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CUL4B, NEDD4, and Ugt1as Involve in the TGF-Β Signalling in Hepatocellular Carcinoma

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CUL4B, NEDD4, and Ugt1as Involve in the TGF-Β Signalling in Hepatocellular Carcinoma 568 Qu Z, et al. , 2016; 15 (4): 568-576 ORIGINAL ARTICLE July-August, Vol. 15 No. 4, 2016: 568-576 The Official Journal of the Mexican Association of Hepatology, the Latin-American Association for Study of the Liver and the Canadian Association for the Study of the Liver CUL4B, NEDD4, and UGT1As involve in the TGF-β signalling in hepatocellular carcinoma Zhaowei Qu,* Di Li,** Haitao Xu,* Rujia Zhang,*** Bing Li,* Chengming Sun,* Wei Dong,* Yubao Zhang* * Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Tumor Hospital of Harbin Medical University, Harbin 150081, Heilongjiang Province, China. ** Department of Anesthesiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China. *** Operating Room, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China. ABSTRACT Introduction and Aim. TGF-β signalling is involved in pathogenesis and progress of hepatocellular carcinoma (HCC). This bioin- formatics study consequently aims to determine the underlying molecular mechanism of TGF-β activation in HCC cells. Material and methods. Dataset GSE10393 was downloaded from Gene Expression Omnibus, including 2 Huh-7 (HCC cell line) samples treated by TGF-β (100 pmol/L, 48 h) and 2 untreated samples. Differentially expressed genes (DEGs) were screened using Limma package (false discovery rate < 0.05 and |log2 fold change| > 1.5), and then enrichment analyses of function, pathway, and disease were performed. In addition, protein-protein interaction (PPI) network was constructed based on the PPI data from multiple databas- es including INACT, MINT, BioGRID, UniProt, BIND, BindingDB, and SPIKE databases. Transcription factor (TF)-DEG pairs (Bonferroni adjusted p-value < 0.01) from ChEA database and DEG-DEG pairs were used to construct TF-DEG regulatory network. Furthermore, TF-pathway-DEG complex network was constructed by integrating DEG-DEG pairs, TF-DEG pairs, and DEG-path- way pairs. Results. Totally, 209 DEGs and 30 TFs were identified. The DEGs were significantly enriched in adhesion-related func- tions. PPI network indicted hub genes such as CUL4B and NEDD4. According to the TF-DEG regulatory network, the two hub genes were targeted by SMAD2, SMAD3, and HNF4A. Besides, the 11 pathways in TF-pathway-DEG network were mainly en- riched by UGT1A family and CYP3A7, which were predicted to be regulated by SMAD2, SMAD3, SOX2, TP63, and HNF4A. Conclusions. TGF-β might influence biological processes of HCC cells via SMAD2/SMAD3-NEDD4, HNF4A-CUL4B/NEDD4, SOX2/TP63/HNF4A-CYP3A7, and SMAD2/SMAD3/SOX2/TP63/HNF4A-UGT1As regulatory pathways. Key words. Hepatocellular carcinoma. Differentially expressed genes. Transcription factors. Protein-protein interaction network. Transcription factor-target regulatory network. INTRODUCTION apoptosis, motility, and homeostasis is aberrantly regulat- ed in HCC.4 Cytokines like transforming growth factor β Hepatocellular carcinoma (HCC) is the primary liver (TGF-β) play important roles in HCC progression and cancer that mainly occurs in developing countries.1 The invasion. Reportedly, TGF-β is a multifunctional factor worldwide incidence of HCC ranks the seventh in wom- and plays bipartite roles in HCC:5,6 en (226,000 cases per year, 6.5% of all cancers) and the fifth in men (523,000 cases per year, 7.9% of all cancers).2 HCC • TGF-β suppresses tumor formation at early stage of is the main form of malignant liver cancer. Due to the liver damage. high occurrence of invasion to intrahepatic large vessels • TGF-β promotes HCC progression by inducing mi- and stomach, the 5-year survival rate for patients with croenvironment changes. HCC remains poor after major resection.3 In recent years, many studies have surveyed the patho- Moreover, TGF-β stimulation induces epithelial-mes- genesis of HCC. For example, Wnt signaling pathway enchymal transitions (EMT) in malignant cancers, pro- which is associated with cell differentiation, proliferation, moting cancer cell migration and invasion.7 Hoshida, et al. Manuscript received: August 06, 2015. Manuscript accepted: October 06, 2015. Mechanism of TGF-β effects on HCC. , 2016; 15 (4): 568-576 569 have found that TGF-β treatment (100 pmol/L, 48 h) quality of microarray data. Then, gcrma package was used enhances Wnt signaling via the intracellular pool of free to adjust the background of different chips, normalize the β-catenin in HCC cell line.8 Besides, transcription factors data in different chips, and transfer the expression values (TFs) play crucial roles in TGF-β stimulation via regulat- into log2 values. The nsFilter function in genefilter pack- ing gene transcription. For example, the modulation of age was utilized to delete the probes with no or little ex- SMAD family member 7 (SMAD7) expression influences pression value. Moreover, we transformed the expression the sensitivity of HCC cell lines (Huh7, FLC-4, HLE and values at probe level into expression values at gene level HLF) for cytostatic TGF-β