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Regulatory Network Analysis of Genes and Micrornas in Human Hepatoblastoma

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Regulatory Network Analysis of Genes and Micrornas in Human Hepatoblastoma ONCOLOGY LETTERS 12: 4099-4106, 2016 Regulatory network analysis of genes and microRNAs in human hepatoblastoma JIMIN HE1,3, XIAOXIN GUO1-3, LINLIN SUN2,3, NING WANG1,3 and JIWEI BAO2,3 1College of Computer Science and Technology; 2College of Software; 3Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China Received March 12, 2015; Accepted January 11, 2016 DOI: 10.3892/ol.2016.5196 Abstract. Hepatoblastoma (HB) is a common type of primary Homo sapiens (hsa)-miR-221, hsa-miR-18a and hsa-miR-17-5p, tumor in children. Previous studies have examined the expression but no miRNAs targeted MYCN. In conclusion, the pathways of genes, including transcription factors (TFs), target genes, host and mechanisms underlying HB were expounded in the present genes and microRNAs (miRNAs or miRs) associated with HB. study, which proposed a fundamental hypothesis for additional However, the regulatory pathways of miRNAs and genes remain studies. unclear. In the present study, a novel perspective is proposed, which focuses on HB and the associated regulatory pathways, Introduction to construct three networks at various levels, including a differ- entially expressed network, an associated network and a global Hepatoblastoma (HB) is the most common hepatic malig- network. Genes and miRNAs are considered as key factors in the nancy in children, and accounts for ~1% of all childhood network. In the three networks, the associations between each tumors (1). Usually, the disease is diagnosed during the first pair of factors, including TFs that regulate miRNAs, miRNAs three years of a child's life (2). The etiology of HB is unclear, that interact with target genes and miRNAs that are located at but has been associated with Beckwith-Wiedemann syndrome, host genes, were analyzed. The differentially expressed network familial adenomatosis polypi and low birth weight (3). The is considered to be the most crucial of the three networks. All primary treatment for HB is surgical resection, and the use factors in the differentially expressed network were mutated or of pre-resection chemotherapy may increase the likelihood differentially expressed, which indicated that the majority of the of a resectable tumor (3). Therefore, the pathogenesis of HB factors were cancerogenic factors that may lead to HB. In addi- requires urgent understanding. tion, the network contained numerous abnormal linkages that Differentially expressed genes and microRNAs (miRNAs may trigger HB. If the expression of each factor was corrected to or miRs) have been previously indicated to be crucial in HB, a normal level, HB may be successfully treated. The associated affecting the development, metastasis and treatment of this network included more HB-associated genes and miRNAs, and disease (4). HB-associated factors have less effect, compared was useful for analyzing the pathogenesis of HB. By analyzing with differentially expressed factors (5). Despite the large these close associations, the first and the last factor of the regula- number of genes and miRNAs that have been reported to tory pathways were revealed to have important roles in HB. For affect HB, the association between them remains unclear. example, v-myc avian myelocytomatosis viral oncogene neuro- Transcription factors (TFs), miRNAs and target genes blastoma derived homolog (MYCN) was observed to regulate construct an intricate regulatory network that provides a novel opportunity to study HB (6). TFs and miRNAs are prominent regulators of gene expression. Gene regulatory factors are predominantly comprised of TFs and miRNAs, which control the expression of genomic information in multicellular genomes (7). Correspondence to: Professor Xiaoxin Guo, College of Computer TFs are proteins that bind to specific DNA sequences, thus Science and Technology, Jilin University, 2,699 Qianjin Street, controlling the transfer of genetic information between DNA Changchun, Jilin 130012, P.R. China and messenger (m)RNA (8). In the three networks at various E-mail: [email protected] levels described in the present study, TFs regulate miRNAs and miRNAs target other genes. As a result, TFs influence the Abbreviations: HB, hepatoblastoma; miRNA, microRNA; TF, transcription factor; TFBs, transcription factors binding sites; NCBI, expression of genes via miRNAs. miRNAs are endogenous National Center for Biotechnology Information; FFL, feed-forward RNAs of ~23 nt in length that are crucial for gene regulation, as loop they interact with the mRNAs of protein-coding genes to control their post-transcriptional repression (9). Magrelli et al (10) Key words: hepatoblastoma, transcription factors, host