Abstract
Endometriosis is associated with ovarian cancers, mainly endometrioid and clear-cell carcinomas. Iron metabolism has been shown to play a role in endometriosis. Therefore, it is vital to explore the relationship between iron metabolism and ovarian cancer and to identify novel markers for diagnostics and therapeutics. The endometriosis dataset GSE51981 and the ovarian cancer dataset GSE26712 were obtained from the gene expression omnibus database, and differentially expressed genes were identified. Iron metabolism genes were obtained from molecular signatures database, and hub genes from the 3 datasets were obtained. Seven hub genes were identified by bioinformatic analysis, and 3 hub genes (NCOA4, ETFDH, and TYW1) were further selected by logistic regression, which were verified in an independent endometriosis dataset (GSE25628) and ovarian cancer dataset (GSE14407), showing good predictive diagnostic value (area under the receiver operating characteristic curve of 0.88 and 0.9, respectively). Gene Ontology, gene set enrichment analysis, and immune infiltration analysis further confirmed the related functions, pathways, and immune relationship between iron metabolism and ovarian cancer. This study highlights the potential of targeting iron metabolism in the prevention of potential ovarian cancer and in the further exploration of endometriosis and endometriosis-relevant ovarian cancer therapeutics.