Search Tool For The Retrieval Of Interacting Genes/Proteins Research Articles

Differential Gene Expression Pattern in Osteoclast Precursor Cells of Indian Postmenopausal Women with and Without Osteoporosis: A Microarray Based Study

Background: Osteoporosis is a multifactorial disease with strong genetic and epigenetic component. In spite of enormous candidate gene association studies, the etiology and molecular mechanism of disease is not fully known. For identification of new markers of osteoporosis which could be vital in diagnosis and prognosis of disease, genome-wide microarray expression approach was employed.Methods: Osteoclast precursor cells were sorted from circulating monocytes of osteoporotic and nonosteoporotic post menopausal females with similar life-style and year after menopause. Following microarray experimentation, gene enrichment analysis was performed on significant DEGs using Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Top 10 novel genes were further used for construction of protein-protein interaction network using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. To validate microarray gene expression pattern, Real time-PCR was performed.Results: A total of 269 genes were found to be differentially expressed between disease and normal groups, of which 138 were observed to be up regulated and 131 down regulated. Furthermore, novel LHX1 gene was observed to be up regulated and its interaction with BMP4 protein was observed. Three known genes for osteoclastogenesis (viz., CX3CL1, ACP5 and CSF1) were found to be up- regulated. Similar pattern of gene expression have been obtained using RT-PCR.Conclusion: Significant enrichment of PI3K-Akt and TGF-s signaling pathways involving DEGs was found in postmenopausal Asian Indian women. Moreover, up regulated LHX1 gene was discovered as novel gene which may have a role in pathogenesis of osteoporosis and can be used for diagnosis and prognosis along with known osteoporotic markers.

Proteomic changes in the ileum of sheep infected with Mycobacterium avium subspecies paratuberculosis

Johne's disease (JD) is a chronic enteritis of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP). To identify the processes activated in the sheep intestine during natural MAP infection, and to provide a panel of differential host and pathogen proteins with diagnostic and prognostic potential, a differential shotgun proteomics workflow, including mass spectrometry, label-free quantisation and pathway analysis, was applied to ileal tissues of ewes with and without JD. Out of 2889 total proteins identified, 384 were differentially expressed and 341 were expressed at a higher level in JD. On the basis of Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) analysis, these proteins were involved in numerous relevant biological networks and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including inhibition of phagosome acidification (such as V-ATPase), bacterial invasion, leucocyte recruitment and activation, and antimicrobial activity (such as haptoglobin, lactoferrin, cathelicidins, calgranulins and interleukins). A total of 28 MAP proteins were identified, including bacterioferritin, β-lactamase and heparin-binding haemagglutinin (HBHA), a mycobacterial adhesin crucial for dissemination of infection.

Maternal Chromium Restriction Leads to Glucose Metabolism Imbalance in Mice Offspring through Insulin Signaling and Wnt Signaling Pathways.

An adverse intrauterine environment, induced by a chromium-restricted diet, is a potential cause of metabolic disease in adult life. Up to now, the relative mechanism has not been clear. C57BL female mice were time-mated and fed either a control diet (CD), or a chromium-restricted diet (CR) throughout pregnancy and the lactation period. After weaning, some offspring continued the diet diagram (CD-CD or CR-CR), while other offspring were transferred to another diet diagram (CD-CR or CR-CD). At 32 weeks of age, glucose metabolism parameters were measured, and the liver from CR-CD group and CD-CD group was analyzed using a gene array. Quantitative real-time polymerase chain reaction (qPCR) and Western blot were used to verify the result of the gene array. A maternal chromium-restricted diet resulted in obesity, hyperglycemia, hyperinsulinemia, increased area under the curve (AUC) of glucose in oral glucose tolerance testing and homeostasis model assessment of insulin resistance (HOMA-IR). There were 463 genes that differed significantly (>1.5-fold change, p < 0.05) between CR-CD offspring (264 up-regulated genes, 199 down-regulated genes) and control offspring. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) analysis revealed that the insulin signaling pathway and Wnt signaling pathway were in the center of the gene network. Our study provides the first evidence that maternal chromium deficiency influences glucose metabolism in pups through the regulation of insulin signaling and Wnt signaling pathways.

