LAUSR

Endometriosis is a common gynecological disease, affecting 6–10% of women of reproductive age. The precise mechanisms underlying the development of endometriosis remain unclear. In the present study, a bioinformatics approach was applied to systematically identify the pathways and genes involved in the development of endometriosis and to discover potential biomarkers. The gene expression profiles of GSE6364, a microarray dataset of endometrial biopsies obtained from women with or without endometriosis, was downloaded from the Gene Expression Omnibus DataSets database that stores original submitter-supplied records (series, samples and platforms), as well as curated datasets. Differentially expressed gene (DEG) analysis was performed with GEO2R. DAVID was used to analyze the gene ontology enrichment of the DEGs. Gene Set Enrichment Analysis (GSEA) was conducted using the GSEA v3.0 software. Protein-protein interactions (PPI) were evaluated with the Search Tool for the Retrieval of Interacting Genes, and PPI network visualization was performed with Cytoscape. In addition, Cell Counting kit-8 and Transwell assays were performed on human endometrial stromal cells (HESCs). A total of 172 DEGs were extracted. Inflammatory response genes were significantly upregulated in the endometriosis tissues and C-X-C motif chemokine receptor 2 (CXCR2), was one of the most up-regulated genes according to DEG analysis. Cell-based experiments confirmed that CXCR2 promoted the proliferation, migration and invasion of HESCs. In conclusion, a bioinformatics approach combined with in vitro experiments in the present study revealed that CXCR2 may be associated with the development of endometriosis and has potential as a biomarker for the diagnosis of endometriosis.