Identification of long non-coding RNA biomarkers and signature scoring, with competing endogenous RNA networks- targeted drug candidates for recurrent implantation failure
Nuan Lin, Jia‐zhe Lin
Abstract
Recurrent implantation failure (RIF) remains a source of frustration and presents challenges to clinicians in the practice of assisted reproductive technology (ART). Long non-coding RNAs (lncRNAs) are increasingly recognised as potential biomarkers in various diseases. In this study, eight differentially expressed lncRNAs (LINC00645, LINC00844, LINC02349, AC010975.1, AC022034.1, AC096719.1, AC104072.1 and DLGAP1-AS3) to distinguish RIF from fertile women were identified by RobustRankAggreg (RRA). A two-lncRNA signature for predicting RIF was established by least absolute shrinkage and selection operator (LASSO) regression, with accuracy confirmed by receiver operating characteristic (ROC) curves. After lncRNA-microRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed, suggesting that the lncRNA-miRNA-mRNA regulatory networks were associated with biological processes involved in endometrial receptivity. Finally, three putative drugs (miconazole, terfenadine and STOCK1N-35215) for RIF were predicted by a Connectivity Map. In conclusion, we identified eight lncRNA biomarkers and a two-lncRNA signature for predicting RIF, as well as proposing three candidate drugs against RIF by targeting the ceRNA networks.