Cell division cycle-associated 7-like gene: A novel biomarker for adverse survival in human high-grade gliomas

Background: High-grade primary gliomas are aggressively growing and have an unfavorable prognosis. The utility of prognostic biomarkers of outcome in glioma patients is important for medical practice. Cell division cycle-associated 7-like (CDCA7L) protein modifies cancer progression and metastasis. Nevertheless, its character in defining the clinical prognosis of human gliomas has not been illuminated. Subjects and Methods: The hypothesis of this study was that CDCA7L is upregulated in human gliomas. We studied two de-linked data from Gene Expression Omnibus (GEO) profile. The first dataset (GDS1816/225081_s_at/CDCA7L) in primary high-grade glioma included age, gender, and survival time. Another dataset (GDS1962/225081_s_at/CDCA7L) was also encompassed to estimate CDCA7L gene expression in each pathological grading. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to survey the protein-protein interaction (PPI) network of CDCA7L-regulated oncogenesis. Results: Statistical analysis of the GEO profile revealed that the World Health Organization (WHO) Grade IV (n = 81) gliomas had higher CDCA7L mRNA expression level than in Grade II (n = 7, P = 2.15 × 10 −14) gliomas and nontumor controls (n = 23, P = 2.87 × 10 − 18). Kaplan-Meier analysis reported that patients with high CDCA7L mRNA levels (n = 49) had adverse survival than those with low CDCA7L expression (n = 28). The PPI analysis of CDCA7L-regulated oncogenesis showed CDCA7L as a potential hub protein. Conclusions: The expression of CDCA7L has a positive correlation with the WHO pathological grading and shorter survival. This finding suggests that CDCA7L may be a potential biomarker of prognosis in human gliomas.

Biological processes and pathway changes in isoflurane-induced anesthesia revealed by bioinformatics analysis of gene expression profiles.

To identify differentially expressed genes (DEGs) and further analyze potential biological processes and pathways involved in isoflurane-induced anesthesia. Microarray data (ID: GSE64617) from rat brains treated with exposure to either isoflurane in oxygen (2%) (test group) or oxygen alone (control group) for 15 min were downloaded from Gene Expression Omnibus. Data pre-processing was performed using the Affy package, followed by DEG screening using the limma package. Protein-protein interactions (PPIs) among DEGs were obtained from STRING (Search Tool for the Retrieval of Interacting Genes/Proteins), and then visualized by constructing a network using Cytoscape. Functional and pathway enrichment analyses were further implemented to identify the biological processes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways enriched by DEGs. A total of 240 DEGs were identified between the test and control groups, including 128 up-regulated DEGs and 112 down-regulated DEGs in the test group, of which 17 DEGs with a connectivity degree >4 were identified as hub genes (e.g., Pik3r1 and Pik3r2) in the constructed PPI network. Additionally, Slc17a7 and Camk4 interacted with Syn1, and Pik3r1 interacted with Pik3r2. Enrichment analysis further revealed the significantly enriched biological processes of 'synaptic transmission', 'cell-cell signaling' and 'transmission of nerve impulse' (e.g., Slc17a7, Camk4, Syn1, Gria1, Prkcg and Lphn1), as well as KEGG pathways including 'focal adhesion', 'Fc γ R-mediated phagocytosis' (e.g., Prkcg, Pik3r1 and Pik3r2), and 'regulation of actin cytoskeleton' (e.g., Pik3r1 and Pik3r2). The identified DEGs significantly enriched in biological processes and KEGG pathways might be implicated in isoflurane-induced anesthesia.

A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network.

The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson’s correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson’s correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis.

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer

ObjectivesTo explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. MethodsThe differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein–protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. ResultsCo-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. ConclusionsOur results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC.

