A PubMed-wide study of endometriosis

Endometriosis affects 5-10% of women in reproductive age, leading to dysmenorrhea, pelvic pain and infertility; however, our understanding on the pathogenesis of this disease remains incomplete. In the present study, we performed a systematic analysis of endometriosis-related genes using text mining. Taking text mining results as input, we subsequently generated a filtered gene set by computing the likelihood of finding more than expected occurrences for every gene across the disease-centered subset of the PubMed database. Characterization of this filtered gene set by gene ontology, pathway and network analysis provides clues to the multiple mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissues, including the migration, implantation, survival and proliferation of ectopic endometrial cells. Finally, using this gene set as “seed”, we scanned human genome to predict novel candidate genes based on gene annotations from multiple databases. Our study provides in-depth insights into the pathogenesis of endometriosis.