Identification of key genes associated with infertile endometriosis based on bioinformatic analysis

Liya Shi^1,2*Xiaocong Chen¹Ye Hongjuan³Xin Xie⁴Yang Wang⁵Jie Cheng⁶Linlin Chang⁷Songguo Xue^1*

• ¹Department of Reproductive Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
• ²b Department of Obstetrics and Gynecology, Ji 'an Hospital, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China., Ji 'an, China
• ³Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
• ⁴Department of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, Shanghai Municipality, China
• ⁵Department of Obstetrics and Gynecology, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
• ⁶Center for Reproductive Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
• ⁷Department of Obstetrics and Gynecology, Department of Obstetrics & Gynecology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

Background: Endometriosis is a common disease among women of childbearing age. However, the molecular mechanism behind it is still unknown. Therefore, new biomarkers and therapeutic targets are needed to improve the diagnosis and treatment of infertile women. Methods: Microarray datasets GSE7305, GSE7307, and GSE51981 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between control and endometriosis. The STRING database and Cytoscape software constructed protein-protein interaction (PPI) and hub gene networks. At the same time, the three data sets were screened for co-differentially expressed genes related to mitosis. Subsequently, we identified mitosis-related hub genes (MRHGs) associated with both mitosis-related genes and hub genes. Next, enrichment analysis for target genes was performed by Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the mRNA-miRNA network was constructed. Finally, GSE25628 and GSE6364 were used to verify the expression of MRHGs individually, while GSE120103 was employed to ascertain the influence of mitosis-related genes on female fertility. Results: A total of 93 DEGs were identified in the endometriosis datasets. Then, we placed 11 potential mitosis-related downregulated hub genes, among which eight showed good diagnostic properties of endometriosis, and two showed good diagnostic properties of infertile endometriosis. The main enriched GO functions revealed that the cell cycle mitotic pathway may be the critical pathway in endometriosis. Meanwhile, mRNA-miRNA interaction networks were constructed by choosing coexpressed mRNAs and miRNAs. Furthermore, cordycepin showed high drugtargeting relevance in infertile endometriosis. Conclusions: We identified eight mitosis-related hub genes as potential biomarkers for diagnosing and treating endometriosis. CENPE and CCNA2 might be associated with infertile endometriosis by affecting the endometrial secretory phase transition. In addition, cordycepin may be a potential clinical treatment for people with infertilityrelated endometriosis. Keywords Endometriosis; infertility; differentially expressed genes; biomarkers; DEGs Highlights • This article uses bioinformatic methods to identify target genes for the treatment of endometriosis.• This article adopts a new search method for target genes based on the common characteristics of infertility and endometriosis.• Scan suitable therapeutic agents for target genes from the drug database.