Prediction of microRNAs affecting the syncytin-1 (HERV-W) and syncytin-2 (HERV-FRD) genes regulation in endometriosis and miscarriage.

Abstract

Abnormal expression levels of microRNAs are associated with numerous diseases in the female reproductive tract. A small subset of human endogenous retroviruses (HERVs) genes have retained open reading frames (ORFs) that serve beneficial functions for the host. Syncytin-1 (HERV-W) and Syncytin-2 (HERV-FRD) play crucial roles in mammalian development and are expressed in placental trophoblasts. The miRNAs associated with HERV-W and HERV-FRD in spontaneous abortion and endometriosis have not been elucidated. The present study aimed to identify potential miRNAs that affect the regulation of Syncytin-1 and Syncytin-2 in endometriosis and miscarriage using bioinformatics tools. Complete CDS of Syncytin-1 (ERVW-1) and Syncytin-2 (ERVFRD-1) genes were collected from the gene bank database. Several target prediction algorithms were utilized, such as TargetScan, DIANA, miRDB, and miRWalk. Complete CDS of Syncytin-1 (ERVW-1) and Syncytin-2 (ERVFRD-1) genes were collected from the gene bank database. By integrating data from these diverse bioinformatics databases, miR-509-3p and miR-625-5p were consistent across multiple platforms, ensuring robust selection criteria. These tools facilitate the identification of differentially expressed miRNAs, understanding their roles in cellular processes, and potentially utilizing them as biomarkers for disease diagnosis and prognosis. Validation of the identified miRNAs in experimental models or clinical samples is needed to confirm their roles in endometriosis and miscarriage.