Risk prediction using epigenetic profiles in blood of osteoarthritis patients

Purpose: Previous studies have highlighted the urgent need for biomarkers to predict OA progression in the clinic, preferably with an AUC of at least 70%. In the current study, we set out to gain more insight into the epigenetically differentially regulated gene networks in peripheral blood mononuclear cells (PBMCs) of OA patients of the GARP study, in particular to predict risk for OA progression within 2 years. Methods: Genome wide DNA methylation was measured in PBMCs of 148 female participants of the GARP study using Illumina Infinium HumanMethylation450 BeadChip arrays. The GARP study consists of patients with generalized OA with a familial background. Following quality control of the data and elimination of CpG-probes containing known single nucleotide polymorphisms (SNPs) or mapped ambiguously, linear regression analysis was performed in R statistical programming language to identify CpGs that associate with OA progression as defined by increased joint space narrowing and osteophytosis at hand, hip, and knee joints while correcting for technical covariates as well as for age and BMI. P-values reported were corrected for multiple testing by the Benjamini-Hochberg method. Gene enrichment analyses (DAVID) was performed for biological processes with significant CpG-probes that were annotated to genes expressed in PBMCs, and protein networks were analyzed with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Results: We identified 908 CpGs that significantly associated with OA progression in 2 years (lowest p=6.2x10-9), of which 2 were also significantly associated with OA progression over 5 years. Of the 908 significant CpGs, 227 were annotated to 201 unique genes expressed in blood. Pathway analysis showed significant enrichment of genes involved in GO-term 0000904: cell morphogenesis involved in differentiation (p=4.4x10-2) and borderline significance for genes involved in GO-term 0031175: neuron projection development (p=6.8x10-2). Among the genes to which the top CpGs were annotated we observed OA relevant genes such as NOTCH1. In addition, the network of protein-protein interactions among identified genes was significantly enriched as determined with STRING (p=2.2x10-3). Baseline receiver operating characteristic (ROC) analysis using age and BMI did not result in significant discriminative capacity (area under the curve AUC=57%). However, including methylation status of 4 of the most significant CpGs associated with OA progression in 2 years, resulted in a 20% increase to a clinical relevant AUC of 76%. Conclusions: In the current study, we showed that OA progression over a 2-year period in women can be marked by epigenetic changes in the easily accessible tissue blood as reflected by methylation of CpG sites at OA relevant genes. Furthermore, we are the first to show that, by using the readily accessible methylation status of only 4 CpG sites, a clinical relevant prediction of OA progression can be established with a ROC of 76%. Our results, therefore, show that the epigenetic landscape in blood of OA patients can provide a powerful tool for prognoses of OA. Confirmation in an independent study is important given these promising results.

Analysis of dermal papilla cell interactome using STRING database to profile the ex vivo hair growth inhibition effect of a vinca alkaloid drug, colchicine.

Dermal papillae (DPs) control the formation of hair shafts. In clinical settings, colchicine (CLC) induces patients’ hair shedding. Compared to the control, the ex vivo hair fiber elongation of organ cultured vibrissa hair follicles (HFs) declined significantly after seven days of CLC treatment. The cultured DP cells (DPCs) were used as the experimental model to study the influence of CLC on the protein dynamics of DPs. CLC could alter the morphology and down-regulate the expression of alkaline phosphatase (ALP), the marker of DPC activity, and induce IκBα phosphorylation of DPCs. The proteomic results showed that CLC modulated the expression patterns (fold > 2) of 24 identified proteins, seven down-regulated and 17 up-regulated. Most of these proteins were presumably associated with protein turnover, metabolism, structure and signal transduction. Protein-protein interactions (PPI) among these proteins, established by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, revealed that they participate in protein metabolic process, translation, and energy production. Furthermore, ubiquitin C (UbC) was predicted to be the controlling hub, suggesting the involvement of ubiquitin-proteasome system in modulating the pathogenic effect of CLC on DPC.

SMC3 may play an important role in atopic asthma development.

