Biological Networks Gene Ontology Tool Research Articles

Identification of Critical Genes Differentiating Stable and Unstable Atherosclerotic Plaques: A Bioinformatic and Computational Analysis.

Identification of biomarkers to distinguish between stable and unstable plaque formation would be very useful to predict plaque vulnerability. We downloaded microarray profiles of gene set enrichment (GSE) accession numbers including GSE71226 and GSE20680 (group A: containing healthy vs stable plaque samples) and GSE62646 and GSE34822 (group B: containing stable vs unstable plaque samples) from Gene expression omnibus (GEO) database. Differentially expressed genes were compared in both data sets of each group. Ten and 12 key genes were screened in groups A and B, respectively. Gene Ontology (GO) enrichment was applied by the plugin "BiNGO" (Biological networks gene ontology tool) of the Cytoscape. The key genes were mostly enriched in the biological process of positive regulation of the cellular process. The protein-protein interaction and co-expression network were analyzed by the STRING (search tool for the retrieval of interacting genes/proteins) and GeneMANIA (gene multiple association network integration algorithm) plugin of Cytoscape, respectively, which showed that Epidermal growth factor (EGF), Heparin-binding EGF like growth factor (HBEGF), and Matrix metalloproteinase 9 (MMP9) were at the core of the network. Further validation of key genes using two datasets showed that Phosphodiesterase 5A (PDE5A) and Protein S (PROS1) were decreased in unstable plaques, while Suppressor of cytokine signaling (SOCS3), HBEGF, and Leukocyte immunoglobulin-like receptor B4 (LILRB4) were increased. The present study used several datasets to identify key genes associated with stable and unstable atherosclerotic plaque.

Study on the Prognostic Values of Dynactin Genes in Low-Grade Glioma.

Objective:This present study aims to investigate the potential prognostic values of dynactin genes (DCTN) for predicting the overall survival (OS) in low-grade glioma (LGG) patients.Methods:The DCTN mRNA expression data were downloaded from The Cancer Genome Atlas database containing 518 patients with LGG. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses for DCTN genes were performed by using Database for Annotation, Visualization, and Integrated Discovery platform, and their enrichment results were verified by using the Biological Networks Gene Ontology tool. Next, the correlations between DCTN genes and LGG were identified by Pearson correlation coefficient analysis. The OS was estimated by Kaplan-Meier survival analysis. The cBio Cancer Genomics Portal was used to analyze the mutations of DCTN genes and their effects on the prognosis of LGG. The correlation between the abundance of immune infiltration and tumor purity of DCTN genes were predicted by The Tumor Immune Estimation Resource.Results:Our research showed that the mRNA expression of DCTN4 in tumor tissues was much higher (P < 0.01) than that in normal tissues. Meanwhile, there was a certain correlation between the DCTN genes. Survival analysis showed that the high expression of DCTN1, DCTN3, DCTN4, DCTN6, and their co-expression were significantly correlated with favorable OS in LGG patients (P < 0.05). In DCTN2, a high mutation rate was observed. Further research showed that the genetic alteration in DCTN genes was related to a poor OS and progression-free survival of LGG patients. The expression of DCTN genes had a certain correlation with immune infiltrating cells.Conclusion:Our study showed that the high expressions of DCTN1, DCTN3, DCTN4, and DCTN6 were associated with a favorable OS of LGG patients, indicating that these DCTN genes are potential biomarkers for evaluating the prognosis of LGG patients.

Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

BackgroundImmunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism.MethodsBased on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN.ResultsIn total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN.ConclusionsWe identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma.

ObjectiveThe main purpose of the present study is to screen prognostic small nucleolar RNA (snoRNA) markers using the RNA‐sequencing (RNA‐seq) dataset of The Cancer Genome Atlas (TCGA) sarcoma cohort.MethodsThe sarcoma RNA‐seq dataset comes from the TCGA cohort. A total of 257 sarcoma patients were included into the prognostic analysis. Multiple bioinformatics analysis methods for functional annotation of snoRNAs and screening of targeted drugs, including biological network gene ontology tool, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and connectivity map (CMap) are used.ResultsWe had identified 15 snoRNAs that were significantly related to the prognosis of sarcoma and constructed a prognostic signature based on four prognostic snoRNA (U3, SNORA73B, SNORD46, and SNORA26) expression values. Functional annotation of these four snoRNAs by their co‐expression genes suggests that some of them were closely related to cell cycle‐related biological processes and tumor‐related signaling pathways, such as Wnt, mitogen‐activated protein kinase, target of rapamycin, and nuclear factor‐kappa B signaling pathway. GSEA of the risk score suggests that high risk score phenotype was significantly enriched in cell cycle‐related biological processes, protein SUMOylation, DNA replication, p53 binding, regulation of DNA repair, and DNA methylation, as well as Myc, Wnt, RB1, E2F, and TEL pathways. Then we also used the CMap online tool to screen five targeted drugs (rilmenidine, pizotifen, amiprilose, quipazine, and cinchonidine) for this risk score model in sarcoma.ConclusionOur study have identified 15 snoRNAs that may be serve as novel prognostic biomarkers for sarcoma, and constructed a prognostic signature based on four prognostic snoRNA expression values.

