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中国科技论文在线 GPSM2 Signal Transduction Computational

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中国科技论文在线 GPSM2 Signal Transduction Computational 中国科技论文在线 http://www.paper.edu.cn GPSM2 Signal Transduction Computational Network Analysis in Human No-Tumor Hepatitis/Cirrhosis and # 5 Hepatocellular Carcinoma Transformation ZHUANG Jing, HUANG Juxiang, WANG Lin** (School of Electronic Engineering, Beijing University of Posts and Telecommunications, 100876) Abstract: LGN protein (GPSM2) is involved in transcription or cell division presented in several papers. However, how the molecular network and interpretation concerning GPSM2 signal 10 transduction between no-tumor hepatitis/cirrhosis and hepatocellular carcinoma (HCC) transformation remains to be elucidated. Here we constructed and analyzed significant higher expression gene GPSM2 activated & inhibited upstream and downstream signal transduction network from HCC vs no-tumor hepatitis/cirrhosis pateints (viral infection HCV or HBV) in GEO Dataset by using gene regulatory network inference method based on linear programming and decomposition procedure, under covering 15 GPSM2 pathway and matching signal transduction enrichment analysis by the CapitalBio MAS 3.0 integrated of public databases including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, etc. By compared the different activated & inhibited GPSM2 network with GO analysis between no-tumor hepatitis/cirrhosis and HCC transformation, our result showed GPSM2 signal transduction network: (1) more nucleus and cytoplasm but less extracellular space protein binding in 20 no-tumor hepatitis/cirrhosis; (2) more growth factor activity but less cytoplasm enzyme activator activity in HCC; (3) less activation & more inhibition molecular numbers in no-tumor hepatitis/cirrhosis but more activation & less inhibition in HCC. Therefore, we inferred (4) GPSM2 signal transduction network stronger transcription but weaker cell differentiation as a result increasing cytoplasm protein translation in no-tumor hepatitis/cirrhosis; (5) stronger cell proliferation but weaker 25 regulation of muscle contraction as a result inceasing nuclear cell division in HCC. Key words: LGN protein (GPSM2) computational network; signal transduction; human hepatocellular carcinoma; transformation; no-tumor hepatitis/cirrhosis 0 Introduction 30 LGN protein (GPSM2) is one of our identified significant high expression genes (fold change ≥2) in human hepatocellular carcinoma compared with no-tumor hepatitis/cirrhosis from GEO Dataset GSE10140-10141 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10140, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10141). GPSM2 cellular component localizes in cytoplasm, and molecular function comprises GTPase activator activity, identical 35 protein binding, and biological process contains signal transduction, G-protein coupled receptor protein signaling pathway (GO (http://www.geneontology.org)). GPSM2 is related to enzyme activator activity, gtpase activator activity (GenMAPP (http://www.genmapp.org/)). From the above analysis, GPSM2 is involved in signal transduction which is very important for no-tumor hepatitis/cirrhosis and hepatocellular carcinoma (HCC) transformation. The relationships between 40 hepatitis/cirrhosis or hepatocellular carcinoma with signal transduction are as follows: Signal transduction cascades and hepatitis B and C related hepatocellular carcinoma [1]; 14-3-3zeta up-regulates hypoxia-inducible factor-1alpha in hepatocellular carcinoma via activation of PI3K/Akt/NF-small ka, CyrillicB signal transduction pathway[2]; Genistein inhibits tumor invasion by suppressing multiple signal transduction pathways in human hepatocellular carcinoma cells [3]; 45 Polycyclic aromatic hydrocarbons induce migration in human hepatocellular carcinoma cells (HepG2) through reactive oxygen species-mediated p38 MAPK signal transduction [4]; Pterostilbene inhibited tumor invasion via suppressing multiple signal transduction pathways in Foundations: National Natural Science Youth Fundation of China (No. 81501372) Brief author introduction: ZHUANG Jing (1994-), Female, Master student, Bioinformatics Correspondance author: HUANG Juxiang (1986-), Female, Associate professor, Bioinformatics. E-mail: [email protected] - 1 - 中国科技论文在线 http://www.paper.edu.cn human hepatocellular carcinoma cells [5]; The A3 adenosine receptor agonist CF102 induces apoptosis of hepatocellular carcinoma via de-regulation of the Wnt and NF-kappaB signal 50 transduction pathways [6]; Radiation-enhanced hepatocellular carcinoma cell invasion with MMP-9 expression through PI3K/Akt/NF-kappaB signal transduction pathway [7]; Oncogenic signal transduction and therapeutic strategy for hepatocellular carcinoma [8]. However, the distinct molecular network and interpretation concerning GPSM2 signal transduction network in no-tumor hepatitis/cirrhosis and HCC transformation remains to be elucidated. 