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

Li Liu,Jiajing Lin,Hongying He

doi:10.3389/fgene.2019.00373

Li Liu, Jiajing Lin + Show 1 more

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https://doi.org/10.3389/fgene.2019.00373

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Abstract

Background and ObjectiveEndometrial 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 ResultsAn 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.ConclusionThis 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.

Highlights

Endometrial cancer (EC) is a group of epithelial malignancies that occur in the endometrium and is the most common gynecological malignancy in developed countries
4,410 Differentially Expressed Gene (DEG) were obtained, which consisted of 2,215 upregulated genes and 2,195 downregulated genes in EC tissue when compared with normal endometrial tissue in the The Cancer Genome Atlas (TCGA) dataset (Supplementary Table S2)
255 common DEGs were identified between the GSE63678 and the TCGA EC dataset which comprised of 168 upregulated genes and 87 downregulated genes (Figure 1C and Supplementary Table S3)

Summary

Introduction

Endometrial cancer (EC) is a group of epithelial malignancies that occur in the endometrium and is the most common gynecological malignancy in developed countries. In China, the incidence and mortality of EC was 6.6/100,000 and 1.54/100,000, respectively, in females in 2014 (Chen et al, 2014). The incidence of EC has increased during recent years based on the population age and population size (Chen et al, 2017; Global Burden of Disease Cancer Collaboration, Fitzmaurice et al, 2018). EC patients usually have good prognosis but advanced, recurrent, or metastatic EC patients commonly have a bad outcome, which contributes to an ineffective response to radical surgery for EC (Creasman et al, 2006; Watari et al, 2009; Mcgunigal et al, 2017). Endometrial cancer (EC) is a common gynecological malignancy worldwide. 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

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