Bioinformatics analysis of next generation sequencing data reveals novel biomarkers and signaling pathways associated with recurrent implantation failure
Recurrent implantation failure (RIF) is a cases in which women have had three fruitless in vitro fertilization (IVF) bid with positive quality embryos. The RIF originates from uterine endometrium microbiota has been implicated in reproductive failure, and poor prognosis and lacks effective treatment. Efforts have been made to elucidate the molecular pathogenesis of RIF. To identify key genes and signaling pathways in RIF, the next genetation sequencing data GSE243550 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between RIF and normal controls samples were identified using t-tests in the limma R bioconductor package. Using the DEGs, we further performed a series of gene ontology (GO) and REACTOME pathway enrichment analyses. Protein-protein interaction network was derived using the IMex interactome database and visualized using Cytoscape software. The most significant modules from the PPI network were selected for GO and pathway enrichment analysis. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed depending on key hub genes and visualized using Cytoscape software. A receiver operating characteristic curve (ROC) analysis was plotted to diagnose RIF. In total, 958 DEGs were identified, of which 479 were up regulated genes and 479 were down regulated genes. GO and REACTOME pathway enrichment analysis results revealed that the upregulated genes were mainly enriched in multicellular organismal process, membrane, small molecule binding and extracellular matrix organization, whereas downregulated genes were mainly enriched in organonitrogen compound metabolic process, intracellular anatomical structure, catalytic activity and translation. Through analyzing the PPI network, we screened hub genes APP, HSP90AA1, CAND1, CUL1, HSP90AB1, SIRT7, SRC, CDKN1A, ISG15 and RPS16 by the Cytoscape software. The regulatory network analysis revealed that microRNAs (miRNAs) include hsa-miR-574-3p and hsa-mir-208a-3p, and transcription factors (TFs) include SREBF1 and RELA might be involved in the development of RIF. Receiver operating characteristic curve analysis demonstrated that the hub genes screened for RIF were of good diagnostic significance. Overall, these results thus highlight a range of novel signaling pathways and genes that are linked to the incidence and progression of RIF, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of RIF.