effects, while the knockdown based on the annotation files in platform GPL3921. of TF Y-box binding protein-1 (YB-1) reduces TGF-β- induced SMAD7 expression in Huh7 cells.9 Despite these DEGs screening encouraging findings, the mechanism of TGF-β action in HCC cells has not been clearly illustrated. Limma package has been widely used to identify the This bioinformatics study was conducted to compre- genes which are differentially expressed between two hensively determine the underlying molecular mechanism groups. Thus, DEGs between Huh-7 cells with and of TGF-β activation in HCC cells by using the gene ex- without TGF-β treatment were identified by using pression profile up-loaded by Hoshida, et al. Consequent- Limma package (version 3.22.7, available at: http://www. ly, differentially expressed genes (DEGs) between HCC bioconductor.org/packages/3.0/bioc/html/limma.html).11 cells with and without TGF-β treatment were identified, The raw p-value produced in DEGs screening was adjust- DEG functions were investigated, and TF-pathway-DEG ed into false discovery rate (FDR) by using Benjamini- 12 complex network was constructed. The results of this Hochberg method. Then, FDR < 0.05 and |log2 study might provide novel directions for further HCC fold change (FC)| > 1.5 were used as the cut-off criteria studies and therapeutic targets for HCC treatment. (Figure 1). MATERIAL AND METHODS Enrichment analyses Microarray data Gene Ontology (GO) terms in GO database and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways Gene expression profile of GSE10393 deposited by in KEGG database are usually used to describe the biolog- Hoshida, et al.8 was downloaded from Gene Expression ical processes, molecular functions, and subcellular loca- Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) data- tions of protein.13 Functional enrichment analysis has base. Totally, GSE10393 included 2 Huh-7 (HCC cell been widely used to identify the functions that involve the line) samples with TGF-β treatment (100 pmol/L, 48 h) genes in a gene set, while pathway enrichment analysis and 2 Huh-7 samples without TGF-β treatment. The cor- is generally utilized to find the pathways that include the responding platform was GPL3921 [HT_HG-U133A] Af- genes in a gene set. For the identified DEGs, functional fymetrix HT Human Genome U133A Array. and pathway enrichment analyses were performed by us- ing clusterProfiler package (version 2.0.1, available at: Data preprocessing http://www.bioconductor.org/packages/3.0/bioc/html/ clusterProfiler.html)14 based on GO and KEGG databases. Based on the R statistical programming language, Bio- Besides, Disease Ontology (DO) terms can provide the conductor provides 1024 open software packages for the biomedical community with consistent, reusable and sus- analysis of high-throughput genomic data. Therefore, tainable descriptions of human disease terms, phenotype microarray data GSE10393 was preprocessed by using Bio- characteristics, and related medical vocabulary disease conductor package series (version 3.0, available at: http:// concepts.15 Similarly, DO term enrichment analysis www.bioconductor.org/packages/3.0/bioc/),10 including was conducted by using DOSE package (version 2.4.0, affy, simpleaffy, gcrma, and genefilter. In this study, affy available at: http://www.bioconductor.org/packages/3.0/ package was used to read the raw data of microarray, and bioc/html/DOSE.html).15 For these analyses, FDR < 0.05 simpleaffy package was utilized to detect and control the was used as the cut-off criterion. Expression value in Huh-7 cells with TGF-β treatment FC = Expression value in Huh-7 cells without TGF-β treatment Figure 1. The calculation formula for fold change of genes. FC: fold change. 570 Qu Z, et al. , 2016; 15 (4): 568-576 Protein-protein interaction (PPI) RESULTS network and its modules DEGs Proteins coded by genes often play their biological functions via interacting with other proteins. Thus, PPIs A total of 209 DEGs between Huh-7 cells with and associated with the identified DEGs were extracted from without TGF-β treatment were identified, including 109 the online databases, including the INDECT project up-regulated DEGs and 100 down-regulated DEGs. Advanced Image Catalogue Tool (INACT),16 Molecular INTeraction (MINT),17 Biological General Repository Enrichment analyses for Interaction Datasets (BioGRID),18 Universal Protein (UniProt),19 Biomolecular Interaction Network Totally, 107 GO functions, 11 KEGG pathways, and 2 Database (BIND),20 protein-ligand Binding DataBase DO terms were enriched by the DEGs (Table 1). The top (BindingDB)21 and Signaling Pathways Integrated Knowl- 10 enriched functions were mainly associated with cell ad- edge Engine (SPIKE)22 databases. Then, PPI network was hesion and glucose metabolism, while the 11 enriched conducted and visualized by using Cytoscape software pathways were mainly associated with drug, vitamin, and (version 3.2.1, available at: http://cytoscape.org/).23 Fur- saccharides metabolism. thermore, the algorithm GLay24 of clusterMaker plugin25 in Cytoscape was used to screen modules in the PPI net- PPI network and its modules work.
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