A relational database to identify differentially expressed genes in the endometrium and endometriosis lesions

Abstract

Endometriosis is a common inflammatory estrogen-dependent gynecological disorder, associated with pelvic pain and reduced fertility in women. Several aspects of this disorder and its cellular and molecular etiology remain unresolved. We have analyzed the global gene expression patterns in the endometrium, peritoneum and in endometriosis lesions of endometriosis patients and in the endometrium and peritoneum of healthy women. In this report, we present the EndometDB, an interactive web-based user interface for browsing the gene expression database of collected samples without the need for computational skills. The EndometDB incorporates the expression data from 115 patients and 53 controls, with over 24000 genes and clinical features, such as their age, disease stages, hormonal medication, menstrual cycle phase, and the different endometriosis lesion types. Using the web-tool, the end-user can easily generate various plot outputs and projections, including boxplots, and heatmaps and the generated outputs can be downloaded in pdf-format.Availability and implementationThe web-based user interface is implemented using HTML5, JavaScript, CSS, Plotly and R. It is freely available from https://endometdb.utu.fi/ .

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1

a) Schematic overview of the EndometDB used for transcriptomic analysis. The questionnaire data is collected through the Webropol survey system and imported into EndometDB via an automated service layer API. The numerical results of the biomedical samples analyzed are uploaded into the system together with the sample information. The uploading is done through the web user interface. Analytical functions are available through the analysis engine and can be used to query the whole data set. (b) EndometDB web graphical user interface (GUI) analysis process. The GUI, through a client, communicates with the analysis engine through an API layer implemented with PHP. The analysis engine analyzes the data using the R programming language and Plotly graphing library with the ggplot2 R package is used to generate the plots that are transferred back to the GUI.

Fig. 2

EndometDB GUI. Screenshot of the EndometDB user interface showing tabbed browsing functions (on the left) and an example boxplot (HSD17B2) as an output of a gene analyzed (on right). The browsing functions include controls for interacting with the filters such as the clinical data (age, menstrual cycle phase, hormonal medication, disease stage), sample data (tissue type; endometrium, peritoneum, endometriosis lesions) and plots and projections. The different color boxplot represents the different tissues and lesions for both the healthy women and patients. The colors make it easier to distinguish between the different tissues and lesions as well as between healthy controls and patients.

Fig. 3

Example output of unsupervised hierarchical clustering analysis generated via the EndometDB GUI. Example of unsupervised hierarchical clustering analysis of mRNA expression of the differentially expressed WNT pathway genes (Online-only Table 1) in all the sample groups. The different clinical features of the samples (lesion/tissue type, age of the patients with pre-selected grouping, hormonal stage, and disease stage) are attached to the heatmap. Canberra distance metric with Ward’s clustering method was applied showing clusters corresponding to lesions and tissue types. The dendrogram on the x-axis shows the hierarchical relationship between the tissues and lesion as well as the cycle phase and disease stage. While the dendrogram on the y-axis shows the measure of similarities in the activation levels of the WNT signaling pathway genes. The colors represent different tissues and lesions from both healthy controls and patients.

Fig. 4

Example of correlation heatmap from the EndometDB generated via the GUI. Correlation heatmap after hierarchical clustering of WNT pathway genes (Online-only Table 1) with Pearson correlation method and Ward’s clustering method. Clustering dendrogram on both axes show the measure of the relationship between the genes.

Fig. 5

Example of projection outputs from the EndometDB generated via the GUI. (a) PCA analysis of mRNA expression of the differentially expressed WNT pathway genes in all the sample groups. Samples are colored by tissue types, and the confidence ellipses with 95% confidence level for the expression in various tissue types are generated using the EndometDB GUI. The PCA separates control and patient endometrium from the three subtypes of endometriosis lesions. (b) LFDA analysis of WNT pathway genes with raw eigenvectors metric colored by tissues. In addition to separating endometriosis lesions from endometrium, LFDA separates ovarian endometriosis from peritoneal and deep endometriosis. The list of WNT pathway genes used in these analyses are listed in Online-only Table 1.

Fig. 6

Validation of microarray result with qRT-PCR. Examples of the mRNA expression for steroid receptors (ESR1, PGR), steroid metabolizing enzymes (HSD17B2, HSD17B6), and WNT- pathway genes (SFRP2, and MPZL2) analyzed by RT-qPCR (a1, b1, c1, d1, e1, f1) and with the EndometDB (a2, b2, c2, d2, e2, f2).