Abstract

Endometriosis (EM) is a chronic neuroinflammatory disorder that is associated with pain and infertility that affects ∼10% of reproductive-age women. The pathophysiology and etiology of EM remain poorly understood, and diagnostic delays are common. Exploration of the underlying molecular mechanism, as well as novel diagnostic biomarkers and therapeutic targets, is urgently needed. Inflammation is known to play a key role in the development of lesions, which are a defining feature of the disorder. In our research, the CIBERSORT and WGCNA algorithms were used to establish a weighted gene co-expression network and to identify macrophage-related hub genes using data downloaded from the GEO database (GSE11691, 7305). The analysis identified 1,157 differentially expressed genes (DEGs) in EM lesions, of which five were identified as being related to M2 macrophages and were validated as differentially expressed by qRT-PCR and immunohistochemistry (IHC). Of these putative novel biomarker genes, bridging integrator 2 (BIN2), chemokine receptor 5 (CCR5), and macrophage mannose receptor 1 (MRC1) were upregulated, while spleen tyrosine kinase (SYK) and metalloproteinase 12 (ADAM12) were downregulated in ectopic endometria vs. normal endometria. Meanwhile, 23 potentially therapeutic small molecules for EM were obtained from the cMAP database, among which topiramate, isoflupredone, adiphenine, dexverapamil, MS-275, and celastrol were the top six molecules with the highest absolute enrichment values. This is our first attempt to use the CIBERSORT and WGCNA algorithms for the identification of novel Mϕ2 macrophage-related biomarkers of EM. Our findings provide novel insights into the impact of immune cells on the etiology of EM; nevertheless, further investigation of these key genes and therapeutic drugs is needed to validate their effects on EM.

Highlights

  • Endometriosis (EM) is a chronic, estrogen-dependent inflammatory disease that is characterized by abnormal growth of endometrial tissue outside the uterine cavity (Zannoni et al, 2016)

  • The mRNA expression profiles (Hull et al, 2008; Falcone and Flyckt, 2018) of 19 pairs of endometriosis and healthy endometrial samples in the GSE11691 and GSE7305 datasets were downloaded from the GEO database, which were merged and batch-normalized for the following analysis

  • CIBERSORT algorithm analyzes the abundance of the 22 distinct types of immune cell infiltration in 19 pairs of samples

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Summary

INTRODUCTION

Endometriosis (EM) is a chronic, estrogen-dependent inflammatory disease that is characterized by abnormal growth of endometrial tissue outside the uterine cavity (Zannoni et al, 2016). We used the normal endometrium and ectopic endometrium-related gene expression datasets, GSE11691 and GSE7305, which were extracted from the Gene Omnibus (GEO) database, to identify potential macrophage-related biomarkers in EM using WGCNA. With the application of the cMAP database, we identified the small molecules that may have an effect on EM by using differentially expressed genes (DEGs). This is the first time that WGCNA was used to explore macrophage-related genes in EM, which could provide novel insights for the early diagnosis at the molecular level and treatment of patients with EM

RESULTS
MATERIALS AND METHODS
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DATA AVAILABILITY STATEMENT
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