Discovery Software Research Articles

Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis.

Huntington's disease (HD) is an inherited, progressive neurodegenerative disease caused by a CAG expansion in the huntingtin (HTT) gene; various dysfunctions of biological processes in HD have been proposed. However, at present the exact pathogenesis of HD is not fully understood. The present study aimed to explore the pathogenesis of HD using a computational bioinformatics analysis of gene expression. GSE11358 was downloaded from the Gene Expression Omnibus andthe differentially expressed genes (DEGs) in the mutant HTT knock-in cell model STHdhQ111/Q111 were predicted. DEGs between the HD and control samples were screened using the limma package in R. Functional and pathway enrichment analyses were conducted using the database for annotation, visualization and integrated discovery software. A protein-protein interaction (PPI) network was established by the search tool for the retrieval of interacting genes and visualized by Cytoscape. Module analysis of the PPI network was performed utilizing MCODE. A total of 471 DEGs were identified, including ribonuclease A family member 4 (RNASE4). In addition, 41 significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways, as well as several significant Gene Ontology terms (including cytokine-cytokine receptor interaction and cytosolic DNA-sensing) were identified. A total of 18 significant modules were identified from the PPI network. Furthermore, a novel transcriptional regulatory relationship was identified, namely signal transducer and activator of transcription 3 (STAT3), which is regulated by miRNA-124 in HD. In conclusion, deregulation of 18 critical genes may contribute to the occurrence of HD. RNASE4, STAT3, and miRNA-124 may have a regulatory association with the pathological mechanisms in HD.

Practice Patterns of Dentist Anesthesiologists in North America.

This study provides trends in the discipline of dental anesthesiology. A questionnaire-based survey was sent to 338 members of the American Society of Dentist Anesthesiologists to evaluate practice patterns. One focus of the study was modality of sedation/anesthesia used for dentistry in North America. Age, gender, years in practice, and geographic region of practice were also obtained. Data gathered from the returned questionnaires were entered into an Excel spreadsheet and then imported into JMP Statistical Discovery Software (v12.2 Pro) for descriptive analysis. A total of 112 surveys were completed electronically and 102 surveys were returned via post, for a total response rate of 63.3% ( N = 214). Data from this survey suggested a wide variation of therapeutic practices among dentist anesthesiologists in North America. Of the surveyed dentist anesthesiologists, 58.7% (SE = 4.2%) practice as mobile providers, 32.2% (SE = 3.1%) provide care in an academic environment, and 27.7% (SE = 2.8%) function as operator/anesthetists. The majority of anesthesia is provided for pediatric dentistry (47.0%, SE = 4.2%), oral and maxillofacial surgery (18.5%, SE = 3.9%), and special needs (16.7%, SE = 3.6%). Open-airway (58.7%, SE = 5.5%) sedation/anesthesia was the preferred modality of delivery, compared with the use of advanced airway (41.3%, SE = 4.6%). The demographics show diverse practice patterns of dentist anesthesiologists in multiple regions of the continent. Despite concerns regarding specialty recognition, reimbursement difficulties, and competition from alternative anesthesia providers, the overall perceptions of dentist anesthesiologists and the future of the field seem largely favorable.

Bioinformatic analysis of gene expression profiling of intracranial aneurysm.

