Identification and analysis of novel endometriosis biomarkers via integrative bioinformatics

Endometriosis is a gynecological disease prevalent in women of reproductive age, and it is characterized by the ectopic presence and growth of the eutopic endometrium. The pathophysiology and diagnostic biomarkers of endometriosis have not yet been comprehensively determined. To discover molecular markers and pathways underlying the pathogenesis of endometriosis, we identified differentially expressed genes (DEGs) in three Gene Expression Omnibus microarray datasets (GSE11691, GSE23339, and GSE7305) and performed gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analyses. We also validated the identified genes via immunohistochemical analysis of tissues obtained from patients with endometriosis or healthy volunteers. A total of 118 DEGs (79 upregulated and 39 downregulated) were detected in each dataset with a lower (fold change) FC cutoff (log2|FC| > 1), and 17 DEGs (11 upregulated and six downregulated) with a higher FC cutoff (log2|FC| > 2). KEGG and GO functional analyses revealed enrichment of signaling pathways associated with inflammation, complement activation, cell adhesion, and extracellular matrix in endometriotic tissues. Upregulation of seven genes (C7, CFH, FZD7, LY96, PDLIM3, PTGIS, and WISP2) out of 17 was validated via comparison with external gene sets, and protein expression of four genes (LY96, PDLIM3, PTGIS, and WISP2) was further analyzed by immunohistochemistry and western blot analysis. Based on these results, we suggest that TLR4/NF-κB and Wnt/frizzled signaling pathways, as well as estrogen receptors, regulate the progression of endometriosis. These pathways may be therapeutic and diagnostic targets for endometriosis.

Discovery and validation of peritoneal endometriosis biomarkers in peritoneal fluid and serum

RESEARCH QUESTION

What are the potential biomarkers for peritoneal endometriosis in peritoneal fluid and serum?

DESIGN

Case-control studies composed of independent discovery and validation sets were conducted. In the discovery set, untargeted liquid chromatography-mass spectrometry (LC-MS/MS) metabolomics, multivariable and univariable analyses were conducted to generate global metabolomic profiles of peritoneal fluid for endometriosis and to identify potential metabolites that could distinguish peritoneal endometriosis (n = 10) from controls (n = 31). The identified metabolites from the discovery set were validated in independent peritoneal fluid (n =19 peritoneal endometriosis and n = 20 controls) and serum samples (n = 16 peritoneal endometriosis and n = 19 controls) using targeted metabolomics. The area under the receiver-operating characteristics curve (AUC) analysis was used to evaluate the diagnostic performance of peritoneal endometriosis metabolites.

RESULTS

In the discovery set, peritoneal fluid phosphatidylcholine (34:3) and phenylalanyl-isoleucine were significantly increased in peritoneal endometriosis groups compared with control groups, with AUC 0.77 (95% CI 0.61 to 0.92; P = 0.018) and AUC 0.98 (95% CI 0.95 to 1.02; P < 0.001), respectively. In the validation set, phenylalanyl-isoleucine retained discriminatory performance to distinguish peritoneal endometriosis from controls in both peritoneal fluid (AUC 0.77, 95% CI 0.61 to 0.92; P = 0.006) and serum samples (AUC 0.81, 95% CI 0.64 to 0.99; P = 0.004), with notably stronger discrimination between peritoneal endometriosis and controls in proliferative phase.

CONCLUSION

Our preliminary results propose phenylalanyl-isoleucine as a potential biomarker of peritoneal endometriosis, which may be used as a minimally invasive diagnostic biomarker of peritoneal endometriosis.

Construction of Competing Endogenous RNA Networks Incorporating Transcription Factors to Reveal Differences in Granulosa Cells from Patients with Endometriosis

Purpose: This study aimed to reveal the molecular differences in granulosa cells (GCs) from patients with endometriosis (EM). Methods: RNA sequencing was performed on GCs from patients with EM-related infertility (n = 3) and controls (n = 3). Differentially expressed long noncoding RNAs [differentially expressed lncRNAs (DELs), |log2 FC|>4, false discovery rate (FDR) <0.05] and genes [differentially expressed genes (DEGs), |log2 FC|>1.4, FDR <0.05] in patients with EM-related infertility and controls were screened. Protein-protein interaction (PPI) networks of the DEGs were constructed. Then, mRNA-miRNA-lncRNA pairs based on DEGs and DELs were constructed by comprehensive bioinformatics analyses. In addition, overlapping genes identified from both the PPI and mRNA-miRNA-lncRNA pairs were selected. Finally, a competing endogenous RNA (ceRNA) network incorporating transcription factors (TFs) was constructed. Results: A total of 25,806 lncRNAs and 19,684 mRNAs were detected, and 7 DELs and 46 DEGs were identified. Five hub genes from the PPI network were also identified. A single overlapping gene, NR4A2, from both the PPI network and mRNA-miRNA-lncRNA pairs was identified. Finally, a ceRNA network incorporating TFs, including one mRNA (NR4A2), one miRNA (hsa-miR-217), three lncRNAs (XIST, MCM3AP-AS1, and C17orf51), and five TFs (SRF, POLR2A, NRF1, MNT, and TCF7L2), was successfully constructed. Conclusions: The proposed ceRNA network and the prediction of TFs in GCs from EM-related infertility revealed differences in GCs from patients with EM. Importantly, the novel TFs, lncRNAs, miRNAs, and mRNAs involved in the ceRNA network might provide new insights into the underlying molecular mechanisms of EM-related infertility.