gene, demonstrated that miRNAs associated with carcinogenesis microRNA, target genes, network may play a pivotal role in the onset of HB. TFs and miRNAs interact with each other (11). Wang et al (12) reported that TFs regulate the expression of miRNAs, and miRNAs regulate the 4100 HE et al: NETWORK OF GENES AND miRNAS IN HEPATOBLASTOMA transcription of TFs. This complex association is included in the identity. The miRNA and host gene data were included in the networks of the present study. dataset D3. Target genes are targeted by miRNAs (13). Naeem et al (14) Extraction of data regarding genes and miRNAs. The indicated that miRNAs may exert a widespread impact on the differentially expressed genes in HB were collected from the regulation of target and non-target genes. At present, miRNAs Cancer GeneticsWeb (www.cancerindex.org/geneweb), the are known to target numerous genes (15). These target genes, NCBI database of single nucleotide polymorphism (dbSNP) for which abundant information may be obtained from available (www.ncbi.nlm.nih.gov/snp) and pertinent literature in the databases and pertinent literature (16), may provide essential Science Citation Index (22). The differentially expressed evidence for the discovery of the biological role of miRNAs (17). genes were termed dataset D4. The associated genes were Host genes are genes where miRNAs are located. then extracted from GeneCards® (www.genecards.org) and Rodriguez et al (18) suggested that the transcriptional patterns pertinent literature. of all miRNA host genes were derived from a variety of sources Predicted TFs were identified from target genes of the that illustrate the spatial, temporal and physiological regula- differentially expressed miRNAs. The TFs were extracted tion of miRNA expression. This theory indicates that miRNAs using the P-Match method (http://www.gene-regulation.com/ are transcribed in parallel with their host transcripts, and that pub/programs.html#pmatch), an algorithm that operates on the the two different transcription classes of miRNAs identified concepts of pattern matching and weight matrix approaches in (‘exonic’ and ‘intronic’) may require slightly different mecha- order to identify transcription factor binding sites (TFBSs) in nisms of biogenesis (18). The cooperation of miRNAs with the DNA sequences (23). The genes that appeared in TransmiR were host gene may affect the disease process (19). In the present regarded to be HB-associated genes, and were further analyzed. study, the host genes are considered to be mutated and involved In total, 1,000 nt promoter region sequences of the targets in the progression of cancer when their corresponding miRNA of differentially expressed genes were downloaded from the is differentially expressed. University of California Santa Cruz database (24). P-Match was From the experimental data obtained in the present used to combine pattern matching and weight matrix approaches study, the differentially expressed genes and miRNAs were in order to identify the TFBSs in 1,000 nt promoter region concluded to have a paramount impact on HB. In addition, the sequences. The TFBSs were mapped onto the promoter regions associated genes and miRNAs also exert certain effect on HB. of the targets. The P-Match matrix library consists of a set of In the present study, the underlying networks of HB were known TFBSs that were collected from the TRANSFAC® data- assessed with respect to miRNAs, target genes, TFs, host base (http://www.gene-regulation.com/pub/databases.html), genes of miRNAs and the associations between these factors thus providing the possibility to search for large numbers of in human HB. Various data were collected, and the associa- differentially expressed TFBSs. The differentially expressed tions between the aforementioned factors were revealed to be genes that were obtained were also associated genes. The data intricate. The differences and similarities between these regarding differentially expressed genes and associated genes factors were compared, and significant associations were were termed dataset D5. extracted for analysis, in order to aid the understanding of the The differentially expressed miRNAs were extracted pathogenesis of HB. from pertinent literature and miR2Disease (25,26), which is a manually curated database regarding differentially expressed Materials and methods miRNAs in HB. The differentially expressed miRNAs were termed dataset D6. The associated miRNAs were collected Material collection and data processing from pertinent literature. The differentially expressed miRNAs Extraction of the regulatory associations between miRNAs and were also associated miRNAs. The associated miRNAs target genes. The interactions between human miRNAs and collected were termed dataset D7. target genes were extracted from two databases, TarBase
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