Intracranial aneurysm (IA) is a severe clinical condition of primary concern and currently, there is no effective therapeutic reagent. The present study aimed to investigate the molecular mechanism of IA via bioinformatic analysis. Various gene expression profiles (GSE26969) were downloaded from the Gene Expression Omnibus database, including 3 IA and 3 normal superficial temporal artery samples. Firstly, the limma package in R language was used to identify differentially expressed genes (DEGs; P-value <0.01 and |log2 FC|≥1). Secondly, the database for annotation, visualization and integrated discovery software was utilized to perform pathway and functional enrichment analyses (false discovery rate ≤0.05). Finally, protein-protein interaction (PPI) network and sub-network clustering analyses were performed using the biomolecular interaction network database and ClusterONE software, respectively. Following this, a transcription factor regulatory network was identified from the PPI network. A total of 1,124 DEGs were identified, of which 989 were upregulated and 135 downregulated. Pathway and functional enrichment analyses revealed that the DEGs primarily participated in RNA splicing, functioning of the spliceosome, RNA processing and the mRNA metabolic process. Following PPI network analysis, 1 hepatocyte nuclear factor (HNF) 4A (transcription factor)-centered regulatory network and 5 DEG-centered sub-networks were identified. On analysis of the transcription factor regulatory network, 6 transcription factors (HNF6, HNF4A, E2F4, YY1, H4 and H31T) and a regulatory pathway (HNF6-HNF4-E2F4) were identified. The results of the present study suggest that activating transcription factor-5, Jun proto-oncogene, activator protein-1 transcription factor subunit, HNF6, HNF4 and E2F4 may participate in IA progression via vascular smooth muscle cell apoptosis, inflammation, vessel wall remodeling and damage and the tumor necrosis factor-β signaling pathway. However, further experimental studies are required to validate these predictions.

Identification and analysis of key genes in osteosarcoma using bioinformatics

Osteosarcoma (OS) is an invasive malignant neoplasm of the bones. The present study identified and analyzed key genes associated with OS. Expression profiling of the dataset GSE49003, which included 6 metastatic and 6 non-metastatic OS cell lines and was obtained from the Gene Expression Omnibus, was performed. Following data preprocessing, the differentially expressed genes (DEGs) were selected using the limma package in R. Subsequently, bidirectional hierarchical clustering using the pheatmap package in R and an unpaired Students' t-test was performed for the DEGs. Based on the Search Tool for the Retrieval of Interacting Genes database and Cytoscape software, a protein-protein interaction (PPI) network for the DEGs was constructed. Using Database for Annotation, Visualization and Integrated Discovery software and the Kyoto Encyclopedia of Genes and Genomes Orthology Based Annotation System server, functional and pathway enrichment analyses were performed for the DEGs corresponding to the proteins of the network. In addition, the transcription factors (TFs) and CpG islands of the gene promoter were searched for using the TRANSFAC database and CpG Island Searcher software, respectively. A total of 323 DEGs were identified between the metastatic and non-metastatic samples. In the PPI network, upregulated epidermal growth factor receptor (EGFR) exhibits a high degree and was therefore highly interconnected with other proteins. Enrichment analysis revealed that EGFR was enriched in cytoskeleton organization, organic substance response and the signaling pathway of focal adhesion. The TFs early growth response 1, nuclear factor-κB complex subunits, peroxisome proliferator activated receptor α, signal transducer and activator of transcription 3 and MYC proto-oncogene were identified in the EGFR promoter region. Furthermore, multiple CpG islands, starting from the 400 bp of the EGFR promoter sequence, were predicted. Methylated modification of the CpG islands in the EGFR promoter may help to regulate EGFR expression. The TFs identified in the EGFR promoter may function in the progression of OS.

A survey of software tools for microRNA discovery and characterization using RNA-seq.

Since the small RNA-sequencing (sRNA-seq) technology became available, it allowed the discovery of thousands new microRNAs (miRNAs) in humans and many other species, providing new data on these small RNAs (sRNAs) of high biological and translational relevance. MiRNA discovery has not yet reached saturation, even in the most studied model organisms, and many researchers are using sRNA-seq in studies with different aims in biomedicine, fundamental research and in applied animal sciences. We review several miRNA discovery and characterization software tools that implement different strategies, providing a useful guide for researchers to select the programs best suiting their study objectives and data. After a brief introduction on miRNA biogenesis, function and characteristics, useful to understand the biological background considered by the algorithms, we survey the current state of miRNA discovery bioinformatics discussing 26 different sRNA-seq-based miRNA prediction software and toolkits released in the past 6 years, including 15 methods specific for miRNA prediction and 11 more general-purpose software suites for sRNA-seq data analysis. We highlight the main features of mature miRNAs and miRNA precursors considered by the methods categorizing them according to prediction strategy and implementation. In addition, we describe a typical miRNA prediction and analysis workflow by delineating the objectives, potentialities and main steps of sRNA-seq data analysis projects, from preparatory data processing to miRNA prediction, quantification and diverse downstream analyses. Finally, we outline the caveats affecting sRNA-seq-based prediction tools, and we indicate the possibilities offered by data set pooling and by integration with other types of high-throughput sequencing data.

