Search

This study is aimed at investigating the potential molecular features of allergic rhinitis (AR) and identifying gene signatures and related transcription factors using transcriptome analysis and in silico datasets. Transcriptome profiles were obtained using three independent cohorts (GSE101720, GSE19190, and GSE46171) comprising healthy controls (HC) and patients with AR. The pooled dataset (n = 82) was used to identify the critical signatures of AR compared with HC. Subsequently, key transcription factors were identified by a combined analysis using transcriptome and in silico datasets. Gene ontology: bioprocess (GO: BP) analysis using differentially expressed genes (DEGs) revealed that immune response-related genes were significantly enriched in AR compared with HC. Among them, IL1RL1, CD274, and CD44 were significantly higher in AR patients. We also identified key transcription factors between HC and AR using the in silico dataset and found that AR samples frequently express KLF transcription factor 4 (KLF4), which regulates immune response-related genes including IL1RL1, CD274, and CD44 in human nasal epithelial cells. Our integrative analysis of transcriptomic regulation provides new insights into AR, which may help in developing precision management for patients with AR. [ABSTRACT FROM AUTHOR]

The naïve Bayes classifier (NBC) is one of the most popular classifiers for class prediction or pattern recognition from microarray gene expression data (MGED). However, it is very much sensitive to outliers with the classical estimates of the location and scale parameters. It is one of the most important drawbacks for gene expression data analysis by the classical NBC. The gene expression dataset is often contaminated by outliers due to several steps involved in the data generating process from hybridization of DNA samples to image analysis. Therefore, in this paper, an attempt is made to robustify the Gaussian NBC by the minimum β-divergence method. The role of minimum β-divergence method in this article is to produce the robust estimators for the location and scale parameters based on the training dataset and outlier detection and modification in test dataset. The performance of the proposed method depends on the tuning parameter β. It reduces to the traditional naïve Bayes classifier when β → 0. We investigated the performance of the proposed beta naïve Bayes classifier (β-NBC) in a comparison with some popular existing classifiers (NBC, KNN, SVM, and AdaBoost) using both simulated and real gene expression datasets. We observed that the proposed method improved the performance over the others in presence of outliers. Otherwise, it keeps almost equal performance. [ABSTRACT FROM AUTHOR]

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition. [ABSTRACT FROM AUTHOR]

Background. To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. Results. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR), and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. Conclusions. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms. [ABSTRACT FROM AUTHOR]

The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The error function based on L1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others. [ABSTRACT FROM AUTHOR]

Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms. [ABSTRACT FROM AUTHOR]

Objective. The aim of the study was to explore the potential mechanism of Zanthoxylum bungeanum in the treatment of diabetes mellitus (DM) using network pharmacology. Methods. The DrugBank database and TCMSP platform were used to search for the main chemical components and their targets of Zanthoxylum bungeanum, and the genes related to diabetes mellitus were obtained from the genecards database. Import the data into the Venny 2.1.0 platform for intersection analysis to obtain the Zanthoxylum bungeanum-DM-gene dataset. The protein-protein interaction (PPI) analysis of Zanthoxylum bungeanum-DM gene was performed using the String data platform, and the visualization and network topology analysis were performed using Cytoscape 3.8.2. The KEGG pathway enrichment and biological process of GO enrichment analysis were carried out using the David platform. The active ingredients and key targets of Zanthoxylum bungeanum were molecularly docked to verify their biological activities by using Discovery Studio 2019 software. Zanthoxylum bungeanum was extracted and isolated by ethanol and dichloromethane. HepG2 cells were cultured, and cell viability assay was utilized to choose the suitable concentration of Zanthoxylum bungeanum extract (ZBE). The western blot assay was used for measuring the expression of AKT1, IL6, HSP90AA1, FOS, and JUN proteins in HepG2 cells. Results. A total of 5 main compounds, 339 targets, and 16656 disease genes were obtained and retrieved, respectively. A total of 187 common genes were screened, and 20 core genes were finally obtained after further screening. The antidiabetic active ingredients of Zanthoxylum bungeanum are kokusaginin, skimmianin, diosmetin, beta-sitosterol, and quercetin, respectively. The main targets for its antidiabetic effect are AKT1, IL6, HSP90AA1, FOS, and JUN, respectively. GO enrichment analysis revealed that the biological process of Zanthoxylum bungeanum and DM is related to a positive regulation of gene expression, positive regulation of transcription, positive regulation of transcription from RNA polymerase II promoter, response to drug, positive regulation of apoptotic process, and positive regulation of cell proliferation, etc. KEGG enrichment analysis revealed that common biological pathways mainly including the phospholipase D signaling pathway, MAPK signaling pathway, beta-alanine metabolism, estrogen signaling pathway, PPAR signaling pathway, and TNF signaling pathway. Molecular docking results showed that AKT1 with beta-sitosterol and quercetin, IL-6 with diosmetin and skimmianin, HSP90AA1 with diosmetin and quercetin, FOS with beta-sitosterol and quercetin, and JUN with beta-sitosterol and diosmetin have relatively strong binding activity, respectively. Experiment verification results showed that DM could be significantly improved by downregulating the expression of AKT1, IL6, HSP90AA1, FOS, and JUN proteins after being treated at concentrations of 20 μmol/L and 40 μmol/L of ZBE. Conclusion. The active components of Zanthoxylum bungeanum mainly including kokusaginin, skimmianin, diosmetin, beta-sitosterol, and quercetin. The therapeutic effect of Zanthoxylum bungeanum on DM may be achieved by downregulating core target genes including AKT1, IL6, HSP90AA1, FOS, and JUN, respectively. Zanthoxylum bungeanum is an effective drug in treatment of DM related to the above targets. [ABSTRACT FROM AUTHOR]

