (PEG). The in vitro and in vivo behaviour of PEG‐coated GNPs‐BBN conjugate was investigated performing cytotoxicity, binding, and internalisation assays as well as biodistribution and X‐ray imaging studies in mouse bearing breast tumour. Cytotoxicity study showed biocompatibility of the prepared bioconjugate. The binding and internalisation studies using T47D cell line approved the targeting ability of new agent. The biodistribution study showed the considerable accumulation of prepared conjugate in breast tumour in mouse model. The breast tumour was clearly visualised in X‐ray images taken from the mouse model. The results showed the potential of PEG‐coated GNPs‐BBN conjugate as a contrast agent in X‐ray imaging of breast tumour in humans that need further investigations.Inspec keywords: diagnostic radiography, cancer, tumours, radiology, gold, nanoparticles, nanomedicine, polymers, coatings, toxicology, cellular biophysicsOther keywords: bombesin conjugated gold nanoparticles, breast cancer, radiology, targeted contrast agents, X‐ray imaging, X‐ray contrast agents, spherical shape, polyethyleneglycol, coating, in vitro behaviour, in vivo behaviour, cytotoxicity, internalisation assays, biodistribution, mouse bearing breast tumour, biocompatibility, bioconjugate, T47D cell line, Au     

Ant colony optimisation of decision tree and contingency table models for the discovery of gene–gene interactions

In this study, ant colony optimisation (ACO) algorithm is used to derive near‐optimal interactions between a number of single nucleotide polymorphisms (SNPs). This approach is used to discover small numbers of SNPs that are combined into a decision tree or contingency table model. The ACO algorithm is shown to be very robust as it is proven to be able to find results that are discriminatory from a statistical perspective with logical interactions, decision tree and contingency table models for various numbers of SNPs considered in the interaction. A large number of the SNPs discovered here have been already identified in large genome‐wide association studies to be related to type II diabetes in the literature, lending additional confidence to the results.Inspec keywords: genetics, genomics, DNA, polymorphism, molecular biophysics, molecular configurations, ant colony optimisation, decision trees, bioinformatics, diseasesOther keywords: ant colony optimisation, decision tree, contingency table models, gene‐gene interactions, ACO algorithm, near‐optimal interactions, single nucleotide polymorphisms, SNP, genome‐wide association studies, type II diabetes     

Chitosan nanoparticles loaded with aspirin and 5‐fluororacil enable synergistic antitumour activity through the modulation of NF‐κB/COX‐2 signalling pathway

Based on the enhancement of synergistic antitumour activity to treat cancer and the correlation between inflammation and carcinogenesis, the authors designed chitosan nanoparticles for co‐delivery of 5‐fluororacil (5‐Fu: an as anti‐cancer drug) and aspirin (a non‐steroidal anti‐inflammatory drug) and induced synergistic antitumour activity through the modulation of the nuclear factor kappa B (NF‐κB)/cyclooxygenase‐2 (COX‐2) signalling pathways. The results showed that aspirin at non‐cytotoxic concentrations synergistically sensitised hepatocellular carcinoma cells to 5‐Fu in vitro. It demonstrated that aspirin inhibited NF‐κB activation and suppressed NF‐κB regulated COX‐2 expression and prostaglandin E2 (PGE2) synthesis. Furthermore, the proposed results clearly indicated that the combination of 5‐Fu and aspirin by chitosan nanoparticles enhanced the intracellular concentration of drugs and exerted synergistic growth inhibition and apoptosis induction on hepatocellular carcinoma cells by suppressing NF‐κB activation and inhibition of expression of COX‐2.Inspec keywords: proteins, molecular biophysics, cellular biophysics, biomedical materials, cancer, nanoparticles, drug delivery systems, enzymes, tumours, nanomedicine, drugsOther keywords: chitosan nanoparticles, aspirin, 5‐fluororacil, synergistic antitumour activity, anticancer drug, nonsteroidal antiinflammatory drug, hepatocellular carcinoma cells, NF‐κB activation, NF‐κB regulated COX‐2 expression, PGE2, synergistic growth inhibition, apoptosis induction, prostaglandin E2 synthesis, intracellular concentration, noncytotoxic concentrations, NF‐κB‐cyclooxygenase‐2 signalling pathways, cyclooxygenase‐2, nuclear factor kappa B     

