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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00373

Identification of Potential Crucial Genes Associated with the Pathogenesis and Prognosis of Endometrial Cancer

  • 1Department of Obstetrics and Gynecology, LiuzhouWorker’s Hospital, Fourth Affiliated Hospital of Guangxi Medical University, China

Background and Objective: Endometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes.
Methods and Results: An mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20 and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1 and TRPM4 were constructed. The signature performed well for prognosis prediction (p<0.001) and time-dependent receiver–operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729 and 0.647 for 1, 3, 5 and 10-year overall survival (OS) prediction, respectively.
Conclusion: This study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction.

Keywords: endometrial cancer, bioinformatics, prognosis, biomarker, GEO, TCGA

Received: 27 Jan 2019; Accepted: 09 Apr 2019.

Edited by:

Gajendra P. Raghava, Indraprastha Institute of Information Technology Delhi, India

Reviewed by:

Andrew Dellinger, Elon University, United States
Hao Zhang, Jilin University, China  

Copyright: © 2019 Liu, Lin and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mrs. Hongying He, LiuzhouWorker’s Hospital, Fourth Affiliated Hospital of Guangxi Medical University, Department of Obstetrics and Gynecology, Liuzhou, China,