1. Network analysis approach to compare the gene expression profiling for Hepatocellular carcinoma with and without viral infection INTRODUCTION Hepatocellular carcinoma (HCC) is a primary malignancy of liver and account second most malignancy related deaths worldwide. In particular, HCC is the fifth and ninth common cancer in male and female respectively. Hepatocellular carcinoma (HCC) is the fifth most common malignancy in men and the ninth in women (Figure 1, 2). It’s the second most common cause of cancer-related death in the world because of its very poor prognosis and of consequent high mortality ratio of 0.95. Present medical treatment as well as liver transplants often leave very short survival time. Sakshi 1, Giovanni Colonna 2, Giovanni Di Bernardo 3, Marcella Cammarota 3, Giuseppe Castello 4, Susan Costantini 4 1 Dottorato in Biologia Computazionale, Dipartimento di Biochimica, Biofisica e Patologia generale, Seconda Università degli Studi di Napoli, Napoli, Italy 2 Servizio di Informatica Medica, Azienda Ospedaliera Universitaria, Seconda Università di Napoli, Napoli, Italy 3 Dipartimento di Medicina Sperimentale,Seconda Università degliStudi di Napoli, Napoli, Italia 4 CROM, Istituto Nazionale Tumori “Fondazione G. Pascale” - IRCCS, Napoli, Italia OBJECTIVE In this study we compared the microarray evaluation of gene expression of the human hepatocyte cancerous cell line (HepG2) in comparison to normal human hepatocytes with that from liver tissues with different diseased conditions (HCV, HCV-related cirrhosis and HCC with HCV-related cirrhosis) in comparison to normal liver tissue obtained from public datasets. We performed the network analysis on differentially expressed genes among HCV, HCV- related cirrhosis and HCC with HCV-related cirrhosis and HepG2 cell-line. METHODS We extracted the gene expression data obtained from 40 normal liver tissues and from liver tissues of 61 HCV, 17 HCV-related cirrhosis and 107 HCC with HCV-related cirrhosis patients from the publicly available database of affymatrix from E-MTAB-950 dataset (www.ebi.ac.uk/arrayexpress). We used Robust Multi-array Average or Robust Multi-chip Average (RMA) for the normalization and selected the up and down expressed genes concerning a fold change value equal to 2 and -2, respectively, comparing gene expression data in HCV or HCV- related cirrhosis or HCC with HCV-related cirrhosis patients compared to healthy controls. In details, the RMA normalization begins with a computing background corrected perfect match intensities for each perfect match cell on every gene chip. Then, we re-analyzed the microarray data obtained recently in our laboratory on normal hepatocytes and hepatoma cell line (HepG2) using DESeq tool in R package and a fold change value equal to 2 and -2, to select the up- and down-expressed genes in HepG2 cells compared to normal hepatocytes. We used DESeq as the number of samples were comparatively low in illunima microarray experiment of HepG2 cell-line (Figure 4).www.ebi.ac.uk/arrayexpress RESULTS CONCLUSIONS  In HCC with HCV-related cirrhosis network, SUMO2, SUMO1, FN1, KIAA0101 and COPS5 are among high degree nodes with 421, 269, 209, 205 and 194 degree respectively.  The hub nodes in this network are playing important role in protein sumoylation, cellular protein localization and intracellular protein transport.  In HepG2 network, AURKA, PCNA, ACTB, AURKB and CDC20 are among high degree nodes with degree of 45, 39, 35, 35 and 27 respectively.  The hub nodes in HepG2 network are found to mainly involve in hydrolase activity and protein binding process. The dysfunctional protein kinase activity plays an important role in affecting ester hydrolases and ​ ester hydrolases are shown to be regulated by an endogenous cAMP-dependent protein kinase activity. RESULTS Presented by: Sakshi Dipartimento di Biochimica, Biofisica e Patologia generale Seconda Università degli Studi di Napoli, Napoli, Italy E-mail address: kushi.sa@gmail.com Statistical analysis of seed network HCV network HCV related Cirrhosis network HCC with HCV etiology network HepG2 network HepG2 network (1 st order) Nodes1708141917562506509 Interactio ns 11452825915420754220381 Network centraliza tion 0.1320.1290.2300.1570.745 Average neighbors 13.4111.64117.5636.0367.715 Network heterogene ity 1.4411.4671.4821.1191.581 Characteri stic path length 3.2963.2653.0113.6422.336 Clustering coefficient 0.2560.2340.2690.2780.331 Network Density 0.008 0.0100.020.01 Twelfth Annual Meeting of the