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     

Emerging microfluidic devices for cancer cells/biomarkers manipulation and detection

Circulating tumour cells (CTCs) are active participants in the metastasis process and account for ∼90% of all cancer deaths. As CTCs are admixed with a very large amount of erythrocytes, leukocytes, and platelets in blood, CTCs are very rare, making their isolation, capture, and detection a major technological challenge. Microfluidic technologies have opened‐up new opportunities for the screening of blood samples and the detection of CTCs or other important cancer biomarker‐proteins. In this study, the authors have reviewed the most recent developments in microfluidic devices for cells/biomarkers manipulation and detection, focusing their attention on immunomagnetic‐affinity‐based devices, dielectrophoresis‐based devices, surface‐plasmon‐resonance microfluidic sensors, and quantum‐dots‐based sensors.Inspec keywords: microfluidics, bioMEMS, cancer, cellular biophysics, biomedical equipment, patient diagnosis, tumours, proteins, molecular biophysics, electrophoresis, surface plasmon resonance, quantum dotsOther keywords: quantum‐dot‐based sensors, surface‐plasmon‐resonance microfluidic sensors, dielectrophoresis‐based devices, immunomagnetic‐affinity‐based devices, cancer biomarker‐proteins, CTC detection, blood samples, microfluidic technology, platelets, leukocytes, leukocytes, erythrocytes, cancer deaths, metastasis process, circulating tumour cells, cancer cell‐biomarker detection, cancer cell‐biomarker manipulation, microfluidic devices     

Mining gene link information for survival pathway hunting

This study proposes a gene link‐based method for survival time‐related pathway hunting. In this method, the authors incorporate gene link information to estimate how a pathway is associated with cancer patient''s survival time. Specifically, a gene link‐based Cox proportional hazard model (Link‐Cox) is established, in which two linked genes are considered together to represent a link variable and the association of the link with survival time is assessed using Cox proportional hazard model. On the basis of the Link‐Cox model, the authors formulate a new statistic for measuring the association of a pathway with survival time of cancer patients, referred to as pathway survival score (PSS), by summarising survival significance over all the gene links in the pathway, and devise a permutation test to test the significance of an observed PSS. To evaluate the proposed method, the authors applied it to simulation data and two publicly available real‐world gene expression data sets. Extensive comparisons with previous methods show the effectiveness and efficiency of the proposed method for survival pathway hunting.Inspec keywords: cancer, physiological models, bioinformatics, genomicsOther keywords: permutation test, pathway survival score, gene link‐based Cox proportional hazard model, cancer patient survival time, survival time‐related pathway hunting, gene link‐based method     

Robust group fused lasso for multisample copy number variation detection under uncertainty

One of the most important needs in the post‐genome era is providing the researchers with reliable and efficient computational tools to extract and analyse this huge amount of biological data, in which DNA copy number variation (CNV) is a vitally important one. Array‐based comparative genomic hybridisation (aCGH) is a common approach in order to detect CNVs. Most of methods for this purpose were proposed for one‐dimensional profiles. However, slightly this focus has moved from one‐ to multi‐dimensional signals. In addition, since contamination of these profiles with noise is always an issue, it is highly important to have a robust method for analysing multi‐sample aCGH profiles. In this study, the authors propose robust group fused lasso which utilises the robust group total variations. Instead of l _2,1 norm, the l ₁ − l ₂ M‐estimator is used which is more robust in dealing with non‐Gaussian noise and high corruption. More importantly, Correntropy (Welsch M‐estimator) is also applied for fitting error. Extensive experiments indicate that the proposed method outperforms the state‐of‐the art algorithms and techniques under a wide range of scenarios with diverse noises.Inspec keywords: genomics, DNA, molecular biophysics, Gaussian noise, entropyOther keywords: robust group fused lasso, multisample copy number variation detection, post‐genome era, computational tools, biological data, DNA copy number variation, array‐based comparative genomic hybridisation, one‐dimensional profiles, one‐dimensional signals, multidimensional signals, profile contamination, multisample aCGH profiles, robust group total variations, l₁ ‐l₂ M‐estimator, nonGaussian noise, correntropy, Welsch M‐estimator, fitting error, diverse noises     