As a common disease with a complex risk, including genetic and environmental factors, atopic asthma is prevalent but treatable. The aim of the study was to predict the underlying mechanism of asthma and identify target genes for the disease. The affymetrix chip data, GSE18965, were available from Gene Expression Omnibus and the differentially expressed genes (DEGs) between nine atopic asthmatic specimens and seven healthy nonatopic samples were identified by R. Then Gene Ontology and pathway enrichment analyses were performed to these DEGs. Further, search tool for the retrieval of interacting genes/proteins (STRING) was used to select protein-protein interaction (PPI) for DEGs, and then the network was visualized by Cytoscape. Finally, transcription factor binding site analysis was conducted to the hot gene. Total 565 DEGs were identified, including 535 upregulated and 30 downregulated genes. The upregulated genes, such as structural maintenance of chromosome (SMC)3, significantly affected cellular component of extracellular matrix (P = 1.56E-04). Otherwise, DEGs were remarkably enriched in three pathways, including transforming growth factor-beta signaling pathway (P = 0.005252649). Further, SMC3 was detected as hot gene in PPI network, and NET (Elk3) was predicted as the significant transcription factor for this gene. SMC3 may play an important role in atopic asthma development; therefore, it has the potential to be the target for the disease. Moreover, our findings provide more knowledge about the mechanism of atopic asthma and help the researchers to explore it in future.

Construction of a gene-gene interaction network with a combined score across multiple approaches.

Recent progress in computational methods for inves-tigating physical and functional gene interactions has provided new insights into the complexity of biological processes. An essential part of these methods is presented visually in the form of gene interaction networks that can be valuable in exploring the mechanisms of disease. Here, a combined network based on gene pairs with an extra layer of re-liability was constructed after converting and combining the gene pair scores using a novel algorithm across multiple approaches. Four groups of kidney cancer data sets from ArrayExpress were downloaded and analyzed to identify differentially expressed genes using a rank prod-ucts analysis tool. Gene co-expression network, protein-protein interac-tion, co-occurrence network and a combined network were constructed using empirical Bayesian meta-analysis approach, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, an odds ratio formula of the cBioPortal for Cancer Genomics and a novel rank algorithm with combined score, respectively. The topological features of these networks were then compared to evaluate their performances. The results indicated that the gene pairs and their relationship rank-ings were not uniform. The values of topological parameters, such as clustering coefficient and the fitting coefficient R(2) of interaction net-work constructed using our ranked based combination score, were much greater than the other networks. The combined network had a classic small world property which transferred information quickly and displayed great resilience to the dysfunction of low-degree hubs with high-clustering and short average path length. It also followed distinct-ly a scale-free network with a higher reliability.

A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

Proteomic investigation of Vibrio alginolyticus challenged Caenorhabditis elegans revealed regulation of cellular homeostasis proteins and their role in supporting innate immune system.

Caenorhabditis elegans has been the preferred model system for many investigators to study pathogenesis. In the present investigation, regulation of C. elegans proteome was explored against V. alginolyticus infection using quantitative proteomics approach. Proteins were separated using 2D-DIGE and the differentially regulated proteins were identified using PMF and MALDI TOF/TOF analysis. The results thus obtained were validated using Western blotting for candidate proteins. The corresponding transcriptional regulation was quantified subsequently using real-time PCR. Interaction network for candidate proteins was predicted using search tool for the retrieval of interacting genes/proteins (STRING) and functional validation was performed using respective mutant strains. Out of the 25 proteins identified, 21 proteins appeared to be upregulated while four were downregulated. Upregulated proteins included those involved in stress-response (PDI-2, HSP-6), immune-response (protein kinase -18, GST-8) and energy-production (ATP-2) while proteins involved in structural maintenance (IFB-2) and lipid metabolism (SODH-1) were downregulated. The roles of these players in the host system during Vibrio infection was analyzed in vivo using wild type and mutant C. elegans. Survival assays using mutants lacking pdi-2, ire-1, and xbp-1 displayed enhanced susceptibility to V. alginolyticus. Cellular stress generated by V. alginolyticus was determined using ROS assay. This is the first report of proteome changes in C. elegans against V. alginolyticus challenge and highlights the significance of unfolded protein response (UPR) pathway during bacterial infection.

Differential proteomic analysis of the pancreas of diabetic db/db mice reveals the proteins involved in the development of complications of diabetes mellitus.