Bioinformatics Italian Society June 3-5, 2015, Milan, Italy Figure 3: Risk factors for the development of liver cirrhosis with subsequent hepatocellular carcinoma. (Courtesy Current Status in the Therapy of Liver Diseases. Philipp Uhl, Gert Fricker, Uwe Haberkorn and Walter Mier.) Figure 4. The DESeq analysis of HepG2 cell-line expressed genes. The red dots show the significant expressed genes. Figure 5: Venn diagram of all cases showing exclusive and recurrent genes in HCV (2748) HCV Cirrhosis (2187) HCC with HCV etiology ( 2471) and HepG2 (651) different cases. (Courtesy VENNY2.0 by Juan Carlos Oliveros Computational Genomics, CNB-CSIC). Table 1: The number of differentially expressed genes in different diseases cases. Table 2: The statistical analysis of different diseased networks. Figure 6: Network of differentially expressed genes of HepG2 cell-line. Figure 7: The sub network of hub nodes of HepG2 cell-line differentially expressed genes. The degree distribution and betwenness distribution of the HepG2 differentially expressed network. Figure 8: The first order network of HepG2 cell-line differentially expressed genes. The degree distribution and betweenness distribution of the network depicting the scale free property of the network. Figure 1: Estimated Liver Cancer Mortality Worldwide in 2012 in Men. Figure 2: Estimated Liver Cancer Mortality Worldwide in 2012 in Women. The chronic viral infection of hepatitis B and C virus (HBV and HCV), consumption of alcohol and smoking are the main factors that trigger liver diseases and HCC (Figure3). The obesity and type 2 diabetes are also known to be a causative agents for HCC through non-alcoholic fatty live or fatty liver disease. Moreover the exposure to vinyl chloride or polyvinyl chloride make people more susceptible for this type of cancer. However, in literature it is also reported that the iron load and estrogen-progesterone combined oral contraceptives (OC) are also known to increase the risk of HCC. HCC diffusion changes on geographical regions, ethnic groups, sex group and environmental conditions. REFERNCES J. Ferlay, I. Soerjomataram I, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman D, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.,” Int. J. Cancer, vol. 00, Sep. 2014. S. Jonas, W. O. Bechstein, T. Steinmüller, M. Herrmann, C. Radke, T. Berg, U. Settmacher, and P. Neuhaus, “Vascular invasion and histopathologic grading determine outcome after liver transplantation for hepatocellular carcinoma in cirrhosis,” Hepatology, vol. 33, no. 5, pp. 1080–1086, 2001. S. Costantini, G. Di Bernardo, M. Cammarota, G. Castello, and G. Colonna, “Gene expression signature of human HepG2 cell line.,” Gene, vol. 518, no. 2, pp. 335–45, Apr. 2013. A. Marshall, M. Lukk, C. Kutter, S. Davies, G. Alexander, and D. T. Odom, “Global gene expression profiling reveals SPINK1 as a potential hepatocellular carcinoma marker.,” PLoS One, vol. 8, no. 3, p. e59459, Jan. 2013. M. L. Bailey and W. McLean Grogan, “Protein kinase-mediated activation of temperature-labile and temperature-stable cholesteryl ester hydrolases in the rat testis,” J. Biol. Chem., vol. 261, no. 17, pp. 7717–7722, 1986. The differentially expressed genes in different diseased datasets provided with different number of genes (Table 1). The Venn analysis explained the number of genes exclusively and commonly differentially expressed among different diseased datasets. The network analysis of HepG2 cell- line differentially expressed genes with Cytoscape shown in (Figure 6). The hub nodes of this network provided the most important nodes of the network explaining the influence of these nodes on the rest of the network. The degree distribution of this network follow the power law implying the characteristic property of scalefreeness of the network (Figure 7). The hub nodes of HepG2 seed network included AURKA, ACTB, PCNA and other very high degree nodes as compared to other nodes in network. The red color show the high degree nodes while yellow and green medium and low degree nodes respectively. Then, the first order network of hepg2 cell-line differentially expressed genes was performed along with other diseased networks. The statistical analysis of these networks are given in (Table 2). The gene ontology studies of hub nodes of these network showed their involvement in many molecular processes like Purine–specific mismatch base pair DNA N-gylcosylase activity, enzyme binding, MutLalpha complex binding and DNA insertion or deletion binding.