Genome-scale integrated analysis to identify prospective molecular mechanisms and therapeutic targets in isocitrate dehydrogenase 2 R140Q-mutated acute myeloid leukemia.

The aim of the present study was to identify potential molecular mechanisms and therapeutic targets in regards to isocitrate dehydrogenase 2 (IDH2) R140Q-mutated acute myeloid leukemia (AML). An RNA sequencing dataset of IDH2 wild-type and R140Q-mutated adult de novo AML bone marrow samples was obtained from The Cancer Genome Atlas (TCGA) database. The edgeR package was used to screen for the differentially expressed genes (DEGs), and the potential molecular mechanisms and therapeutic targets were identified using Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8, Biological Networks Gene Ontology tool, Connectivity Map (CMap), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and GeneMANIA. A total of 230 DEGs were identified between the bone marrow tissues of IDH2 R140Q-mutated and wild-type AML patients, of which 31 were significantly associated with overall survival (OS). Functional assessment of DEGs showed significant enrichment in multiple biological processes, including angiogenesis and cell differentiation. STRING and GeneMANIA were used to identify the hub genes of these DEGs. CMap analysis identified 13 potential small-molecule drugs against IDH2 R140Q-mutated adult de novo AML. Genome-wide co-expression network analysis identified several IDH2 R140Q co-expressed genes, of which 56 were significantly associated with AML OS. The difference in IDH2 mRNA expression levels and OS between the IDH2 R140Q-mutated and wild-type AML were not statistically significant in our cohort. In conclusion, we identified several co-expressing genes and potential molecular mechanisms that are instrumental in IDH2 R140Q-mutated adult de novo AML, along with 13 candidate targeted therapeutic drugs.

Hub genes and gene functions associated with postmenopausal osteoporosis predicted by an integrated method.

Postmenopausal osteoporosis (PO) imposes great burden on individuals and society. This study predicted hub genes and gene functions for PO by an integration of the convergent evidence (CE) method, rank product (RP) algorithm and the combing of P-values. Using the gene expression data, genes were ranked by the CE method, RP algorithm and combing P-values, respectively. Subsequently, the top 100 genes were selected from each of the three gene lists, and then the common genes for two or three methods were denoted as informative genes of PO. A mutual information network (MIN) was constructed for the informative genes utilizing the context likelihood of relatedness algorithm. Topological centrality (degree) analysis was conducted on the MIN to investigate hub genes. Then we performed Gene Ontology (GO) enrichment analysis dependent upon the Biological Networks Gene Ontology tool (BiNGO) plugin of Cytoscape to investigate hub gene functions for PO patients. Consequently, a total of 82 informative genes were obtained by integrating the results of the three methods. There were 82 nodes and 1,741 edges in the MIN, of which 8 hub genes were identified, such as PFN1, EEF2 and S100A9. The result of GO enrichment analysis showed that 49 GO terms with P<0.001 were detected, especially the top 5 gene sets were defined as hub gene functions of PO, for instance, translational elongation, translation and cellular macromolecule biosynthetic process. In conclusion, we have predicted 8 hub genes and 5 hub gene functions associated with PO patients. The findings might help understand the molecular mechanism underlying PO.

Gene expression profiling of coronary artery disease and its relation with different severities

Global gene expression profiling is a powerful tool enabling the understanding of pathophysiology and subsequent management of diseases. This study aims to explore functionally annotated differentially expressed genes (DEGs); their biological processes for coronary artery disease (CAD) and its different severities of atherosclerotic lesions. This study also aims to identify the change in expression patterns of DEGs in atherosclerotic lesions of single-vessel disease (SVD) and triple-vessel disease (TVD). The weight of different severities of lesion was estimated using a modified Gensini score. The gene expression profiling was performed using the Affymetrix microarray platform. The functional annotation for CAD was performed using DAVID v6.8. The biological network gene ontology tool (BiNGO) and ClueGO were used to explore the biological processes of functionally annotated genes of CAD. The changes in gene expression from SVD to TVD were determined by evaluating the fold change. Functionally annotated genes were found in an unique set and could be distinguishing two distinct severities of CAD. The biological processes such as cellular migration, locomotion, cell adhesion, cytokine production, positive regulation of cell death etc. enriched the functionally annotated genes in SVD, whereas, wound healing, negative regulation of cell death, blood coagulation, angiogenesis and fibrinolysis were enriched significantly in TVD patients. The genes THBS1 and CAPN10 were functionally annotated for CAD in both SVD and TVD. The 61 DEGs were identified, those have changes their expression with different severities of atherosclerotic lesions, in which 13 genes had more than two-fold change in expression between SVD and TVD. The consistent findings were obtained on validation of microarray gene expression of selected 10 genes in a separate cohort using real-time PCR. This study identified putative candidate genes and their biological processes predisposing toward and affecting the severity of CAD.

