title: A cross-study gene set enrichment analysis identifies critical pathways in endometriosis.
abstract: BACKGROUND
  Endometriosis is an enigmatic disease. Gene expression profiling of endometriosis has been used in several studies, but few studies went further to classify subtypes of endometriosis based on expression patterns and to identify possible pathways involved in endometriosis. Some of the observed pathways are more inconsistent between the studies, and these candidate pathways presumably only represent a fraction of the pathways involved in endometriosis.
  
  
  METHODS
  We applied a standardised microarray preprocessing and gene set enrichment analysis to six independent studies, and demonstrated increased concordance between these gene datasets.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Shanghai Jiao Tong University, PR China. zhaohb@sjtu.edu.cn
authors.forename: Hongbo
authors.initials: H
authors.lastname: Zhao
authors.forename: Qishan
authors.initials: Q
authors.lastname: Wang
authors.forename: Chunyan
authors.initials: C
authors.lastname: Bai
authors.forename: Kan
authors.initials: K
authors.lastname: He
authors.forename: Yuchun
authors.initials: Y
authors.lastname: Pan
mesh_terms: D030541:Databases, Genetic / Q000706:statistics & numerical data; D004715:Endometriosis / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010049:Ovarian Diseases / Q000235:genetics; D010532:Peritoneal Diseases / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics; D014599:Uterus / Q000378:metabolism