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Construction and Analysis of Three Networks of Genes and Micrornas in Adenocarcinoma

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ONCOLOGY LETTERS 10: 3243-3251, 2015 Construction and analysis of three networks of genes and microRNAs in adenocarcinoma JIAHUI NING1,2, XIAOXIN GUO1,2, NING WANG1,2 and LUCHEN XUE1,2 1Department of Computer Science and Technology; 2Key Laboratory of Symbol Computation and Knowledge Engineering of The Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China Received October 19, 2014; Accepted July 28, 2015 DOI: 10.3892/ol.2015.3676 Abstract. Adenocarcinoma is one of the most serious diseases which revealed the regulatory associations between genes that threaten human health. Numerous studies have inves- and miRNAs. The present study clearly revealed components tigated adenocarcinoma and have obtained a considerable of the pathogenesis of adenocarcinoma and the regulatory amount of data regarding genes and microRNA (miRNA) in associations between the elements in adenocarcinoma. The adenocarcinoma. However, studies have only focused on one present study may aid the investigation of gene therapy in or a small number of genes and miRNAs, and the data is stored adenocarcinoma and provides a theoretical basis for studies in a scattered form, making it challenging to summarize and of gene therapy methods as a treatment for adenocarcinoma. assess the associations between the genes and miRNAs. In the present study, three networks of genes and miRNAs in Introduction adenocarcinoma were focused on. This enabled the construc- tion of networks of elements involved in adenocarcinoma and Adenocarcinoma is a malignant tumor of the glands of epithe- the analysis of these networks, rather than only discussing one lial tissues that grows invasively and is not easy to resect (1). gene. Transcription factors (TFs), miRNAs, and target and The rate of lymph node metastasis is relatively high in adeno- host genes of miRNAs in adenocarcinoma, and the regula- carcinoma and may reach 36%. Relapse occurs easily, which tory associations between these elements were identified leads to a poor prognosis (2). Previous studies have demon- in the present study. These elements and associations were strated that the differentially-expressed genes and miRNAs then used to construct three networks, which consisted of the of adenocarcinoma may affect the development, transfer and differentially-expressed, associated and global networks. The treatment of cancer (3-7). In addition, there is a complex regu- similarities and differences between the three networks were latory network between the TFs, miRNAs, and target and host compared and analyzed. In total, 3 notable TFs, consisting genes in adenocarcinoma (3). Through these studies, a novel of TP53, phosphatase and tensin homolog and SMAD4, method of studying cancer may be produced. The present were identified in adenocarcinoma. These TFs were able to study aimed to investigate the elements and regulatory asso- regulate the differentially-expressed genes and the majority ciations between the elements in adenocarcinoma to increase of the differentially-expressed miRNAs. Certain important knowledge with regards to the pathogenesis, development and regulatory associations were also found in adenocarcinoma, gene therapy of adenocarcinoma. in addition to self-regulating associations between TFs and TFs are a type of protein that promotes or suppresses the miRNAs. The upstream and downstream elements of the transcription of genes by binding to the upstream regions of differentially-expressed genes and miRNAs were recorded, genes (8). TFs may regulate the transcription of genes indi- vidually or in combination with other proteins (8). MicroRNAs (miRNAs) are short non-coding RNA sequences that demonstrate regulatory functions (8). At present, the biological function of certain miRNAs has been Correspondence to: Professor Xiaoxin Guo, Department confirmed, which has revealed the notable regulatory role of Computer Science and Technology, Jilin University, played by miRNA in the growth, differentiation and apop- 2699 Qianjin Street, Changchun, Jilin 130012, P.R. China tosis of cells and the developmental process of disease (4). A E-mail: [email protected] decrease in miRNA levels is often observed in human cancers, Abbreviations: miRNA, microRNA; TFs, transcription factors; indicating that miRNA may possess an intrinsic function NCBI, National Center for Biotechnology Information; TFBSs, of tumor suppression (9). In addition, miRNAs participate transcription factor binding sites in the adjustment of cancer development by adjusting the target genes of the miRNA, which has been assessed in Key words: three networks, transcription factors, microRNA, target previous studies (4,10-12). O'Donnell et al revealed that the genes, host genes, adenocarcinoma mir-17-92 gene may be associated with tumors, and also identi- fied that mir‑17‑92 regulates MYC by regulating E2F1 (4). 