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. 2019 Sep;18(3):2464-2476.
doi: 10.3892/ol.2019.10550. Epub 2019 Jun 28.

Identification of key genes and pathways between type I and type II endometrial cancer using bioinformatics analysis

Affiliations

Identification of key genes and pathways between type I and type II endometrial cancer using bioinformatics analysis

Kai Zhang et al. Oncol Lett. 2019 Sep.

Abstract

Endometrial carcinoma (EC) is a common malignant neoplasm of the female reproductive tract. The malignant degree of type II EC is much greater than that of type I EC, usually presenting with a high recurrence rate and a poor prognosis. Therefore, the present study aimed to examine the principal genes associated with the degree of differentiation in type I and type II EC and reveal their potential mechanisms. Differentially expressed genes (DEGs) were selected from the gene expression profiles derived from The Cancer Genome Atlas. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. In the present study, the KEGG pathway enrichment analysis revealed that 5,962 upregulated DEGs were significantly enriched in the 'p53 signaling pathway' and involved in 'lysine degradation'. In addition, 3,709 downregulated DEGs were enriched in 'pathways in cancer', as well as 'tight junction regulation', the 'cell cycle' and the 'Wnt signaling pathway'. The 13 top hub genes MAPK1, PHLPP1, ESR1, MDM2, CDKN2A, CDKN1A, AURKA, BCL2L1, POLQ, PIK3R3, RHOQ, EIF4E and LATS2 were identified via the protein-protein interaction network. Furthermore, the OncoPrint algorithm from cBioPortal declared that 25% of EC cases carried genetic alterations. The altered DEGs (MAPK1, MDM2, AURKA, EIF4E and LATS2) may be involved in tumor differentiation and may be valuable diagnostic biomarkers. In conclusion, a number of principal genes were identified in the present study that may be determinants of poorly differentiated type II EC carcinogenesis, which may contribute to future research into potential molecular mechanisms. In addition, these genes may help identify candidate biomarkers and novel therapeutic targets for type II EC.

Keywords: bioinformatics method; biomarkers; core genes; endometrial carcinoma; survival; therapeutic targets.

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Figures

Figure 1.
Figure 1.
Heat map of the top 200 differentially expressed genes. Red indicates upregulated genes and blue indicates downregulated genes. The top 200 upregulated DEGs and the top 200 downregulated DEGs in type I EC compared with type II EC are shown. DEGS, differentially expressed genes; EC, endometrial carcinoma.
Figure 2.
Figure 2.
Comparison of overall survival and progression-free survival in patients with type I and type II endometrial cancer. (A) Kaplan-Meier analysis curves of overall survival of 119 patients in the type I group versus 58 patients in the type II group. Significant statistical difference was noted in the overall survival times between the two groups (P=0.022). (B) Kaplan-Meier analysis curves of progression-free survival of 119 patients in the type I group versus 58 in the type II group. There was also no statistical difference identified in the progression-free survival times between these two groups (P=0.192).
Figure 3.
Figure 3.
PPI network of the differentially expressed genes and the top 13 hub genes. (A) PPI network and hub genes of the differentially expressed genes. Nodes were given different colors according to the interaction values between two genes. Yellow nodes represent a low value (darker node represents a smaller value) whereas blue nodes represent a high value (darker node represents a higher value). (B) PPI network of the top 13 hub genes. PPI, protein-protein interaction.
Figure 4.
Figure 4.
Significant module and enrichment analyses from the protein-protein interaction network. Only modules with a score >4 were presented. (A) Module 1. (B) Module 2. (C) Module 3. (D) Module 4.
Figure 5.
Figure 5.
(A) Genetic alterations. Red represents amplification and blue represents deep deletion of genes in 136 of the 547 endometrial carcinoma patients (25%). The aberrant expression threshold was defined as z-score ±2.0 from The Cancer Genome Atlas RNA Sequencing version 2 data. A Kaplan-Meier curve was created between groups with and without alterations. (B) Overall survival Kaplan-Meier estimate. (C) Progression-free survival Kaplan-Meier estimate. The red and blue lines represent cases with and without alterations, respectively.

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