1. A meta-analysis for gene expression profiling in hepatocellular carcinoma (HCC) with and without compliances.
Sakshi (1), Costantini S(1,2), Colonna G(3)
(1)Dottorato di ricerca in Biologia Computazionale, Dipartimento di Biochimica, Biofisica e Patologia generale, Seconda Università degli Studi di Napoli
(2) Istituto Nazionale Per Lo Studio E La Cura Dei Tumori “Fondazione Giovanni Pascale”, IRCCS, Italy
(3)Center of Medical Informatics – AOU – Second University of Naples
Introduction
Network Analysis
Functional Analysis
Hepatocellular carcinoma (HCC) is the primary malignancy of liver which accounts for 0.5 million deaths around the world per year. Microarray studies can be very useful to identify novel functional genes and subgroups involved in the development and progression of this cancer. The evaluation of recurrent and exclusive differentially expressed genes among different stages of hepatitis C virus (HCV) infection, HCV-related cirrhosis, and HCC with HCV-related cirrhosis 1 by studies on tissues and on HCC cell line without viral compliance (HepG2) compared to normal hepatocytes2 indicates their involvement in specific biological functions and important metabolic pathways
Our studies evidenced the high expression of tumor necrosis factor (TNF) receptor in all cases that promotes apoptosis via different pathways. Gene ontology studies of up-expressed genes showed their involvement in various vital functions like cell proliferation, neuroblast proliferation, DNA replication and cell-cell adhesion in HCC tissues. Apart from these, we also found genes involved in the various phases of the cell cycle. The down-expressed genes are mostly involved in complement activation, immune response, activation of plasma proteins involved in acute inflammatory response, oxidation-reduction processes, acute inflammation and in response to wounding. This analysis also predicts the hub nodes like Small Ubiquitin-Related Modifier 2 and 1 (SUMO2,1) that play important role in post translational modifications system and Fibronectin 1 (FN1) in highly involved in metastasis.
HCC with HCV etiology network is constructed with differentially expressed genes by extracting interactions from human interactome. The network in figure 1 present the connected component of seed network of HCC with HCV etiology. The degree distribution is presented with color scheme while the betweenness of the nodes is represented with size of the nodes. The colors red, yellow and green represents high degree, intermediate degree and low degrees respectively and bigger size of node represent the greater betweenness of nodes in the network. in this network the genes SUMO2, SUMO1, FN1, KIAA0101, COPS5, MDM2 and FBXO6 are high degree nodes ranging from 421 to 147 degree in the network and these nodes also exhibit very high betweenness centrality predicting the importance of these nodes in the following network. The degree distribution of this network follows the power law that predicts the scalefree property of this network.
Aim
Aim of our research is to verify what genes are differentially expressed both in HCC cells and in human liver tissues and what are specific for presence of HCC or virus/cirrhosis.
Methodology
We collected microarray experiments from liver tissues of 270 patients ( between which the authors had excluded the patients with liver transplant by publically available dataset E-MTAB-950. The dataset contained samples with only HCV (61 patients), with HCV-related Cirrhosis (17 patients) and with HCC and HCV-related Cirrhosis (107 patients), and samples for 40 normal healthy individuals.
Scheme flow-chart
Figure 1: The spring embedded representation of connected component of seed network of Hepatocellular carcinoma with hepatitis C virus etiology. A. B.
Future perspective
Understand the global architecture of these genes using Protein-protein interaction (PPI) and gene – gene interaction networks.
The network analysis of Hepatitis C virus differentially expressed genes and hepatitis C virus related Cirrhosis.
Prediction of Hub nodes in different networks to predict the genes playing role in disease specific case and common in all cases.
Differentially expressed genes C. D.
We obtained the number of up and down expressed genes in Hepatitis C virus , Hepatitis C virus related cirrhosis and Hepatocellular carcinoma with Hepatitis C virus etiology (Table1), considering the fold change value > or < 2 in all cases.
Table 1: The number of differentially expressed genes in HCV, HCV related Cirrhosis and HCC with HCV etiology.
References E.
Conditions
Up expressed genes
Down expressed genes
HCV
2475
291
HCV related Cirrhosis
2185 33
HCC with HCV etiology
2324
166
1. Marshall A, Lukk M, Kutter C, et al. Global gene expression profiling reveals SPINK1 as a potential hepatocellular carcinoma marker. PLoS One. 2013;8(3):e59459.
2. Costantini S, Di Bernardo G, Cammarota M, Castello G, Colonna G. Gene expression signature of human HepG2 cell line. Gene. 2013;518(2):335–45.
3. Y GP, Bauer JH, Haridas V, et al. Identification and functional characterization of DR6, a novel death domain-containing TNF receptor. febs Lett. 1998;431(3):351–356.
Figure 2: (A) Degree distribution. (B) Clustering coefficient distribution.
(C) Closeness centrality distribution. (D) Betweenness centrality distribution.
(E) Topological coefficient distribution.