Huoxiang Zhengqi Oral Liquid (HZOL) is a classic Chinese patent medicine used in China for more than 1,000 years in treating gastrointestinal and respiratory diseases. Clinically applied HZOL in early respiratory disease stages can reduce the proportion of lung infection patients that progress to severe acute lung injury (ALI). However, few pharmacological studies evaluated its level of protection against ALI. We explored mechanisms of HZOL against ALI by employing network pharmacology, molecular docking, and rat experiments. Firstly, network pharmacology prediction and published biological evaluation of active ingredients of HZOL suggested that HZOL exerted the protective effect in treating ALI mainly in the areas of regulation of cell adhesion, immune response, and inflammatory response and closely related to the NF-κB pathway. Secondly, molecular docking results demonstrated that imperatorin and isoimperatorin combined well with targets in the NF-κB pathway. Finally, ALI rats induced by lipopolysaccharides (LPS) were used to validate prediction after pretreatment with HZOL for 2 weeks. Results confirmed that lung and colon injury occurred in ALI rats. Furthermore, HZOL exerts anti-inflammatory effects on LPS-induced ALI and gut injury by repairing lung and colon pathology, reducing and alleviating pulmonary edema, inhibiting abnormal enhancement of thymus and spleen index, modulating hematologic indices, and increasing levels of total short-chain fatty acids (SCFAs) in the cecum. Additionally, abnormal accumulation of inflammatory cytokines IL-6, IL-1β, TNF-α, and IFN-γ in serum and bronchoalveolar lavage fluid was significantly reduced after pretreating with HZOL. Furthermore, HZOL downregulated the expression of TLR4, CD14, and MyD88 and phosphorylation of NF-κB p65 in lung tissue. Altogether, HZOL was found to exert an anti-inflammatory effect regulation by increasing levels of SCFAs, inhibiting the accumulation of inflammatory cytokines, and attenuating the activation of the TLR4/NF-κB p65 pathway. Our study provided experimental evidences for the application of HZOL in preventing and treating ALI. [ABSTRACT FROM AUTHOR]

The plant B3 gene superfamily contains a large number of transcription factors playing a vital role in both vegetative growth and reproductive development in plants. Although several B3 genes have been well studied, molecular functions of the B3 genes in olive are largely unknown. In our study, a total of 200 B3 genes were identified in olive genome based on RNA-seq and comparative genomic analyses and further classified into five groups (i.e., REM, RAV, LAV, HSI, and ARF) based on phylogenetic analysis. Results of gene structure and motif composition analyses revealed diversified functions among these five groups of B3 genes. Results of genomic duplication and syntenic analyses indicated the gene expansion in the B3 genes. Results of gene expression based on both transcriptomics and relative expression revealed the tissue-biased expression patterns in B3 genes. The results of the comparative expression analysis of B3 genes between two olive cultivars with high and low oil contents identified several potential REM genes which may be involved in oil biosynthesis in olive. Based on the comprehensive characterization of the molecular structures and functions of B3 genes in olive genome, our study provided novel insights into the potential roles of B3 transcription factors in oil biosynthesis in olive and lays the groundwork for the functional explorations into this research field. [ABSTRACT FROM AUTHOR]

