Bioinformatics Analysis Of Differentially Expressed Genes In Epithelial Ovarian Cancer

Ovarian cancer is one of the common malignant tumors of the female reproductive system. Although the incidence rate of ovarian cancer is 2.4%-5.6%, being the second highest incidence rate among malignant tumors of female genital system, its mortality rate of ovarian cancer ranks the first among the Gynecologic Oncology, which is 85-90%. Since the molecular mechanisms of ovarian cancer is still unclear, it has a high degree of malignancy and low survival rate. Therefore, study on the mechanism of ovarian cancer from the perspective of molecular biology has important clinical significance for ovarian cancer prevention, early diagnosis and treatment. The development and application of bioinformatics provides a new approach to the study of disease.Objective:This study is based on bioinformatics analysis of epithelial ovarian cancer microarray, to screen differentially expressed genes related to the occurrence and development of epithelial ovarian cancer, and to analyze the functional annotation, pathway analysis and protein interaction network of differentially expressed genes related to ovarian cancer, meanwhile the miRNA of differentially expressed genes were screened by the method of combined biological information and literature mining, and to analyze the interaction between them, to provide important information for a better understanding the molecular mechanisms of epithelial ovarian cancer, and to provide a new direction for further study of the occurrence and development of epithelial ovarian cancer.Methods:Screening expression of epithelial ovarian cancer microarray data as the research object from the GEO database, using the R language and Bioconductor packages to preprocess the data,using empirical Bayes test screening of epithelial ovarian cancinoma differentially expression gene; GO functional annotation and KEGG pathway analysis of differentially expressed genes by using DAVID online analytical database; STRING database was used to draw differentially expressed protein encoded by gene interaction network diagram (PPI), and the Cytosacpe software was used to calculate the protein interaction network and the topological characteristics of each node, each module using MCODE plugin throughout the network of interation is closely related to the function module were analyzed, using BiNGO plugin of biological process mining, screening in the high stability of the network play an important role in connecting with high degree of node genes; using WebGestalt online analysis platform to obtain the orresponding miRNA of differentially expressed genes, and using Cytosacpe software to build PPI-miRNA interaction network, calculate the topological characteristics of the network and nodes of the network, filter out the important role played in the stability of the network connection of high node miRNA.Results:This study using the R language and Bioconductor packages analysis of epithelial ovarian cancer expression profiling data, found that the number of 4748 differentially expressed genes (FDR<0.05,| logFC|> 1), wherein the number of genes are upregulated 654 and downregulated genes 4094. GO functional annotation found upregulated differentially expressed genes of biological processes involved mainly in the cell cycle, cell cycle progression, cell cycle M phase, mitosis, signaling cascades within the cell, cell adhesion, and regulation of cell proliferation and other processes; there are major cellular components of intracellular non-membrane organelles, membrane fraction, cytoskeleton, chromosomes and extracellular matrix; molecular functions are mainly involved in microtubule motor activity, the structural components of the extracellular matrix protein kinase regulatory activity and the like. At the same time, the differences upregulated genes across KEGG pathway analysis found that these differentially expressed genes mainly involved in cell cycle, cell matrix receptor interaction, p53 signaling pathways focal adhesion, oocyte meiosis and cancer signaling pathways pathways. Downregulated differentially expressed genes of biological processes involved mainly in protein localization, protein transcription, protein degradation, intracellular signaling cascade, proteolysis procedural regulation of apoptosis and cellular macromolecules metabolism process; there are major cellular components of intracellular non-membrane organelles, cytoskeleton, mitochondria and insoluble debris; molecular functions are mainly involved in the translation of the main factor activity, nucleic acid binding, phospholipid binding, threonine type enzymatic activities. KEGG pathway analysis found that these down-regulated differentially expressed genes mainly involved in sphingolipid metabolism, endocytosis, focal adhesion, phagocytosis Fcγ receptor-mediated pathways, etc.By using the STRING database to analyze interaction between the protein encoded by the differentially expressed genes and construct related protein interaction network diagram, upregulated differentially expressed protein-coding gene construct networks found nine key genes, respectively CDK1, AURKA, CCNB1, CCNB2, BUB1, BUB IB, BIRC5, KIF11 and CENPE, using MCODE plugin to anslyze module clustering, there are two function modules(nodes>15),using BiNGO to analyze biological process, the biological processes are involved concentrated in mitosis, cell M phase, nuclear division and cell cycle regulation process; the downregulation of PPI network, differentially expression gene in larger multiples, high degree in protein interaction network of genes include ITLN1, BNC1, LHX2, ADH1B, UBC, ITGA, FYC, RPS6, RPLs etc., using MCODE plugin to anslyze module clustering, there are three function modules(nodes>30), using BiNGO to analyze biological process, the biological processes are involved process focused on gene expression, translation, LSU-rRNA maturation, cellular macromolecules metabolism, RNA processing, nuclear RNA splicing, RNA splicing, mRNA processing, protein modification and protein ubiquitination and other biological processes.Using WebGestalt online analysis platform to prediction the target miRNA of differentially expressed genes, and construct the miRNA-protein interaction network. Screened upregulated differentially expressed genes corresponding to 17 key target miRNA, respectively, miR-524, miR-124a, miR-506, miR-23a, miR-23b, miR-15a, miR-15b, miR-16, miR-195, miR-497, miR-424, miR-9, miR-27a, miR-27b, miR-29a, miR-29b, miR-29c. Down differentially expressed genes of the target miRNA and protein interaction network were screened out 23 key target miRNA, respectively, miR-19a, miR-19b, miR-200b, miR-200c, miR-429, miR-23a, miR-23b, miR-15a, miR-15b, miR-16, miR-195, miR-497, miR-424, miR-9, miR-130a, miR-130b, miR-301, miR-181a, miR-181b, miR- 181c, miR-181d, miR-493, miR-183.Upper and lower differentially expressed genes of the targer miRNA integration analysis, identify common coding miRNA have miR-23a、miR-23b、 miR-15a、miR-15b、miR-16、miR-195、miR-497、miR-424、miR-9.Conclusions:(1) Successfully screened out epithelial ovarian carcinoma differentially expressed genes, and carried on its GO analysis and pathway analysis to provide a theoretical basis for the study of biological processes of epithelial ovarian cancer.(2) Successfully constructed epithelial ovarian cancer genes differentially expressed protein interaction networks, selected multiple key genes of the differentially expressed genes encoding proteins in the whole network that involved in the process of epithelial ovarian cancer in the form a group of molecules, which is conducive to the further study of the interaction between the differentially expressed genes.(3) Screened out epithelial ovarian cancer differentially expressed gene that encoded together miRNA:miR-23a, miR-23b, miR-15a, miR-15b, miR-16, miR-195, miR-497, miR-424, miR-9 and most of them involved in cell proliferation, differentiation, apoptosis and chemoresistance, which is conducive to the further study of the upstream regulatory mechanism.