Identification of key genes, transcription factors and microRNAs involved in intracranial aneurysm.

Intracranial aneurysm (IA) is a devastating disease, the pathogenesis of which remains to be elucidated. The present study aimed to determine the molecular mechanism of IA and to identify potential therapeutic targets using bioinformatics analysis. The GSE54083 dataset, which includes data from patients with ruptured IA and superficial temporal artery controls, was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) were identified in the ruptured IA samples using the limma package in R. Subsequently, the Database for Annotation, Visualization and Integrated Discovery software was used to perform function and pathway enrichment analyses and the Search Tool for the Retrieval of Interacting Genes database was used to construct the protein-protein interaction (PPI) network. Then, microRNA (miRNA) target and transcription factor (TF) target pairs were identified using the miR2Disease, MiRwalk2, ITFP and TRANSFAC databases. Finally, an integrated network of TF-target-miRNAs was constructed using Cytoscape. A total of 402 upregulated DEGs and 375 downregulated DEGs were identified from the ruptured IA samples compared with the superficial temporal artery samples. The majority of the upregulated DEGs were significantly enriched in the immune system development category, including CD40 ligand (CD40LG) and CD40 and the downregulated DEGs, such as striatin (STRN), were enriched in neuron projection development. In addition, nitric oxide synthase 1 (NOS1), a target of miRNA-125b, and myosin heavy chain 11 (MYH11), a target of minichromosome maintenance complex component 4 (MCM4), had higher degree scores in the integrated network. These findings suggest that CD40, CD40LG, NOS1, STRN, MCM4, MYH11 and miR-125b may be potential therapeutic targets for the treatment of IA.

Effect of Er-xian decoction on femur proteomics in ovariectomized osteoporosis rats

This experiment was mainly aimed to investigate the effect of Er-xian decoction on osteoporosis and the femur proteomics in ovariectomized rats with osteoporosis. The female SD rats were randomly divided into sham operation group, model group, Alendronate group (1 mg•kg⁻¹), Er-xian decoction group (in dose of 8 g•kg⁻¹) according to their weight. The rats in sham operation group and model group were gavaged with normal saline; the rats in Alendronate group were gavaged with the Alendronate at the dose of 1 mg•kg⁻¹ and the rats in Er-xian decoction group were gavaged with Er-xian decoction at the dose of 8 g•kg⁻¹, once a day for continuous 90 days. Then the femoral bone mineral density (BMD) was detected. The femoral bone proteins were detected by NanoLC-LTQ-Orbitrap system, identified by Protein Discovery software, and the intensity of differentially expressed proteins were quantitated by SIEVE software. The results showed that Er-xian decoction could significantly improve femoral BMD in ovariectomized rats. As compared with model group, 41 differentially expressed proteins whose variation trend was consistent with the sham operation group, were found in Er-xian decoction group, mainly including biological oxidation related protein, signal transduction pathway related protein, proteins involved in aliphatic acid metabolism, cytoskeleton related protein, proteins involved in energy metabolism, and proteins involved in glucose metabolism etc. The osteoporosis could be prevented and cured by Er-xian decoction. The differentially expressed proteins such as carbonic anhydrase 2 and integrin β1 may be the action targets for Er-xian decoction.

Expression of genes associated with BMP signaling pathway in porcine oocytes before and after IVM \u2013 a microarray approach