Type 2 diabetes mellitus is characterized by hyperglycemia and insulin-resistance. Diabetes results from pancreatic inability to secrete the insulin needed to overcome this resistance. We analyzed the protein profile from the pancreas of ten-week old diabetic db/db and wild type mice through proteomics. Pancreatic proteins were separated in two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and significant changes in db/db mice respect to wild type mice were observed in 27 proteins. Twenty five proteins were identified by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) and their interactions were analyzed using search tool for the retrieval of interacting genes/proteins (STRING) and database for annotation, visualization and integrated discovery (DAVID). Some of these proteins were Pancreatic α-amylase, Cytochrome b5, Lithostathine-1, Lithostathine-2, Chymotrypsinogen B, Peroxiredoxin-4, Aspartyl aminopeptidase, Endoplasmin, and others, which are involved in the metabolism of carbohydrates and proteins, as well as in oxidative stress, and inflammation. Remarkably, these are mostly endoplasmic reticulum proteins related to peptidase activity, i.e., they are involved in proteolysis, glucose catabolism and in the tumor necrosis factor-mediated signaling pathway. These results suggest mechanisms for insulin resistance, and the chronic inflammatory state observed in diabetes.

Gene expression analysis of lung adenocarcinoma and matched adjacent non-tumor lung tissue.

The aim of this study was to find disease-associated genes and gene functions in lung adenocarcinoma and matched adjacent non-tumor lung tissues with DNA microarray. We downloaded the gene expression profile GSE32863 from the Gene Expression Omnibus database including 58 lung adenocarcinoma and 58 adjacent non-tumor lung tissue samples. Data were preprocessed and the differentially expressed genes (DEGs) were identified using packages in the R computing language. The selected DEGs were further analyzed with bioinformatics methods. After the coexpression network of DEGs was constructed by STRING (Search Tool for the Retrieval of Interacting Genes/Proteins), we analyzed gene functions with DAVID (The Database for Annotation, Visualization and Integrated Discovery) and WebGestalt (WEB-based Gene Set Analysis Toolkit). A total of 1429 genes were filtered as DEGs, including 873 downregulated genes and 556 upregulated genes, and the DEGs including CDC45, CCNB2, CDC20, MCM2, PTTG1, MCM4 and FEN1 were most significantly related to cell cycle and DNA replication. The discovery of featured genes which were significantly related to cell cycle and DNA replication has potential for use in the clinic for the diagnosis of lung adenocarcinoma in the future. However, further experiments will be needed to confirm our result.

Focussed microarray analysis of apoptosis in periodontitis and its potential pharmacological targeting by carvacrol

ObjectiveThe objective of this study was to perform a landscape analysis of apoptosis-related genes/proteins and to study the differential gene expression by analysing array data from periodontitis patients and, second, to evaluate the anti-apoptotic effects of carvacrol, a monoterpenoid phenol, in vitro. DesignA gene/protein interaction network model ‘APOP’ was developed by using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) version 9.05. Differential gene expression was determined by using the limma package from R and false discovery rate (FDR). With ViaComplex software, gene expression was plotted over the network. The anti-apoptotic effect of carvacrol was tested on sorbitol-treated HaCaT cells, by using a commercial kit for caspase-3 activity. ResultsThe ‘APOP’ model characterised the landscape of interactions between apoptosis-related genes/proteins in silico. Forty-nine out of 70 genes from this model, such as CSF2RB, NFKBIE, ENDOG, CASP10 and CASP3, were differentially expressed (corrected p-value<0.05) in periodontitis samples when compared to those of healthy controls. In addition, carvacrol (0.43%) was able to inhibit the pro-apoptotic effects induced by sorbitol (0.3M), as seen by the reduction in caspase-3 activity on HaCaT cells. ConclusionOur results suggest that caspase-3 can be a target protein to inhibit periodontitis-associated apoptosis of epithelial cells and that carvacrol has therapeutic potential as an anti-apoptotic agent.

Functional analysis of differentially expressed genes associated with glaucoma from DNA microarray data.