3244 NING et al: CONSTRUCTION AND ANALYSIS OF THREE NETWORKS IN ADENOCARCINOMA Host genes are the genes that code for miRNAs. A components of the present study. The genes and miRNAs were considerable number of miRNAs have been identified in the then identified, as they were required to construct the three introns of host genes, and these miRNAs were termed intronic networks in the present study. miRNAs (5). The intronic miRNAs are transcribed in parallel Differentially‑expressed genes. The differentially-expressed with the host genes (5,6). Intronic miRNAs and the host genes genes in adenocarcinoma were obtained from the NCBI snp usually act as potential partners to achieve biological func- database (21), KEGG pathway database (18) and relevant tion and affect the alteration of pathways (7). These previous literature. studies have indicated that miRNA and the miRNA host genes Differentially‑expressed miRNAs. The Harbin Institute of may affect the development of cancer. Technology miR2Disease database (19) is a manually created The presence of regulatory associations between TFs, database of differentially-expressed miRNAs in various miRNAs, and the target and host genes of miRNA in adeno- human diseases. This database was used in the present study to carcinoma has been determined from the aforementioned identify differentially-expressed miRNAs in adenocarcinoma. understanding. These elements may affect the development In addition, differentially-expressed miRNAs and associated of cancer, and a considerable number of studies have investi- miRNAs in adenocarcinoma were identified using the relevant gated these regulatory associations (3-7). From these previous literature (20,22-29). Following the collection of these data, studies, it has been revealed that miRNAs regulate TFs and TFs differentially-expressed miRNAs from miR2Disease and the regulate miRNAs (13). It has also been demonstrated that TFs relevant literature were summarized as differentially-expressed and miRNA may regulate genes (8) and miRNA may regulate miRNAs in adenocarcinoma. The differentially-expressed and target genes (10). In addition, the data of numerous studies associated miRNAs in adenocarcinoma were summarized have been combined to form various databases, including from the relevant literature, and were classified as the associ- TransmiR (13), computational predicted methods (14), experi- ated miRNAs in adenocarcinoma. mentally validated databases (15,16), miRBase (17), the KEGG Associated genes. The associated gene network in adeno- pathway database (18), the miR2Disease database (19) and the carcinoma consisted of 4 components. Firstly, certain GeneCards database (20). In these databases, a large amount of differentially-expressed genes in adenocarcinoma were identi- data may be found, which was the basis for the present study. fied using theNCBI snp database, KEGG pathway database and In the present study, TFs, miRNAs, and the host and target relevant literature (30-34). These genes formed a component genes of miRNAs in adenocarcinoma were collected and of the associated gene network in adenocarcinoma. Secondly, analyzed. The aim of the present study was to identify the genes were identified in adenocarcinoma using the GeneCards networks surrounding various elements in adenocarcinoma database (35). The genes with a relevance score >0.8, according and to analyze these networks. Data were manually collected to the GeneCards database, were extracted as a component for adenocarcinoma, consisting of the differentially-expressed of the associated gene network for adenocarcinoma. Thirdly, genes and miRNA, associated genes and miRNA, and the 1,000-nt promoter region sequences of the target genes of host and target genes in adenocarcinoma. The regulatory differentially-expressed genes were obtained from the Univer- associations between the elements in adenocarcinoma were sity of California, Santa Cruz database (36). The P-match also recorded. This data formed the basis of follow-up assess- method, which combines pattern matching and weight matrix ments. Subsequent to the collection of various data, this data approaches, was then used to identify transcription factor was used to construct three networks, which consisted of binding sites (TFBSs) in 1,000-nt promoter region sequences the differentially-expressed, associated and global networks. and to map TFBSs onto the promoter region of target genes. This However, the global network was so complex that no useful method identified certain associated genes that corresponded data was obtained. The differentially-expressed and associ- with miRNAs through target genes. These associated genes ated networks were considered to
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