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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
Identification of Key Candidate Genes and Pathways in Endometrial Cancer by Integrated Bioinformatical Analysis
Lihong Liu1,2, Fangxu Chen, Aihui Xiu
1Department of Gynecological Ward, The Third Affiliated Hospital ,Jinzhou Medical University, Jinzhou, China.
Abstract:
Endometrial Cancer is the most common female genital tract malignancy, its pathogenesis is complex, not yet fully described. To identify key genes of Endometrial Cancer we downloaded the gene chip GSE17025 from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified through the GEO2R analysis tool. Functional and pathway enrichment analysis were performed for DEGs using DAVID database. The network of protein–protein-interaction (PPI) was established by STRING website and visualized by Cytoscape. Then, functional and pathway enrichment analysis of DEGS were performed by DAVID database. A total of 1000 significant differences genes were obtained, contain 362 up-regulated genes and 638 down-regulated genes. PCDH10, SLC6A2, OGN, SFRP4, TRH, ANGPTL, FOSB are down-regulated genes. The gene of IGH, CCL20, ELF5, LTF, ASPM expression level in tumor patients are up-regulated. Biological function of enrichment include metabolism of xenobiotics by cytochrome P450, MAPK signaling pathway, Serotonergic synapse, Protein digestion and absorption, IL-17 signaling pathway, Chemokine signaling pathway, HIF-1 signaling pathway, p53 signaling pathway. All in all, the current study to determine endometrial differentially expressed genes and biological function, comprehensive analysis of intrauterine membrane carcinoma pathogenesis mechanism, and might be used as molecular targets and diagnostic biomarkers for the treatment of endometrial cancer.
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