Microarray data of astrocytes extracted from the optic nerves of donors with and without glaucoma were analyzed to screen for differentially expressed genes (DEGs). Functional exploration with bioinformatic tools was then used to understand the roles of the identified DEGs in glaucoma. Microarray data were downloaded from the Gene Expression Omnibus (GEO) database, which contains 13 astrocyte samples, 6 from healthy subjects and 7 from patients suffering from glaucoma. Data were pre-processed, and DEGs were screened out using R software packages. Interactions between DEGs were identified, and networks were built using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). GENECODIS was utilized for the functional analysis of the DEGs, and GOTM was used for module division, for which functional annotation was conducted with the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A total of 371 DEGs were identified between glaucoma-associated samples and normal samples. Three modules included in the PPID database were generated with 11, 12, and 2 significant functional annotations, including immune system processes, inflammatory responses, and synaptic vesicle endocytosis, respectively. We found that the most significantly enriched functions for each module were associated with immune function. Several genes that play interesting roles in the development of glaucoma are described; these genes may be potential biomarkers for glaucoma diagnosis or treatment.

Identification of human prolactinoma related genes by DNA microarray.

To identify the genes involved in prolactinoma by bioinformatics methods and provide new potential biomarkers for prolactinoma. The gene-expression profile data, GSE36314, including 4 prolactinoma samples and 3 controls, was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package in R and were then classified into different functional groups by COG (Clusters of Orthologous Groups) annotation based on BLASTX (Basic Local Alignment Search Tool). Transcriptional factors (TFs) were screened out by employing the Transcription Factor (TRANSFAC) database. An interaction network among DEGs and TFs was constructed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis were then performed for the genes in this network. A total of 52 genes were identified as being significantly different between prolactinomas and normal samples which were classified into 29 COG functional categories. Three TFs, ZIC3 (Zic family member 3), NGFIC (nerve growth factor-induced protein C) and SP1 (Specificity Protein 1) were screened out, which can regulate part of DEGs. Two down-regulated genes, FSHB (follicle stimulating hormone β subunit) and LHB (luteinizing hormone β subunit) were involved in GnRH (gonadotropin-releasing hormone) signaling pathway. Several DEGs between prolactinoma and normal samples were identified in our study and candidate agents such as LHB and FSHB may provide the groundwork for a targeted therapy approach for prolactinomas.

Protein-protein interaction network analysis of osteoarthritis-related differentially expressed genes.

The purpose of this study was to identify genes and pathways for osteoarthritis (OA) diagnosis and therapy. We downloaded the gene expression profile of OA from Gene Expression Omnibus (GEO) database including 10 early OA, 9 late OA, and 5 normal control samples. Next, we screened differentially expressed genes (DEGs) between early- and late-stage OA samples comparing with healthy control samples. Then, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software was used to construct protein-protein interaction (PPI) network, which was to predict the proteins that may interact with DEGs. The Gene Ontology (GO)-enrichment method was used to analyze the function of genes in the PPI networks. Meanwhile network module analysis was performed using Cytoscape. A total of 24 and 29 DEGs were identified for the early and late OA, respectively. TAC1 showed the highest degree in the PPI network. Functional annotation of the TAC1 network module indicated that this gene is associated with the G protein-coupled signal transduction pathway. In summary, TAC1, together with G protein-coupled receptors, appear to play a role in the biogenesis and progress of OA. Further analysis of this gene and pathway could therefore provide a potential target for the diagnosis and treatment of OA.

Urinary a HGF, IGFBP3 and OPN as Diagnostic and Prognostic Biomarkers for Prostate Cancer

Serum PSA screening for prostate cancer (PCa) is controversial. Here, we identify three urinary biomarkers - aHGF, IGFBP3 and OPN - for PCa screening and prognostication. Urinary aHGF, OPN and IGFBP3 from healthy men (n = 19) and men with localized (n = 65) and metastatic (n = 36) PCa were quantified via ELISA. Mann-Whitney nonparametric t-test and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) analyses were used to analyze associations. Mean aHGF and IGFBP3 levels were significantly elevated in PCa patients versus controls (p = 0.0006 and p = 0.0012, respectively), and the area under the curve of the receiver operating characteristic curve (indicator of diagnostic accuracy) for aHGF and IGFBP3 was 0.75 and 0.74, respectively. OPN levels were significantly higher in metastatic groups (p = 0.0060) versus localized and controls (area under the curve = 0.68). Urinary aHGF and IGFBP3 exhibit the capacity for diagnostic discrimination for PCa, whereas OPN may indicate presence of metastatic disease.