Genetic variations (mutation, crossing over, and recombination) act as a source for the gradual alternation in phenotype along a geographic transect where the environment changes. Posttranslational modifications (PTMs) predicted modifications successfully in different and the same species of living organisms. Protein diversity of living organisms is predicted by PTMs. Environmental stresses change nucleotides to produce alternations in protein structures, and these alternations have been examined through bioinformatics tools. The goal of the current study is to search the diversity of genes and posttranslational modifications of protease serine endopeptidase in various strains of Sordaria fimicola. The S. fimicola's genomic DNA was utilized to magnify the protease serine endopeptidase (SP2) gene; the size of the product was 700 and 1400 base pairs. Neurospora crassa was taken as the reference strain for studying the multiple sequence alignment of the nucleotide sequence. Six polymorphic sites of six strains of S. fimicola with respect to N. crassa were under observation. Different bioinformatics tools, i.e., NetPhos 3.1, NetNES 1.1 Server, YinOYang1.2, and Mod Pred, to search phosphorylation sites, acetylation, nuclear export signals, O-glycosylation, and methylation, respectively, were used to predict PTMs. The findings of the current study were 35 phosphorylation sites on the residues of serine for protease SP2 in SFS and NFS strains of S. fimicola and N. crassa. The current study supported us to get the reality of genes involved in protease production in experimental fungi. Our study examined the genetic biodiversity in six strains of S. fimicola which were caused by stressful environments, and these variations are a strong reason for evolution. In this manuscript, we predicted posttranslational modifications of protease serine endopeptidase in S. fimicola obtained from different sites, for the first time, to see the effect of environmental stress on nucleotides, amino acids, and proteases and to study PTMs by using various bioinformatics tools. This research confirmed the genetic biodiversity and PTMs in six strains of S. fimicola, and the designed primers also provided strong evidence for the presence of protease serine endopeptidase in each strain of S. fimicola. [ABSTRACT FROM AUTHOR]

The present study was designed to evaluate the dynamic expression of lncRNA NORAD in fracture healing of patients with brittle fractures and explore the function and mechanism of NORAD in regulating osteoblastic proliferation, differentiation, and apoptosis. The expression level of NORAD was detected by quantitative real-time PCR. The proliferation, differentiation, and apoptosis of osteoblasts were analyzed by MTT assay, ELISA, and flow cytometry. Luciferase report analysis was used to confirm the interaction between NORAD and its target ceRNA miR-26a. This study showed no significant differences in serum NORAD expression on the 7th day during fracture healing in patients, but increased expression of NORAD was certified on the 14, 21, and 28 days after fixation. Overexpression of NORAD promoted the proliferation and differentiation of osteoblasts and suppressed the apoptosis of osteoblasts. miR-26a proved to be the target gene of NORAD and was inhibited by overexpression of NORAD in osteoblasts. The enhanced expression of miR-26a was negatively linked to the lessened expression of NORAD. NORAD could accelerate the proliferation and differentiation of osteoblasts and inhibit apoptosis, thereby promoting fracture healing. [ABSTRACT FROM AUTHOR]

Laryngeal squamous cell cancer (LSCC) is a common malignant tumor with a high degree of malignancy, and its etiology remains unclear. Therefore, screening potential biomarkers is necessary to facilitate the treatment and diagnosis of LSCC. Robust rank aggregation (RRA) analysis was used to integrate two gene expression datasets of LSCC patients from the Gene Expression Omnibus (GEO) database and identify differentially expressed genes (DEGs) between LSCC and nonneoplastic tissues. A gene coexpression network was constructed using weighted gene correlation network analysis (WGCNA) to explore potential associations between the module genes and clinical features of LSCC. Combining differential gene expression analysis and survival analysis, we screened potential hub genes, including CDK1, SPC24, HOXB7, and SELENBP1. Subsequently, western blotting and immunohistochemistry were used to test the protein levels in clinical specimens to verify our findings. Finally, four candidate diagnostic and prognostic biomarkers (CDK1, SPC24, HOXB7, and SELENBP1) were identified. We propose, for the first time, that SPC24 is a gene that may associate with LSCC malignancy and is a novel therapeutic target. These findings may provide important mechanistic insight of LSCC. [ABSTRACT FROM AUTHOR]

In this study, the expression of Cripto-1 and the role of macrophage polarization in immune response after allogeneic transfusion were analyzed by constructing a mouse model of allogeneic transfusion. In order to analyze the effects of miR-449a on the PI3K/AKT/NF-κB signaling pathway and the expression of downstream related regulatory factors under normal and abnormal conditions, we adopt in vitro and in vivo experiments separately. The molecular mechanism of PI3K/AKT/NF-κB signaling pathway was analyzed by blocking or activating gene expression and western blotting. Experiment in vitro has confirmed that inhibition of miR-449a increased the protein expression of Cripto-1. In vivo experiments confirmed that allogeneic transfusion reduced the expression of Cripto-1, which further inhibited NF-κB signaling pathway through AKT/PI3K phosphorylation, regulated macrophage polarization, inhibited M1 polarization of macrophages, promoted M2 polarization, and thus affected immune response of the body. [ABSTRACT FROM AUTHOR]

