The value of long noncoding RNAs for predicting the recurrence of endometriosis: A protocol for meta-analysis and bioinformatics analysis

BACKGROUND: As a gynecological disease, endometriosis (EM) seriously endangers the health of women at the age of childbearing and is closely related to long noncoding RNAs (lncRNAs). Current studies have discovered that there are differential expressions of many kinds of lncRNAs in EM. However, whether lncRNAs can be applied as a new marker for the prediction of the recurrence of EM is still controversial. In this study, meta-analysis and bioinformatics analysis were carried out to explore the value of lncRNAs as a predictor of the recurrence of EM and to analyze its biological role. METHODS: PubMed, Embase, and Web of Science databases were searched through computer and the articles published from the self-built database to April 2021 were collected. According to the inclusion and exclusion criteria, the literature was screened, and the quality of the inclusion study was evaluated. Stata 16.0 software was used for meta-analysis. The co-expression genes related to lncRNAs were screened by online tool Co-LncRNA. Then David for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were conducted. A competitive endogenous RNA network that may exist in lncRNAs through Starbase was built. RESULTS: The results of this meta-analysis would be submitted to peer-reviewed journals for publication. CONCLUSION: This meta-analysis could provide high-quality evidence support for lncRNAs, so as to predict the recurrence of EM. At the same time, we use bioinformatics technology to predict and analyze its biological effects, which provides a theoretical basis for further experimental verification. ETHICS AND DISSEMINATION: The private information from individuals will not be published. This systematic review also should not damage participants’ rights. Ethical approval is not available. The results may be published in a peer-reviewed journal or disseminated in relevant conferences. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/MF3QJ.