Comparison of the gene expression profiles of endometrial and trophoblastic cells in women with recurrent miscarriage: A bioinformatics approach

Abstract

Abstract Background: Recurrent miscarriage (RM) remains unsolved in > 50% of patients and causes physical and psychological problems in women without specific risk factors for miscarriage. For a successful pregnancy, acceptance of the endometrium and invasion of trophoblast cells into the endometrium is necessary. Objective: This study aimed to use computational analysis to identify key genes and related pathways in endometrial and trophoblast cells derived from RM samples. Materials and Methods: In this bioinformatics study, we explored the differential expression of genes in endometrial and trophoblast cells by analyzing the GSE165004 and GSE76862 datasets, respectively with the limma package in R software. Subsequently, overlapped genes between 2 datasets were selected, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. The overlapped genes were integrated to construct a protein-protein interaction network and hub genes selection. Results: We observed 41 overlapped genes between endometrial and trophoblast cells, and future analysis was accomplished in overlapped and nonoverlapped genes. Kyoto Encyclopedia of Genes and Genomes analysis indicated that overlapped genes were significantly enriched in the complement and coagulation cascades, pluripotency of stem cells, and synthesis and degradation of ketone bodies. Gene ontology analysis suggested that the genes were enriched in the cell cycle, apoptosis, and cell division. The top 10 genes included: IRS1, FGF2, MAPK6, MAPK1, MAPK3, MAPK8, MAPK9, PLK1, PRKACA, and PRKCA were identified from the PPI network. Conclusion: This study identified the key genes and potential molecular pathways underlying the development of RM. This could provide novel insights to determine the possible mechanisms and interventional strategies associated with miscarriage.

Keywords

• recurrent miscarriage, transcriptome profile, gene ontology, bioinformatics.