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Identification of key pathways and genes in endometrial cancer using bioinformatics analyses

  • Authors:
    • Yan Liu
    • Teng Hua
    • Shuqi Chi
    • Hongbo Wang
  • View Affiliations / Copyright

    Affiliations: Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, P.R. China
    Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 897-906
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    Published online on: November 5, 2018
       https://doi.org/10.3892/ol.2018.9667
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Abstract

Endometrial cancer (EC) is one of the most common gynecological cancer types worldwide. However, to the best of our knowledge, its underlying mechanisms remain unknown. The current study downloaded three mRNA and microRNA (miRNA) datasets of EC and normal tissue samples, GSE17025, GSE63678 and GSE35794, from the Gene Expression Omnibus to identify differentially expressed genes (DEGs) and miRNAs (DEMs) in EC tumor tissues. The DEGs and DEMs were then validated using data from The Cancer Genome Atlas and subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape were used to construct a protein‑protein interaction network and the prognostic effects of the hub genes were analyzed. Finally, miRecords was used to predict DEM targets and an miRNA‑gene network was constructed. A total of 160 DEGs were identified, of which 51 genes were highly expressed and 100 DEGs were discovered from the PPI network. Three overlapping genes between the DEGs and the DEM targets, BIRC5, CENPF and HJURP, were associated with significantly worse overall survival of patients with EC. A number of DEGs were enriched in cell cycle, human T‑lymphotropic virus infection and cancer‑associated pathways. A total of 20 DEMs and 29 miRNA gene pairs were identified. In conclusion, the identified DEGs, DEMs and pathways in EC may provide new insights into understanding the underlying molecular mechanisms that facilitate EC tumorigenesis and progression.
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Copy and paste a formatted citation
Spandidos Publications style
Liu Y, Hua T, Chi S and Wang H: Identification of key pathways and genes in endometrial cancer using bioinformatics analyses. Oncol Lett 17: 897-906, 2019.
APA
Liu, Y., Hua, T., Chi, S., & Wang, H. (2019). Identification of key pathways and genes in endometrial cancer using bioinformatics analyses. Oncology Letters, 17, 897-906. https://doi.org/10.3892/ol.2018.9667
MLA
Liu, Y., Hua, T., Chi, S., Wang, H."Identification of key pathways and genes in endometrial cancer using bioinformatics analyses". Oncology Letters 17.1 (2019): 897-906.
Chicago
Liu, Y., Hua, T., Chi, S., Wang, H."Identification of key pathways and genes in endometrial cancer using bioinformatics analyses". Oncology Letters 17, no. 1 (2019): 897-906. https://doi.org/10.3892/ol.2018.9667
Copy and paste a formatted citation
x
Spandidos Publications style
Liu Y, Hua T, Chi S and Wang H: Identification of key pathways and genes in endometrial cancer using bioinformatics analyses. Oncol Lett 17: 897-906, 2019.
APA
Liu, Y., Hua, T., Chi, S., & Wang, H. (2019). Identification of key pathways and genes in endometrial cancer using bioinformatics analyses. Oncology Letters, 17, 897-906. https://doi.org/10.3892/ol.2018.9667
MLA
Liu, Y., Hua, T., Chi, S., Wang, H."Identification of key pathways and genes in endometrial cancer using bioinformatics analyses". Oncology Letters 17.1 (2019): 897-906.
Chicago
Liu, Y., Hua, T., Chi, S., Wang, H."Identification of key pathways and genes in endometrial cancer using bioinformatics analyses". Oncology Letters 17, no. 1 (2019): 897-906. https://doi.org/10.3892/ol.2018.9667
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