Analysis of expression profile data identifies key genes and pathways in hepatocellular carcinoma.

The aims of the present study were to identify key genes and pathways associated with hepatocellular carcinoma (HCC) progression and predict compounds potentially associated with this type of carcinogenesis. The gene expression profile data of the GSE49515 dataset was obtained from the Gene Expression Omnibus database. The limma software package was used to identify the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Biological Networks Gene Ontology tool and the Database for Annotation, Visualization and Integrated Discovery, respectively. The Michigan Molecular Interactions database plugin within the Cytoscape software platform was used to perform protein-protein interaction (PPI) network analysis. Chemical-gene interaction data for HCC were obtained from the Comparative Toxicogenomics Database to evaluate the associations between drugs and specific genes. A total of 302 DEGs, including 231 downregulated and 71 upregulated, were identified. Cytokine-cytokine receptor interaction and chemokine signaling were the significantly enriched pathways. Additionally, PPI network analysis indicated a total of 13 highest degree hub nodes, including FBJ murine osteosarcoma viral oncogene homolog (FOS) and DNA damage-inducible transcript 3 protein (DDIT3). Chemical-gene interaction analysis revealed that FUN and FOS were targeted by >500 compounds, while >200 genes were targeted by 2,3,7,8-tetrachlorodibenzodioxin and benzo(α)pyrene. In conclusion, the present study demonstrated that FOS, DDIT3, the cytokine-cytokine receptor interaction pathway and the chemokine signaling pathway may be key genes and pathways associated with the development of HCC. Furthermore, exposure to 2,3,7,8-tetrachlorodibenzodioxin or benzo(α)pyrene may lead to hepatocarcinogenesis.

Overexpression of CD44 is associated with the occurrence and migration of non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a potentially fatal disease and the incidence is increasing annually. In order to diagnose and treat NSCLC effectively, greater understanding of its molecular mechanism is required. In the present study, 36 NSCLC tissues and 10 normal tissues were selected. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to analyze the CD44 mRNA expression level in NSCLC tissue and DNA sequencing was performed to further verify the CD44 expression level. Differentially expressed genes between tumor tissues and controls were determined by DNA sequencing and the Gene_act_net between CD44 and its associated genes was constructed. Gene Ontology (GO) term enrichment analysis of the differentially expressed genes was performed by the Biological Networks Gene Ontology tool. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed based on the Expression Analysis Systematic Explorer test applied in the Database for Annotation, Visualization and Integrated Discovery. RT-qPCR results showed that CD34 was overexpressed in 21 of the 36 NSCLC tissues (58.3%). The Gene_act_net indicated that there were 20 differentially expressed genes with 17 upregulated and 3 downregulated. Among them, CD44, MET, ERBB2, EGFR, AKT1, IQGAP1 and STAT3 were associated with the occurrence and migration of NSCLC. In KEGG pathway analysis, extracellular matrix-receptor interaction and hematopoietic cell lineage pathways were the most affected by overexpressed CD44; and thus may be important in the development and migration of NSCLC. In conclusion, CD44 was overexpressed in NSCLC and the overexpression was associated with the occurrence of NSCLC and migration of NSCLC cells.

Functional networks for Salvia miltiorrhiza and Panax notoginseng in combination explored with text mining and bioinformatical approach

Salvia miltiorrhiza (SM) and Panax notoginseng (PN) in combination (SMPN) have been widely used primarily in Traditional Chinese Medicine (TCM), for the treatment of coronary heart disease, and its pharmacological activity should be complicated because of its multiple components. Here, we combine text mining with bioinformatics to predict functional networks for the combination. 53 genes related with SMPN were found with text mining. Protein-protein interaction information for these genes from databases and Literature data was searched. Eight highly-connected regions were detected by IPCA algorithm to infer significant complexes or pathways in this network. Over-represented Gene Ontology categories of highly-connected regions by biological network gene ontology tool involved in small GTPase mediated signal transduction, apoptosis, regulation of immune effector process, phosphorylation about enzyme linked receptor protein signaling pathway, positive regulation of biological process. Integrate expression data from six microarray experiments about coronary heart disease into the SMPN network, and use the jActiveModules tool to find active subnetworks in differential expression conditions. The most relevant functions and pathways extracted from these subnetworks were related to proliferation and apoptosis of endothelial cell, apoptosis of arterial smooth muscle cell, apoptosis and regulation of immune system process within macrophages during foam cell formation, cardiocyte apoptosis. Analysis of the subnetwork composition indicated that there were in each subnetworks, and in the most subnetworks were dominant, the nodes came from SM network more than from PN network. It was suggested that, therapeutic efficacies of SMPN should be results of interaction between SM and PN in the multiple pathways and biological processes, and SM maybe play a principal role and PN serve as adjuvant one to assist the effects during the treatment of coronary heart disease. Key words: Panax notoginseng, salvia miltiorrhiza, herbal combination, bioinformatics, text mining, pharamcological activity.