Identification of rhythmic gene expression from metabolic cycles to circadian rhythms is crucial for understanding the gene regulatory networks and functions of these biological processes. Recently, two algorithms, JTK_CYCLE and ARSER, have been developed to estimate periodicity of rhythmic gene expression. JTK_CYCLE performs well for long or less noisy time series, while ARSER performs well for detecting a single rhythmic category. However, observing gene expression at high temporal resolution is not always feasible, and many scientists are interested in exploring both ultradian and circadian rhythmic categories simultaneously. In this paper, a new algorithm, named autoregressive Bayesian spectral regression (ABSR), is proposed. It estimates the period of time-course experimental data and classifies gene expression profiles into multiple rhythmic categories simultaneously. Through the simulation studies, it is shown that ABSR substantially improves the accuracy of periodicity estimation and clustering of rhythmic categories as compared to JTK_CYCLE and ARSER for the data with low temporal resolution. Moreover, ABSR is insensitive to rhythmic patterns. This new scheme is applied to existing time-course mouse liver data to estimate period of rhythms and classify the genes into ultradian, circadian, and arrhythmic categories. It is observed that 49.2% of the circadian profiles detected by JTK_CYCLE with 1-hour resolution are also detected by ABSR with only 4-hour resolution. [ABSTRACT FROM AUTHOR]

More and more studies have shown that many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional biological pathway or network and are highly correlated. Differential coexpression analysis, as a more comprehensive technique to the differential expression analysis, was raised to research gene regulatory networks and biological pathways of phenotypic changes through measuring gene correlation changes between disease and normal conditions. In this paper, we propose a gene differential coexpression analysis algorithm in the level of gene sets and apply the algorithm to a publicly available type 2 diabetes (T2D) expression dataset. Firstly, we calculate coexpression biweight midcorrelation coefficients between all gene pairs. Then, we select informative correlation pairs using the “differential coexpression threshold” strategy. Finally, we identify the differential coexpression gene modules using maximum clique concept and k-clique algorithm. We apply the proposed differential coexpression analysis method on simulated data and T2D data. Two differential coexpression gene modules about T2D were detected, which should be useful for exploring the biological function of the related genes. [ABSTRACT FROM AUTHOR]

Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L1/2 penalized solver to penalize network-constrained logistic regression model called an enhanced L1/2 net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected. [ABSTRACT FROM AUTHOR]

α-Amylase as an important industrial enzyme has been widely used in starch processing, detergent, and paper industries. To improve expression efficiency of recombinant α-amylase from Bacillus licheniformis (B. licheniformis), the α-amylase gene from B. licheniformis was optimized according to the codon usage of Pichia pastoris (P. pastoris) and expressed in P. pastoris. Totally, the codons encoding 305 amino acids were optimized in which a total of 328 nucleotides were changed and the G+C content was increased from 47.6 to 49.2%. The recombinants were cultured in 96-deep-well microplates and screened by a new plate assay method. Compared with the wild-type gene, the optimized gene is expressed at a significantly higher level in P. pastoris after methanol induction for 168 h in 5- and 50-L bioreactor with the maximum activity of 8100 and 11000 U/mL, which was 2.31- and 2.62-fold higher than that by wild-type gene. The improved expression level makes the enzyme a good candidate for α-amylase production in industrial use. [ABSTRACT FROM AUTHOR]

Studies over the past decade have shown a significant role of synonymous mutations in posttranscriptional regulation of gene expression, which is particularly associated with messenger RNA (mRNA) secondary structure alterations. Most studies focused on prokaryote genomes and the nuclear genomes of eukaryotes while little is known about the regulation of mitochondrial DNA (mtDNA) gene expression. This paper reveals signs of selection in synonymous sites of the mitochondrial cytochrome b gene (Cytb) of Baikal oilfish or golomyankas (Comephoridae) directed towards altering the secondary structure of the mRNA and probably altering the character of mtDNA gene expression. Our findings are based on comparisons of intraspecific genetic variation patterns of small golomyanka (Comephorus dybowski) and two genetic groups of big golomyanka (Comephorus dybowskii). Two approaches were used: (i) analysis of the distribution of synonymous mutations between weak-AT (W) and strong-GC (S) nucleotides within species and groups in accordance with mutation directions from central to peripheral haplotypes and (ii) approaches based on the predicted mRNA secondary structure. [ABSTRACT FROM AUTHOR]