55 HCC is one of the most common causes of cancer-related death. So to develop novel drugs in HCC has become a challenge for biologists. Here we constructed and analyzed significant higher expression gene GPSM2 activated & inhibited signal transduction network from HCC vs no-tumor hepatitis/cirrhosis pateints (viral infection HCV or HBV) in GEO Dataset by gene regulatory network inference method based on linear programming and decomposition procedure, on the 60 condition that our GPSM2 network covered GPSM2 pathway and matched signal transduction enrichment analysis by the CapitalBio MAS 3.0 based on the integration of public databases including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, etc. The mechanisms that shut off a signal are as important as the mechanisms that turn it on. GRNInfer tool can identified molecular activation and inhibition relationships based on a novel 65 mathematic method called GNR (Gene Network Reconstruction tool) using linear programming and a decomposition procedure for inferring gene networks. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the reconstruction reliability [9]. 70 In this paper, by compared the different activated & inhibited molecules and numbers of GPSM2 network with GO analysis between no-tumor hepatitis/cirrhosis and HCC transformation, we need to do (1) prediction of different stronger and weaker cellular component, molecular function and biological process between GPSM2 signal transduction network of no-tumor hepatitis/cirrhosis and HCC transformation, (2) computation of different more and less activation 75 and inhibition numbers between GPSM2 signal transduction network of no-tumor hepatitis/cirrhosis and HCC transformation, (3) different inferences between GPSM2 signal transduction network of no-tumor hepatitis/cirrhosis and HCC transformation, (4) different evidences from literatures to support the inferences respectively. 1 Materials and Methods 80 1.1 Microarray Data We used microarrays containing 6144 genes from 25 no-tumor hepatitis/cirrhosis and 25 HCC patients in GEO Dataset GSE10140-10141 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10140, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10141). We preprocessed raw 85 microarray data as log2. 1.2 Gene Selection Algorithms Potential HCC molecular markers were identified using significant analysis of microarrays (SAM) [10]. We normalized data by log2, selected two classes paired and minimum fold change ≥2, and chose the significant high expression value genes of HCC compared with no-tumor 90 hepatitis/cirrhosis genes under the false-discovery rate and q-value were 0%. The q-value is like the well-known p-value, but adapted to multiple-testing situations. - 2 - 中国科技论文在线 http://www.paper.edu.cn 1.3 Unsupervised Clustering Significant higher expression genes from no-tumor hepatitis/cirrhosis vs HCC was done unsupervised clustering using cluster 3.0(http://bonsai.ims.tokyo.ac.jp/~mdehoon/software/cluster). 95 The steps are as follows: Step 1 Loading and filtering 100% data ; Step 2 Normalizing log transform data for adjusting data; Step 3 Choosing gene culster and array cluster; Step 4 Choosing average linkage of hierarchical clustering Step 5 Doing TreeView. 1.4 Molecule Annotation System (MAS) Molecule Annotation System (MAS) (http://bioinfo.capitalbio.com/mas3/) is a web-based 100 software toolkit for a whole data mining and function annotation solution to extract & analyze biological molecules relationships from public databases of biological molecules and signification. MAS uses relational database of biological networks created from millions of individually modeled relationships between genes, proteins, complexes, cells, disease and tissues. MAS allows a view on your data, integrated in biological networks according to different kinds of biological 105 context. This unique feature results from multiple lines of evidences which are integrated in MASCORE. MAS helps to understand relationship of gene expression data through the given molecular symbols list, and provides thorough, unbiased and visible results. The primary databases of MAS integrated various well-known biological resources such as Gene Ontology, KEGG, BioCarta, GenMapp, HPRD (http://www.hprd.org/), MINT 110 (http://mint.bio.uniroma2.it/mint/Welcome.do), BIND (http://www.blueprint.org/), Intact (http://www.ebi.ac.uk/intact/), UniGene (www.ncbi.nlm.nih.gov/UniGen), OMIM (http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?db=OMIM). MAS offers various query entries and graphics result. The system represents
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