• A network-biology approach for identification of Hub Genes and Key Pathways in Uterine Corpus Endometrial Carcinoma
  • Niloufar Sadat Kalaki,1,*
    1. Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran


  • Introduction: Uterine corpus endometrial carcinoma (UCEC), originating from the endometrium, is the most common type of endometrial cancer. This gynecological malignancy is very common all over the world, especially in developed countries and shows a possibly increasing trend with the increase of obese women.
  • Methods: GSE7305 and GSE25628 were selected from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and a logFC ≥ 1 and logFC ≤-1 were identified. Common DEGs of two datasets were identified using the GEO2R tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases were used to identify pathways. Protein-protein interactions (PPIs) analysis was performed by using the Cytoscap
  • Results: 304 common DEGs have been identified through the use of GEO and PPI, respectively. The GO and KEGG pathways analysis showed DEGs were enriched in cell adhesion and ECM-receptor interaction. The expression of 2 genes GNG4 and DSP showed a significant difference between normal and tumor samples, have been identified by GEPIA analysis.
  • Conclusion: In this study, the hub genes and their related pathways involved in the development of UCEC were identified. These genes, as potential diagnostic biomarkers may provide a potent opportunity to detect UCEC at the earliest stages, resulting in a more effective treatment.
  • Keywords: Endometriosis, UCEC, PPI network, Diagnostic biomarkers