BackgroundThe full maturational capability of mammalian oocytes is accompanied by nuclear and cytoplasmic modifications, which are associated with proliferation and differentiation of surrounding cumulus cells. These events are regulated on molecular level by the expression of target genes involved in signal transduction pathways crucial for folliculogenesis and oogenesis. Transforming growth factor beta signaling includes several molecules that are involved in the regulation of oogenesis and embryo growth, including bone morphogenetic protein (BMP). However, the BMP-related gene expression profile in oocytes at different maturational stages requires further investigation.MethodsOocytes were isolated from pubertal crossbred Landrace gilts follicles, selected with a use of BCB staining test and analyzed before and after in vitro maturation. Gene expression profiles were examined using an Affymetrix microarray approach and validated by RT-qPCR. Database for Annotation, Visualization, and Integrated Discovery (DAVID) software was used for the extraction of the genes belonging to a BMP-signaling pathway ontology group.ResultsThe assay revealed 12,258 different transcripts in porcine oocytes, among which 379 genes were down-regulated and 40 were up-regulated. The DAVID database indicated a “BMP signaling pathway” ontology group, which was significantly regulated in both groups of oocytes. We discovered five up-regulated genes in oocytes before versus after in vitro maturation (IVM): chordin-like 1 (CHRDL1), follistatin (FST), transforming growth factor-beta receptor-type III (TGFβR3), decapentaplegic homolog 4 (SMAD4), and inhibitor of DNA binding 1 (ID1).ConclusionsIncreased expression of CHRDL1, FST, TGFβR3, SMAD4, and ID1 transcripts before IVM suggested a subordinate role of the BMP signaling pathway in porcine oocyte maturational competence. Conversely, it is postulated that these genes are involved in early stages of folliculogenesis and oogenesis regulation in pigs, since in oocytes before IVM increased expression was observed.

Domain-aware Mashup service clustering based on LDA topic model from multiple data sources

ContextMashup is emerging as a promising software development method for allowing software developers to compose existing Web APIs to create new or value-added composite Web services. However, the rapid growth in the number of available Mashup services makes it difficult for software developers to select a suitable Mashup service to satisfy their requirements. Even though clustering based Mashup discovery technique shows a promise of improving the quality of Mashup service discovery, Mashup service clustering with high accuracy and good efficiency is still a challenge problem. ObjectiveThis paper proposes a novel domain-aware Mashup service clustering method with high accuracy and good efficiency by exploiting LDA topic model built from multiple data sources, to improve the quality of Mashup service discovery. MethodThe proposed method firstly designs a domain-aware Mashup service feature selection and reduction process by refining characterization of their domains to consolidate domain relevance. Then, it presents an extended LDA topic model built from multiple data sources (include Mashup description text, Web APIs and tags) to infer topic probability distribution of Mashup services, which serves as a basis of Mashup service similarity computation. Finally, K-means and Agnes algorithm are used to perform Mashup service clustering in terms of their similarities. ResultsCompared with other existing Mashup service clustering methods, experimental results show that the proposed method achieves a significant improvement in terms of precision, recall, F-measure, purity and entropy. ConclusionThe results of the proposed method help software developers to improve the quality of Mashup service discovery and Mashup-based software development. In the future, there will be a need to extend the method by considering heterogeneous network information among Mashup, Web APIs, tags, users, and applying it to Mashup discovery for software developers.

Identification of critical genes in nucleus pulposus cells isolated from degenerated intervertebral discs using bioinformatics analysis

Intervertebral disc (IVD) degeneration is a pathological process, which may lead to lower back pain. The present study aimed to investigate the pathogenesis of IVD degeneration. GSE42611 was downloaded from Gene Expression Omnibus, including 4 nucleus pulposus samples isolated from degenerated IVDs and 4 nucleus pulposus samples separated from normal IVDs. The differentially expressed genes (DEGs) between the degenerated and normal samples were screened using the limma package in R. Functional and pathway enrichment analyses were conducted separately for the upregulated and downregulated genes, using Database for Annotation, Visualization and Integrated Discovery software. In addition, protein-protein interaction (PPI) networks were constructed using the Search Tool for the Retrieval of Interacting Genes database and Cytoscape software. Finally, module analyses were conducted for the PPI networks using the MCODE plug-in in Cytoscape. A total of 558 DEGs were identified in the degenerated nucleus pulposus cells: 253 upregulated and 305 downregulated. Pathway enrichment analysis revealed that downregulated thrombospondin 1 (THBS1) was enriched in extracellular matrix-receptor interaction. Interleukin (IL)-6 in the PPI network for the upregulated genes and vascular endothelial growth factor A (VEGFA) in the PPI network for the downregulated genes had higher degrees. Additionally, four modules (µM1, µM2, µM3 and µM4) were identified from the PPI network for the upregulated genes. Four modules (dM1, dM2, dM3 and dM4) were identified from the PPI network for the downregulated genes. In the dM2 module, collagen genes and integrin subunit α4 (ITGA4) may interact with each other. Additionally, functional enrichment indicated that collagen genes were enriched in extracellular matrix organization. In conclusion, IL-6, VEGFA, THBS1, ITGA4 and collagen genes may contribute to the progression of IVD degeneration. These results suggested that the manipulation of these genes and their products may have potential as a novel therapeutic strategy for the treatment of patients with IVD.