Interferons, as the first line of defense against the viral infection, play an important role in innate immune responses. Type III interferon (IFN-λ) was a newly identified member of IFN family, which plays IFN-like antiviral activity. Towards a better understanding of the type III interferon system in birds, type III interferon lambda receptor (IFNLR1) was first identified in the Chinese goose. In this paper, we had cloned 1952 bp for goose IFNLR1 (goIFNLR1), including an ORF of 1539 bp, encoding a 512-amino acid protein with a 20 aa predict signal peptide at its N terminal and a 23 aa transmembrane region. The predicted amino acid sequence of goIFNLR1 has 90%, 73%, and 34% identity with duck IFNLR1 (predicted sequence), chicken IFNLR1, and human IFNLR1, respectively. And the age-related tissue distribution of goIFNLR1 was identified by Real Time quantitative PCR (RT-qPCR), we found that the goIFNLR1 has a mainly expression in epithelium-rich tissues similar to other species’, such as small intestinal, lung, liver, and stomach. Moreover, a relatively high expression of goIFNLR1 was also observed in the secondary immune tissues (harderian gland and cecal tonsil). The identification and tissue distribution of goIFNLR1 will facilitate further study of the role of IFN-λ in goose antiviral defense. [ABSTRACT FROM AUTHOR]

Oral lichen planus (OLP) is a chronic inflammatory disease that affects oral mucosa, some of which may finally develop into oral squamous cell carcinoma. Therefore, pinpointing the molecular mechanisms underlying the pathogenesis of OLP is important to develop efficient treatments for OLP. Recently, the accumulation of the large amount of omics data, especially transcriptome data, provides opportunities to investigate OLPs from a systematic perspective. In this paper, assuming that the OLP associated genes have functional relationships, we present a new approach to identify OLP related gene modules from gene regulatory networks. In particular, we find that the gene modules regulated by both transcription factors (TFs) and microRNAs (miRNAs) play important roles in the pathogenesis of OLP and many genes in the modules have been reported to be related to OLP in the literature. [ABSTRACT FROM AUTHOR]

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. [ABSTRACT FROM AUTHOR]

In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a progressively and fatally neurodegenerative disorder and leads to irreversibly cognitive and memorial damage in different brain regions. The identification and analysis of the dysregulated pathways and subnetworks among affected brain regions will provide deep insights for the pathogenetic mechanism of AD. In this paper, commonly and specifically significant subnetworks were identified from six AD brain regions. Protein-protein interaction (PPI) data were integrated to add molecular biological information to construct the functional modules of six AD brain regions by Heinz algorithm. Then, the simulated annealing algorithm based on edge weight is applied to predicting and optimizing the maximal scoring networks for common and specific genes, respectively, which can remove the weak interactions and add the prediction of strong interactions to increase the accuracy of the networks. The identified common subnetworks showed that inflammation of the brain nerves is one of the critical factors of AD and calcium imbalance may be a link among several causative factors in AD pathogenesis. In addition, the extracted specific subnetworks for each brain region revealed many biologically functional mechanisms to understand AD pathogenesis. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a common tumor-associated lethal disease, and invasiveness and metastasis are primary challenges in its clinical treatment. Hypoxia microenvironment cannot be ignored in the process of metastasis. Hypoxia inducible factor-1α (HIF-1α) is the core component of the hypoxia signaling pathway. The aim of this study was to identify potential hub genes and signaling pathways associated with HIF-1α. We explored the invasiveness- and metastasis-associated phenotype of GC via bioinformatics analysis and molecular studies. Differentially expressed genes (DEGs) were identified in GC cells and HIF-1α-knockdown GC cells. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and a protein-protein interaction (PPI) network was constructed. Hub genes were identified via centrality analysis and Molecular Complex Detection (MCODE) module analysis. The findings suggested that prolyl 4-hydroxylase beta polypeptide (P4HB) has strong associations with HIF-1α. Further, we observed that HIF-1α and P4HB were upregulated in SGC-7901 and BGC-823 cells. In addition, inhibition of HIF-1α expression reduced invasion and metastasis in GC cells; this effect was partially reversed by P4HB overexpression. Our results confirm that P4HB plays a significant role in the regulatory network of HIF-1α. Therefore, HIF-1α and P4HB may be considered potential biomarkers of GC. [ABSTRACT FROM AUTHOR]