Correlation between cold and hot pattern in traditional Chinese medicine and gene expression profiles in rheumatoid arthritis

Clinical manifestations of rheumatoid arthritis (RA) are diversified, and based on the manifestations, the patients with RA could be classified into different patterns under traditional Chinese medicine. These patterns decide the selection of herbal prescription, and thus they can help find a subset of rheumatoid arthritis patients for a type of therapy. In the present study, we combine genome-wide expression analysis with methods of systems biology to identify the functional gene networks for the sets of clinical symptoms that comprise the major information for pattern classification. Clinical manifestations in rheumatoid arthritis were clustered with factor analysis, and two factors (similar to cold and hot patterns in traditional Chinese medicine) were found. Microarray technology was used to reveal gene expression profiles in CD4(+) T cells from 21 rheumatoid arthritis patients. Protein-protein interaction information for these genes from databases and literature data was searched. The highly-connected regions were detected to infer significant complexes or pathways in this protein-protein interaction network. The significant pathways and function were extracted from these subnetworks using the Biological Network Gene Ontology tool. The genes significantly related to hot and cold patterns were identified by correlations analysis. MAPK signalling pathway, Wnt signaling pathway, and insulin signaling pathway were found to be related to hot pattern. Purine metabolism was related to both hot and cold patterns. Alanine, aspartate, and tyrosine metabolism were related to cold pattern, and histindine metabolism and lysine degradation were related to hot pattern. The results suggest that cold and hot patterns in traditional Chinese medicine were related to different pathways, and the network analysis might be used for identifying the pattern classification in other diseases.

Cold and heat pattern of rheumatoid arthritis in traditional Chinese medicine: distinct molecular signatures indentified by microarray expression profiles in CD4-positive T cell

The research is aimed to explore the distinct molecular signatures in discriminating the rheumatoid arthritis patients with traditional Chinese medicine (TCM) cold pattern and heat pattern. Twenty patients with typical TCM cold pattern and heat pattern were included. Microarray technology was used to reveal gene expression profiles in CD4+ T cells. The signal intensity of each expressed gene was globally normalized using the R statistics program. The ratio of cold pattern to heat pattern in patients with RA at more or less than 1:2 was taken as the differential gene expression criteria. Protein–protein interaction information for these genes from databases was searched, and the highly connected regions were detected by IPCA algorithm. The significant pathways were extracted from these subnetworks by Biological Network Gene Ontology tool. Twenty-nine genes differentially regulated between cold pattern and heat pattern were found. Among them, 7 genes were expressed significantly more in cold pattern. Biological network of protein–protein interaction information for these significant genes were searched and four highly connected regions were detected by IPCA algorithm to infer significant complexes or pathways in the biological network. Particularly, the cold pattern was related to Toll-like receptor signaling pathway. The following related pathways in heat pattern were included: Calcium signaling pathway; cell adhesion molecules; PPAR signaling pathway; fatty acid metabolism. These results suggest that better knowledge of the main biological processes involved at a given pattern in TCM might help to choose the most appropriate treatment.

A Network-based Analysis of Polyanion-binding Proteins Utilizing Yeast Protein Arrays

The high affinity of certain cellular polyanions for many proteins (polyanion-binding proteins (PABPs)) has been demonstrated previously. It has been hypothesized that such polyanions may be involved in protein structure stabilization, stimulation of folding through chaperone-like activity, and intra- and extracellular protein transport as well as intracellular organization. The purpose of the proteomics studies reported here was to seek evidence for the idea that the nonspecific but high affinity interactions of PABPs with polyanions have a functional role in intracellular processes. Utilizing yeast protein arrays and five biotinylated cellular polyanion probes (actin, tubulin, heparin, heparan sulfate, and DNA), we identified proteins that interact with these probes and analyzed their structural and amino acid sequence requirements as well as their predicted functions in the yeast proteome. We also provide evidence for the existence of a network-like system for PABPs and their potential roles as critical hubs in intracellular behavior. This investigation takes a first step toward achieving a better understanding of the nature of polyanion-protein interactions within cells and introduces an alternative way of thinking about intracellular organization.

BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results.