DNA Microarray Analysis of Gene Expression in Eutopic Endometrium from Patients with Endometriosis

Pathogenesis of the endometriosis is complex and the etiology is still unclear. The objective of this study was to examine that endometrial gene expression in late secretory phase endometrium differs between patients with and without endometriosis. Five patients with proven advanced-stage endometriosis and 5 controls underwent endometrial biopsy in the late secretory phase. Analysis of eutopic endometrial gene expression was performed using Affymetrix gene arrays and differentially expressed genes were assigned to gene ontology groups based on overrepresented analysis using Database for Annotation, Visualization, and Integrated Discovery software. Four hundred sixty two genes were identified as up-regulated such as matrix metalloproteinase 10, cytochrome P450 family 24 subfamily A polypeptide 1, matrix metalloproteinase 3, chemokine (C-C motif) ligand 20, Rho family GTPase 1, interleukin 1-beta, and insulin-like growth factor binding protein 1. Six hundred forty three genes were down-regulated in all endometriotic samples. A lot of genes related with metabolic process, cellular ketone metabolic process and ncRNA metabolic processing were included. Expression patterns of selected five genes were validated by quantitative real time PCR. The results of this analysis support that the eutopic endometrium from patients with advanced-stage endometriosis has distinct gene expression profile from eutopic endometrium of control without endometriosis.

Network Pharmacological Screening of Herbal Monomers that Regulate Apoptosis-Associated Genes in Acute Pancreatitis.

In this study, we screened for differentially expressed genes in acute pancreatitis and the herbal monomers that regulate these genes. Gene expression profile data were downloaded from the Gene Expression Omnibus database (GSE3644). We used the Human Protein Reference Database to determine the protein-protein interaction network and CFinder software (Department of Biological Physics of Eötvös University, Budapest, Hungary) to identify several functional modules. Then, we used Database for Annotation, Visualization and Integrated Discovery software (Frederick, Md) to perform a gene ontology-biological process functional enrichment analysis. Based on a database of herbal monomers and a literature search, we constructed a gene-herbal monomer regulatory network using Cytoscape software (San Diego, Calif), and we analyzed the relationships between apoptosis, genes, and herbal monomers. A total of 1745 differentially expressed genes were identified. Nine modules were identified, and the main function of module 3 was closely related to apoptosis. Within module 3, we selected 13 genes that were closely related to apoptosis for further analysis. In the gene-herbal monomer regulatory network, 18 herbal monomers that regulate multiple target genes were selected as the focus of this study. These herbal monomers regulate multiple target genes to induce apoptosis and may potentially be used as new drugs for acute pancreatitis treatment in the future.

Microarray data analysis to identify crucial genes regulated by CEBPB in human SNB19 glioma cells.

