| Literature DB >> 35117376 |
Milena Králíčková1,2,3, Vaclav Vetvicka4, Antonio Simone Laganà5.
Abstract
In developed countries, endometrial cancer (EC) is the most frequent gynecologic malignancy in postmenopausal women. At the same time, EC has become one of the most common cancers in numerous developing countries, probably influenced by global epidemic of obesity. The majority of patients have low-grade endometrioid cancer with a high 5-year survival rate, but with high-risk EC, the survival rates are still rather low. However, despite intensive research in last decades, our knowledge of the mechanisms, risk factors, diagnosis and treatment have not significantly improved. The standard treatment of all types of EC is still a traditional combination of surgery, irradiation and/or chemotherapy, despite the fact that each of these options is not without having some negative side effects. Despite the fact that on the molecular level, EC is relatively well-studied, but the efforts to transform these findings into either diagnosis or therapies of EC remain elusive. In addition, some research into risk factors involved in the development or progression of EC seems to be more a fishing expedition than a well thought-out approach. The purpose of this review is to summarize the most recent developments in the search for biomarkers and prognostic markers and to discuss the progress in EC treatment. 2020 Translational Cancer Research. All rights reserved.Entities:
Keywords: Biomarkers; endometrial cancer (EC); risk factors
Year: 2020 PMID: 35117376 PMCID: PMC8798081 DOI: 10.21037/tcr-20-1720
Source DB: PubMed Journal: Transl Cancer Res ISSN: 2218-676X Impact factor: 1.241
Figure 1Protein-protein interaction network analysis of the dysregulated genes in endometrial adenocarcinoma. The 56 significantly dysregulated genes (47 upregulated and 9 downregulated) were input into the Search Tool for the Retrieval of Interacting Genes (STRING) database for protein-protein interaction (PPI) network analysis. The minimum required interaction score was set to the medium confidence (score =0.400). Nodes represent proteins and edges represent protein-protein associations. Nodes without edges are not displayed. This analysis obtained a highly interactive PPI network of 56 nodes and 67 edges, with PPI enrichment P value of <1.0×10−16. Most genes in the PPI network were associated with three biological pathways, including defense response (19 genes, shown in blue), response to stimulus (44 genes, shown in green), and immune system process (21 genes, shown in red). From (12).
Figure 2Potential pathways directly linking metabolic syndrome with endometrial cancer. From (32).
Figure 3Endometrial cancer blood based biomarker correlation network based on the search tool for the retrieval of interacting genes/proteins (STRING) network analysis using gene names and visualised with the Cytoscape software. Line thickness indicates strength of the interactions. Protein biomarkers were clustered using the markov cluster (MCL) algorithm and subjected to functional enrichment. On the right, the biological processes describing the functions of the candidates are indicated. No significant interactions were reported for Dickkopf-related protein 3 precursor (DKK3), Sperm associated antigen-9 (SPAG 9), Alpha-1-beta glycoprotein (AIBG) and Growth differentiation factor 15 (GDF-15) and, therefore, are not included in the final network. From (43).