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.

Conclusion: 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.

Figure 1

Significant pathways identified and overlap between the 3 ovarian endometriosis data sets. For each data set, GSEA implanted to generate p-values for each pathway and using a permutation test with 1000 times, obtained the significant pathways with p-value cut-off of ≤ 0.05. (a) GSEA detected 24 (Eyster data set), 56 (Hever data set) and 36 (Roth data set) up-regulated pathways and 16 are common. (b) GSEA detected 31 (Eyster data set), 59 (Hever data set) and 44(Roth data set) down-regulated pathways and 19 are common.

Figure 2

Significant pathways identified and overlap between the 2 peritoneal endometriosis data sets. For each data set, GSEA implanted to generate p-values for each pathway and using a permutation test with 1000 times, obtained the significant pathways with p-value cut-off of ≤ 0.05. (a) GSEA detected 57 (Eyster data set) and 29 (Hull data set) up-regulated pathways and 22 are common. (b) GSEA detected 11 (Eyster data set) and 7 (Hull data set) down-regulated pathways and 1 is common.

Figure 3

Significant pathways identified and overlap between the phases of uterine cycle. For each phase, GSEA implanted to generate p-values for each pathway and using a permutation test with 1000 times, obtained the significant pathways with p-value cut-off of ≤ 0.05. (a) GSEA detected 4 (Proliferative phase), 12 (Early secretory phase) and 0 (Mid secretory phase) up-regulated pathways and no overlap. (a) GSEA detected 3 (Proliferative phase), 21 (Early secretory phase) and 22 (Mid secretory phase) down-regulated pathways and the overlap is very low.