BackgroundGlioma is one of the most common primary malignancies in the brain or spine. The transcription factor (TF) CCAAT/enhancer binding protein beta (CEBPB) is important for maintaining the tumor initiating capacity and invasion ability. To investigate the regulation mechanism of CEBPB in glioma, microarray data GSE47352 was analyzed.MethodsGSE47352 was downloaded from Gene Expression Omnibus, including three samples of SNB19 human glioma cells transduced with non-target control small hairpin RNA (shRNA) lentiviral vectors for 72 h (normal glioma cells) and three samples of SNB19 human glioma cells transduced with CEBPB shRNA lentiviral vectors for 72 h (CEBPB-silenced glioma cells). The differentially expressed genes (DEGs) were screened using limma package and then annotated. Afterwards, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) software was applied to perform enrichment analysis for the DEGs. Furthermore, the protein-protein interaction (PPI) network and transcriptional regulatory network were constructed using Cytoscape software.ResultsTotal 529 DEGs were identified in the normal glioma cells compared with the CEBPB-silenced glioma cells, including 336 up-regulated and 193 down-regulated genes. The significantly enriched pathways included chemokine signaling pathway (which involved CCL2), focal adhesion (which involved THBS1 and THBS2), TGF-beta signaling pathway (which involved THBS1, THBS2, SMAD5, and SMAD6) and chronic myeloid leukemia (which involved TGFBR2 and CCND1). In the PPI network, CCND1 (degree = 29) and CCL2 (degree = 12) were hub nodes. Additionally, CEBPB and TCF12 might function in glioma through targeting others (CEBPB → TCF12, CEBPB → TGFBR2, and TCF12 → TGFBR2).Conclusions CEBPB might act in glioma by regulating CCL2, CCND1, THBS1, THBS2, SMAD5, SMAD6, TGFBR2, and TCF12.

Proteomics research of intervention effect of "Qi enriching" herbs on "Qi deficiency" rats based on technology of NanoLC-LTQ-Orbitrap

Proteomics method, based on NanoLC-LTQ-Orbitrap technology, was applied to explore the biological basis of intervention effect of "Qi enriching" herbs on "Qi deficiency" rats. The "Qi deficiency" rat model was established with caloric restriction combined with excessive swimming. Muscle proteins of vastus lateralis from the blank group, the model group and the ginseng group were detected by NanoLC-LTQ-Orbitrap system. The data were imported into Protein Discovery software to identify the proteins and all the raw datum were analyzed by SIEVE software. Compared with model group, 26 significant difference proteins were found in ginseng group, which the variation trend was consistent with the blank group. Through the biological function analysis, the found proteins could be classified into proteins involved in energy metabolism, proteins involved in glucose metabolism, electrolyte balance and material transfer related proteins, inflammation related protein and cytoskeleton protein. The above target proteins and their regulation pathways may be the biological basis which ginseng played a role of tonifying "Qi" of "Qi deficiency" symptom.

Profiling bovine blastocyst microRNAs using deep sequencing.

MicroRNAs (miRNAs) are known to control several reproductive functions, including oocyte maturation, implantation and early embryonic development. Recent advances in deep sequencing have allowed the analysis of all miRNAs of a sample. However, when working with embryos, due to the low RNA content, miRNA profiling is challenging because of the relatively large amount of total RNA required for library preparation protocols. In the present study we compared three different procedures for RNA extraction and prepared libraries using pools of 30 bovine blastocysts. In total, 14 of the 15 most abundantly expressed miRNAs were common to all three procedures. Furthermore, using miRDeep discovery and annotation software (Max Delbrück Center), we identified 1363 miRNA sequences, of which bta-miR-10b and bta-miR-378 were the most abundant. Most of the 179 genes identified as experimentally validated (86.6%) or predicted targets (13.4%) were associated with cancer canonical pathways. We conclude that reliable analysis of bovine blastocyst miRNAs can be achieved using the procedures described herein. The repeatability of the results across different procedures and independent replicates, as well as their consistency with results obtained in other species, support the biological relevance of these miRNAs and of the gene pathways they modulate in early embryogenesis.

Celecoxib exhibits an anti-gastric cancer effect by targeting focal adhesion and leukocyte transendothelial migration-associated genes

