<i>RRM2</i> Computational Phosphoprotein Network Construction and Analysis between No-Tumor Hepatitis/Cirrhotic Liver Tissues and Human Hepatocellular Carcinoma (HCC)

Abstract

RRM2 computational phosphoprotein network construction and analysis of human hepatocellular carcinoma (HCC) is very useful to identify novel markers and potential targets for prognosis and therapy. By integration of gene regulatory network infer (GRNInfer) and the database for annotation, visualization and integrated discovery (DAVID) we identified and constructed significant molecule RRM2 phosphoprotein network from 25 no-tumor hepatitis/cirrhotic liver tissues and 25 HCC patients in the same GEO Dataset GSE10140-10141. We gained the negative result of RRM2 phosphoprotein module through the net numbers of activation minus inhibition compared with no-tumor hepatitis/cirrhotic liver tissues and predicted possibly the decrease of RRM2 phosphoprotein module in HCC. Our integrative result showed that RRM2 phosphoprotein cluster of HCC contained both in human no-tumor hepatitis/cirrhotic liver tissues and HCC terms of phosphoprotein (with RRM2) and cell cycle (without RRM2), only in HCC terms of cell-cell signaling, cell projection part, glycoprotein, cell projection, cell adhesion, biological adhesion, integral to plasma membrane, plasma membrane, kinase and phosphorus metabolic process (without RRM2), and none in HCC terms of cell death (without RRM2) and ion binding (with RRM2) compared with human no-tumor hepatitis/cirrhotic liver tissues, all the condition is vital to invasion of HCC. Therefore, we deduced the weaker RRM2 phosphoprotein function in HCC consistent with our above computation. It would be necessary of RRM2 phosphoprotein function decrease to invasion of HCC. RRM2 phosphoprotein interaction module construction in HCC can be a new route for studying the pathogenesis of HCC.

MYBPC1 Computational Phosphoprotein Network Construction and Analysis between Frontal Cortex of HIV encephalitis (HIVE) and HIVE-Control Patients

“…We have already done some studies in this relative field about gene network construction and analysis presented in our published articles (Sun et al , 2008Wang et al 2010aWang et al , b, 2009a. Based on integrated gene regulatory network infer method by linear programming and a decomposition procedure with analysis of the significant function cluster using kappa statistics and fuzzy heuristic clustering from the database for annotation, visualization, and integrated discovery, we constructed significant molecule MYBPC1 phosphoprotein network and identified only terms in HIVE-control, only terms in HIVE, both in HIVEcontrol patients and HIVE, respectively.…”

Interferon α-Inducible Protein 27 Computational Network Construction and Comparison between the Frontal Cortex of HIV Encephalitis (HIVE) and HIVE-Control Patients~!2010-04-06~!2010-08-05~!2010-09-06~!

“…We have previously published work in this field about gene network construction and analysis [6,[11][12][13][14][15] By integration of gene regulatory network infer (GRNInfer) and the database for annotation, visualization and integrated discovery (DAVID 2010 version) we constructed a significant molecular IFI27 network and compared IFI27 up-and downstream gene numbers for activation and inhibition between HIVE-control patients and HIVE (Table 1). Specifically, IFI27 downstream networks showed no results in the frontal cortex of HIVE-control patients and the frontal cortex of HIVE.…”

Low BIK outside-inside-out interactive inflammation immune-induced transcription-dependent apoptosis through FUT3-PMM2-SQSTM1-SFN-ZNF384

“…d BIK-activatory different mutualpositive-correlation molecules in low normal adjacent tissues compared with the corresponding high lung adenocarcinoma. Con, human normal adjacent tissues; Ex, lung adenocarcinoma; Act, activation Immunol Res out interactive inflammation immune-induced transcription-dependent apoptosis using SAM [7][8][9][10][11][12]. The GSE7670 raw microarray data were processed by log base 2, two classes were paired, and minimum fold change C2 selected (the false-discovery rate 0 %) [13][14][15][16].…”