Maryam Rezazadeh
1 
, Shahla Danaei-Mehrabad
2, Nahideh Afshar Zakariya
3, Fatemeh Kazemi
3, Marziyeh Sadat Moslehian
4, Amin Tamadon
5,6,7, Reza Shirazi
8, Mahdi Mahdipour
4,9* , Parvin Hakimi
3* 1 Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
2 Department of Gynecology, Eastern Azerbaijan ACECR ART Center, Eastern Azerbaijan Branch of ACECR, Tabriz, Iran
3 Women's Reproductive Health Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
4 Stem Cell Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
5 Department of Natural Sciences, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan
6 Stem Cells Technology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
7 Department of Research and Development, PerciaVista R&D Co., Shiraz, Iran
8 Department of Anatomy, School of Biomedical Sciences, Medicine & Health, UNSW Sydney, Sydney, Australia
9 Department of Reproductive Biology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
Abstract
Introduction: Endometrial cancer (EC) is a particularly frequent gynecological cancer, and metastasis is the leading cause of death in patients with EC. Using publicly accessible gene expression data, a bioinformatics study was carried out to increase our knowledge and reveal treatment targets for EC metastasis. This study aimed to identify new important molecular actors and clarify the molecular processes and pathways underlying EC metastasis.
Methods: The GEOexplorer and R programming languages were used to analyze and visualize gene expression data from EC metastatic gene expression datasets, and differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) were identified using bioinformatics with P-value thresholds of < 0.05, and |log2FC| > 1.5. KEGG pathway enrichment analysis and gene ontology enrichment was used to enrich the observed DEGs, protein-protein interactions were established, and hub genes were identified.
Results: The findings revealed that DEGs were considerably enriched in a number of pathways, including the "Pathways in cancer", "Breast cancer", and "Rap1 signaling pathway." DEGs were also found to be involved in a number of biological processes, cellular components, and molecular activities. The PPI network included the hub genes CTNNB1, FGFR3, ESR1, and SRSF3 as well as a number of DElncRNAs, such as LINC01541, SNHG17, LINC00520, BHLHE22-AS1, LOC100509445, H19, and HOTAIRM1.
Conclusion: This study contributes to our understanding of the molecular processes driving EC metastasis, which may result in the development of new treatment targets and indicators for the early identification of EC metastasis. More studies are needed to validate these findings and to understand the functional roles of these key factors in EC metastasis.