Gastric cancer (GC) is a prevalent cancer, which remains incurable, and therefore requires an alternative treatment method. Celecoxib is a nonsteroidal anti-inflammatory drug that targets cyclooxygenase-2, and exhibits anticancer effects. The present study aimed to investigate the anti-GC mechanism of celecoxib using bioinformatics methods. Gene expression datasets GSE56807 (GC tissues and normal gastric tissues) and GSE54657 (celecoxib-treated and non-treated human GC epithelial AGS cells) were downloaded from the Gene Expression Omnibus database. Two groups of differentially expressed genes (DEGs) were identified using limma package in R language. The criterion for GSE56807 was a false discovery rate of <0.05, while that for GSE54657 was P<0.01. Overlapping DEGs from the two datasets were screened out. Subsequently, pathway enrichment analysis was performed using Database for Annotation, Visualization and Integrated Discovery software (P<0.1; gene count ≥2). In addition, the protein-protein interactions (PPIs) among the overlapped DEGs were obtained based on IntAct, Database of Interacting Proteins, Biomolecular Interaction Network Database and Human Protein Reference Database. Finally, a PPI network was visualized using Cytoscape software. A total of 137 overlapped DEGs were obtained, and DEGs with opposite regulation directions in the two datasets were significantly enriched in focal adhesion and leukocyte transendothelial migration. Subsequently, a PPI network of overlapped DEGs was constructed. Comprehensively, a total of 8 key DEGs [cysteine and glycine rich protein 1 (CSRP1), thrombospondin 1 (THBS1), myosin light chain 9 (MYL9), filamin A (FLNA), actinin alpha 1 (ACTN1), vinculin (VCL), laminin subunit gamma 2 (LAMC2) and claudin 1 (CLDN1)] were upregulated in GC tissues and downregulated in celecoxib-treated cells. In conclusion, celecoxib may exhibit anti-GC effects by suppressing the expression of CSRP1, THBS1, MYL9, FLNA, ACTN1, VCL, LAMC2 and CLDN1, and inhibiting leukocyte transendothelial migration and focal adhesion. However, relevant experiments are required to confirm the conclusion of the present study.

Identification of genes involved in Epstein-Barr virus-associated nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is the most common cancer originating from the nasopharynx, and can be induced by infection with Epstein-Barr virus (EBV). To study the mechanisms of EBV-associated NPC, a microarray of the GSE12452 dataset was analyzed. GSE12452 was downloaded from Gene Expression Omnibus and consisted of 31 NPC samples and 10 normal healthy nasopharyngeal tissue samples. The differentially-expressed genes (DEGs) were screened using the linear models for microarray data package in R. Using Database for Annotation, Visualization and Integrated Discovery software, potential functions of the DEGs were predicted by Gene Ontology and pathway enrichment analyses. With the information from the Search Tool for the Retrieval of Interacting Genes/Proteins database, the protein-protein interaction (PPI) network was visualized by Cytoscape. Furthermore, modules of the PPI network were searched using ClusterONE in Cytoscape. A total of 951 DEGs were screened in the NPC samples compared with the normal healthy nasopharyngeal tissue samples. Function enrichment indicated that the upregulated genes were associated with the cell cycle, cytoskeleton organization and DNA metabolism. Meanwhile, the downregulated genes were mainly associated with cell differentiation, hormone metabolism, inflammatory response and immune response. PPI networks for the DEGs suggested that upregulated mitotic arrest deficient 2-like 1 (MAD2L1; degree=133), proliferating cell nuclear antigen (PCNA; degree=125) and cyclin B1 (CCNB1; degree=115), and downregulated member A1 of aldehyde dehydrogenase 1 (ALDH1A1; degree=15) may be of great importance as they exhibited higher degrees on interaction. Mucin 1 (MUC1) was a key node of module 4. Overall, the study indicated that MAD2L1, CCNB1, PCNA, ALDH1A1 and MUC1 may have a correlation with EBV-associated NPC.

Application of network construction to estimate functional changes to insulin receptor substrates1 and2 in Huh7 cells following infection with the hepatitisC virus.

