Ferroptosis resistance mechanisms in endometriosis for diagnostic model establishment

Abstract

Research question: Does ferroptosis resistance occur in patients with endometriosis, and can it be used as an indicator for diagnosis?

Design: Six datasets including 164 expression arrays from endometriosis studies were selected from the National Center for Biotechnology Information Gene Expression Omnibus. Surrogate variable analysis was used for data integration after RobustRankAggreg to determine ferroptosis-associated gene expression trends. Differential genes between eutopic and normal endometrium, as well as between ectopic and eutopic endometrium, were determined. Ferroptosis resistance mechanisms during the development of endometriosis were determined by intergenic co-expression and the Kyoto Encyclopedia of Genes and Genomes pathway. Independent factors were then screened by least absolute shrinkage and selection operator regression to build a nomogram diagnostic model. The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were used to verify model consistency and diagnostic efficacy, respectively.

Results: Differential analysis revealed a progressive increase in changes of ferroptosis-related genes in endometriosis patients during the process of endometrium development from eutopic to ectopic. In the subsequent regression selection, nine ferroptosis-associated genes in the eutopic endometrium could, in most cases, distinguish endometriosis patients from healthy patients. Therefore, these genes can be used as independent factors for endometriosis diagnosis. The nomogram model established by these molecular markers had an area under the ROC curve of 0.979.

Conclusions: The eutopic and ectopic endometrium of patients with endometriosis is associated with ferroptosis resistance genetic alterations. Ferroptosis-associated genes have excellent clinical value in endometriosis diagnosis, achieved by the nomogram model.

Keywords: Endometriosis; Ferroptosis resistance; Least absolute shrinkage and selection operator regression; Nomogram diagnostic model.