Identification of Candidate Genes in Recurrence and Non-Recurrence Endometrial Carcinoma Patients by an Integrative Analysis
Identification of Candidate Genes in Recurrence and Non-Recurrence Endometrial Carcinoma Patients by an Integrative Analysis
Sheyda Khalilian,1,*
1. Department of Medical Genetics, School of medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran, AND Student Research Committee, School of medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran, AND USERN Office, Shahid Beheshti University of Medical Sciences, Tehra
Introduction: Endometrial carcinoma (EC) is one of the most prevalent tumors of the female reproductive system. Although numerous studies, including analysis of gene expression profile and cellular microenvironment have been reported in this field, pathogenesis of this disease remains unclear. The molecular profile of endometrial cancer has become an important tool in determining patient prognosis and their optimal adjuvant treatment. This study aimed to screen the candidate genes differentially expressed in recurrence and non-recurrence patients by bioinformatics analysis.
Methods: GEO database and GEO2R online tool were applied to screen the differentially expressed genes (DEGs) of EC from the microarray datasets. Protein-protein interaction (PPI) network for the DEGs was constructed to further explore the relationships among these genes and identify hub DEGs. Gene ontology and KEGG enrichment analyses were performed to investigate the biological role of DEGs. Besides, expression profile, and survival analysis of MFNG gene, as one of the top hub DEGs, were also investigated using Gene Expression Profiling Interactive Analysis2 (GEPIA2) to further explore the roles of these hub gene in the mechanism of EC tumorigenesis.
Results: A total of 551 DEGs were screened out as the DEGs with 369 upregulated and 182 downregulated in EC. The gene ontology analysis showed that these genes were significantly enriched in cell communication, biological regulation, and localization, etc. The KEGG pathway analysis showed that DEGs were enriched in T-cell activation, leukocyte cell-cell adhesion, and leukocyte activation, etc. More importantly, MFNG, ZAK, SOCS2, WNT4, SMO, SMAD9, USP39, PRKACG, SF3A3, TRAF7 were identified as the hub genes of EC. Expression validation by bioinformatics analysis also proved the expression of MFNG was differentially expressed in EC, but overall survival was not altered significantly.
Conclusion: MFNG involved in the pathogenesis of EC and might be a candidate biomarker for distinguishing recurrence and non-recurrence patients.