Inferring catalysis in biological systems

In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data. This may be due to unknown but present catalytic reactions. From a modelling perspective, the question of whether a certain reaction is catalysed leads to a large increase of model candidates. For large networks the calibration of all possible models becomes computationally infeasible. We propose a method which determines a substantially reduced set of appropriate model candidates and identifies the catalyst of each reaction at the same time. This is incorporated in a multiple‐step procedure which first extends the network by additional latent variables and subsequently identifies catalyst candidates using similarity analysis methods. Results from synthetic data examples suggest a good performance even for non‐informative data with few observations. Applied on CD95 apoptotic pathway our method provides new insights into apoptosis regulation.Inspec keywords: catalysis, catalysts, biochemistry, genetics, enzymes, biology computing, calibration, molecular clustersOther keywords: inferring catalysis, biological systems, systems biology, communication patterns, genes, enzymes, proteins, time‐resolved experiments, time‐resolved communication, reaction networks, gene regulatory networks, biochemical networks, signalling pathways, mathematical data modelling, catalytic reactions, calibration, catalyst, multiple‐step procedure, latent variables, similarity analysis methods, noninformative data, differentiation apoptotic pathway, cluster     

Classification of drug molecules for oxidative stress signalling pathway

In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers’ disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2‐antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE signalling pathway and to propose a multi‐level prediction scheme for the same is the main aim of the study as it contributes in a big amount to the improvement of oxidative stress in humans. Applying the process of knowledge discovery from data, required knowledge is gathered and then machine learning techniques are applied to propose a multi‐level scheme. The validation of the proposed scheme is done using the K‐fold cross‐validation method and an accuracy of 90% is achieved for prediction of activity score for ARE molecules which determine their power to refine oxidative stress.Inspec keywords: cancer, cellular biophysics, biochemistry, drugs, molecular biophysics, proteins, learning (artificial intelligence), medical computingOther keywords: oxidative stress, Nrf2‐antioxidant response element signalling pathway, ARE signalling pathway, diabetes, cancer, hypertension, Alzheimers’ disease, heart failure, machine learning techniques, K‐fold cross‐validation method, ARE molecules     

Network‐based method for detecting dysregulated pathways in glioblastoma cancer

The knowledge on the biological molecular mechanisms underlying cancer is important for the precise diagnosis and treatment of cancer patients. Detecting dysregulated pathways in cancer can provide insights into the mechanism of cancer and help to detect novel drug targets. Based on the wide existing mutual exclusivity among mutated genes and the interrelationship between gene mutations and expression changes, this study presents a network‐based method to detect the dysregulated pathways from gene mutations and expression data of the glioblastoma cancer. First, the authors construct a gene network based on mutual exclusivity between each pair of genes and the interaction between gene mutations and expression changes. Then they detect all complete subgraphs using CFinder clustering algorithm in the constructed gene network. Next, the two gene sets whose overlapping scores are above a specific threshold are merged. Finally, they obtain two dysregulated pathways in which there are glioblastoma‐related multiple genes which are closely related to the two subtypes of glioblastoma. The results show that one dysregulated pathway revolving around epidermal growth factor receptor is likely to be associated with the primary subtype of glioblastoma, and the other dysregulated pathway revolving around TP53 is likely to be associated with the secondary subtype of glioblastoma.Inspec keywords: cancer, tumours, drugs, brain, neurophysiology, genetic algorithms, genetics, skin, proteins, molecular biophysics, genomics, patient diagnosis, molecular configurationsOther keywords: network‐based method, dysregulated pathways detection, glioblastoma cancer, biological molecular mechanisms, precise diagnosis, cancer patient treatment, drug targets, mutual exclusivity, mutated genes, gene mutations, expression changes, expression data, CFinder clustering algorithm, constructed gene network, gene sets, overlapping scores, glioblastoma‐related multiple genes, epidermal growth factor receptor, TP53, secondary subtype     

Gene expression feature selection for prostate cancer diagnosis using a two‐phase heuristic–deterministic search strategy

Here, a two‐phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi‐objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache‐based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.Inspec keywords: cancer, biological organs, optimisation, feature extraction, search problems, particle swarm optimisation, pattern classification, geneticsOther keywords: biomarkers, gene expression feature selection, prostate cancer diagnosis, heuristic–deterministic search strategy, two‐phase search strategy, gene expression data, statistical filtering method, noisiest data, multiobjective optimisation, particle swarm optimisation algorithm, chaotic method, selection algorithm, discriminant genes, available challenging prostate cancer data, informative genes     

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