A cross-study gene set enrichment analysis identifies critical pathways in endometriosis

We applied a standardised microarray preprocessing and gene set enrichment analysis to six independent studies, and demonstrated increased concordance between these gene datasets.We find 16 up-regulated and 19 down-regulated pathways common in ovarian endometriosis data sets, 22 up-regulated and one down-regulated pathway common in peritoneal endometriosis data sets. Among them, 12 up-regulated and 1 down-regulated were found consistent between ovarian and peritoneal endometriosis. The main canonical pathways identified are related to immunological and inflammatory disease. Early secretory phase has the most over-represented pathways in the three uterine cycle phases. There are no overlapping significant pathways between the dataset from human endometrial endothelial cells and the datasets from ovarian endometriosis which used whole tissues.The study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. By standardised microarray preprocessing and GSEA, we have increased the concordance in identifying many biological mechanisms involved in endometriosis. The identified gene pathways will shed light on the understanding of endometriosis and promote the development of novel therapies.Endometriosis is defined as the presence of endometrium-like tissue in sites outside the uterine cavity and occurs in 6-10% of women in the general population [1]. The main clinical features are chronic pelvic pain, pain during intercourse, and infertility [2]. As cellular and molecular mechanisms involved in endometriosis are still uncovered, the classification of this disease evolved from a local disorder to a complex, chronic systemic disease [3]. Despite extensive researches, the etiology of endometriosis remains obscure. Gene expression profiling has been used in several studies of endometriosis, in which from a few to hundreds differentially expressed genes