Bioinformatic analysis reveals endoplasmic reticulum stress-related molecular cluster and immune characterization in endometriosis：implications for disease subtyping and therapeutic strategies
Authors: Huang Erqing , Zhang Ling , Lou Jie , Wang Xiaoli , Chen Lijuan 
Published: April 2024

Background
Numerous investigations have demonstrated the implication of endoplasmic reticulum stress (ERS) in the etiology of endometriosis. Employing bioinformatics methodologies, we conducted an analysis to ascertain the participation of genes associated with endoplasmic reticulum stress in endometriosis disease subtyping and immune infiltration, with the aim of constructing a diagnostic model for the disease.
Materials and Methods
Differential expression analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network construction, and three machine learning algorithms were employed to identify hub genes associated with endoplasmic reticulum stress in endometriosis. Unsupervised cluster analysis was conducted to identify the ERS cluster. The ERS score and immune infiltration score were computed for distinct clusters using the CIBERSORT algorithm. Functional and pathway enrichment analysis was conducted based on the differential expression profiles of genes within the clusters to elucidate their potential biological functions. The differential expression profiles of genes within the clusters were submitted to the Connectivity Map database to identify candidate therapeutic compounds. A diagnostic model was developed utilizing hub genes, and its predictive performance for endometriosis was assessed. Endometrial tissue specimens obtained from patients were subjected to RT-qPCR and immunohistochemistry (IHC) analyses to evaluate the mRNA and protein expression levels of the hub genes.
Results
Von Willebrand factor (VWF), vascular cell adhesion molecule 1 (VCAM1), endothelial PAS domain protein 1 (EPAS1), and coagulation factor VIII (F8) were identified as the ERS-related hub genes in endometriosis. Unsupervised consensus clustering analysis revealed the presence of two stable clusters. Cluster B exhibited significantly higher immune scores compared to cluster A, thereby characterizing cluster B as an immune-enriched cluster and cluster A as a less immune-enriched cluster. Functional enrichment analysis revealed that the differentially expressed genes across the clusters predominantly participated in processes related to cell adhesion and regulation of immune cell activation. Decision curves, clinical impact curves, and calibration curves collectively underscored the robust diagnostic utility of the endometriosis diagnostic model derived from four hub genes. In cluster A, certain adrenergic receptor antagonists, progesterone or progesterone receptor agonists, androgen receptor modulators, and NF-κB pathway inhibitors exhibit promising therapeutic prospects. In contrast, cluster B presents potential therapeutic benefits with certain PKC activators, PPAR receptor agonists, insulin sensitizers, adenylate cyclase activators, and caspase activators. Moreover, the findings obtained from RT-qPCR and IHC assays corroborated the outcomes of the bioinformatic analysis, demonstrating elevated expression levels of both mRNA and protein of endoplasmic reticulum stress (ERS) hub genes in endometriosis tissues.