Hepatitis C virus (HCV) is closely associated with insulin resistance (IS), acting primarily by interfering with insulin signaling pathways, increasing cytokine-mediated (tumor necrosis factor α, interleukin 6) inflammatory responses and enhancing oxidative stress. In the insulin signaling pathways, the insulin receptor substrate (IRS) is one of the key regulatory factors. The present study constructed gene regulatory sub-networks specific for IRS1 and IRS2 in Huh7 cells and HCV-infected Huh7 (HCV-Huh7) cells using linear programming and a decomposition algorithm, and investigated the possible mechanisms underlying the function of IRS1/2 in HCV-induced IS in Huh7 cells. All data were obtained from GSE20948 of the Gene Expression Omnibus database from the National Center for Biotechnology Information. Genes which were significantly differentially expressed between Huh7 and HCV-Huh7 cells were analyzed using the significance analysis of microarray algorithm. The top 50 genes, including IRS1/2, were used as target genes to determine the gene regulatory networks and next the sub-networks of IRS1 and IRS2 in HCV-Huh7 and Huh7 cells using Gene Regulatory Network Inference Tool, an algorithm based on linear programming and the decomposition process. The IRS1/2 sub-networks were divided into upstream/downstream groups and activation/suppression clusters, and were further analyzed using Molecule Annotation System 3.0 and Database for Annotation, Visualization, and Integrated Discovery software, two online platforms for enrichment and clustering analysis and visualization. The results indicated that in Huh7 cells, the downstream network of IRS2 is more complex than that of IRS1, indicating that the insulin metabolism in Huh7 cells may be primarily mediated by IRS2. In HCV-Huh7 cells, the downstream pathway of IRS2 is blocked, suggesting that this may be the underlying mechanism in HCV infection that leads to insulin resistance. The present findings add a further dimension to the understanding of the pathological mechanisms of HCV infection-associated insulin resistance, and provide novel concepts for insulin resistance and glucose metabolism research.

Confirming an integrated pathology of diabetes and its complications by molecular biomarker-target network analysis.

Despite ongoing research into diabetes and its complications, the underlying molecular associations remain to be elucidated. The systematic identification of molecular interactions in associated diseases may be approached using a network analysis strategy. The biomarker-target interrelated molecules associated with diabetes and its complications were identified via the Comparative Toxicogenomics Database (CTD); the Search Tool for Recurring Instances of Neighboring Genes was utilized for network construction. Functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery software to investigate connections between diabetes and its complications. A total of 142 (including 122 biomarkers, 10 therapeutic targets and 10 overlapping molecules) biomarker-target interrelated molecules associated with diabetes and its complications were identified via the CTD database, and analysis of the network yielded 1,087 biological processes and fifteen Kyoto Encyclopedia of Genes and Genomes pathways with significant P‑values. Various critical aspects of the networks were examined in the present study: a) Intermolecular horizontal and vertical combinations in biomarkers and therapeutic targets associated with diabetes and its complicationb) network topology properties associated with molecular pathological responsec) contribution of key molecules to integrated regulation; and d) crosstalk between multiple pathways. Based on a multi-dimensional analysis, it was concluded that the integrated molecular pathological development of diabetes and its complications does not proceed randomly, which suggests a requirement for integrated, multi-target intervention.

Identification of key genes in glioblastoma-associated stromal cells using bioinformatics analysis.

The aim of the present study was to identify key genes and pathways in glioblastoma-associated stromal cells (GASCs) using bioinformatics. The expression profile of microarray GSE24100 was obtained from the Gene Expression Omnibus database, which included the expression profile of 4 GASC samples and 3 control stromal cell samples. Differentially expressed genes (DEGs) were identified using limma software in R language, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery software. In addition, a protein-protein interaction (PPI) network was constructed. Subsequently, a sub-network was constructed to obtain additional information on genes identified in the PPI network using CFinder software. In total, 502 DEGs were identified in GASCs, including 331 upregulated genes and 171 downregulated genes. Cyclin-dependent kinase 1 (CDK1), cyclin A2, mitotic checkpoint serine/threonine kinase (BUB1), cell division cycle 20 (CDC20), polo-like kinase 1 (PLK1), and transcription factor breast cancer 1, early onset (BRCA1) were identified from the PPI network, and sub-networks revealed these genes as hub genes that were involved in significant pathways, including mitotic, cell cycle and p53 signaling pathways. In conclusion, CDK1, BUB1, CDC20, PLK1 and BRCA1 may be key genes that are involved in significant pathways associated with glioblastoma. This information may lead to the identification of the mechanism of glioblastoma tumorigenesis.