Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

• DOI: 10.2147/ott.s152238
• Publication Date: 2018-03-15T01:30:18Z
• Authors: Xiao Yang; ShaoMing Zhu; Li Li; Li Zhang; Shu Xian; Yanqing Wang; Yanxiang Cheng
• Keywords: KEGG

The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, cause and underlying molecular events of are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in reveal potential mechanisms.The expression profiles GDS3592, GSE54388, GSE66957 were downloaded from Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases samples 30 normal samples. three microarray datasets obtain differentially expressed (DEGs) deeply analyzed by methods. gene ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) pathway enrichments DEGs performed DAVID KOBAS online analyses, respectively. protein-protein interaction (PPI) networks constructed STRING database. A total 190 identified GEO datasets, 99 upregulated 91 downregulated. GO analysis showed that biological functions focused primarily on regulating cell proliferation, adhesion, differentiation intracellular signal cascades. main cellular components include membranes, exosomes, cytoskeleton, extracellular matrix. growth factor activity, protein kinase regulation, DNA binding, oxygen transport activity. KEGG these mainly Wnt signaling pathway, amino acid metabolism, tumor pathway. 17 most closely related PPI network.This study indicates screening for pathways using analyses could help us understand mechanism development cancer, be clinical significance early diagnosis prevention provide effective targets treatment cancer.