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<p>Endometriosis is a common disease among women of childbearing age, and endoplasmic reticulum stress (ERS), a response involved in regulating protein homeostasis, has been linked to its pathogenesis. To identify ERS-related hub genes, this study sequentially employed differential expression analysis, weighted gene co-expression network analysis (WGCNA), protein–protein interaction (PPI) network construction, and three machine learning algorithms. These methods led to the identification of four hub genes: Von Willebrand factor (VWF), vascular cell adhesion molecule 1 (VCAM1), endothelial PAS domain protein 1 (EPAS1), and coagulation factor VIII (F8). Unsupervised cluster analysis was conducted to categorize samples into ERS clusters, and the CIBERSORT algorithm was used to calculate immune infiltration scores, revealing two stable clusters. Cluster B was defined as “immune-enriched” with significantly higher immune scores, while Cluster A was “less immune-enriched”. Functional enrichment analysis of differentially expressed genes (DEGs) between the clusters highlighted cell adhesion and regulation of immune cell activation as key to cluster-specific phenotypes. A diagnostic model built with the four hub genes showed robust utility via validation curves, confirming their clinical relevance. DEGs from each cluster were screened in the Connectivity Map database to identify cluster-specific therapeutic agents. RT-qPCR and immunohistochemistry (IHC) validated that both mRNA and protein levels of the four hub genes were elevated in endometriosis tissues, supporting the bioinformatics findings. Overall, this study links ERS-related hub genes to endometriosis subtyping, immune infiltration, and diagnostics, providing a basis for personalized treatments and a potential clinical tool.</p>

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Identified endoplasmic reticulum stress-related molecular cluster and immune characterization in endometriosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月4日

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  • Erqing Huang,
  • Ling Zhang,
  • Jie Lou,
  • Xiaoli Wang,
  • Lijuan Chen

摘要

Endometriosis is a common disease among women of childbearing age, and endoplasmic reticulum stress (ERS), a response involved in regulating protein homeostasis, has been linked to its pathogenesis. To identify ERS-related hub genes, this study sequentially employed differential expression analysis, weighted gene co-expression network analysis (WGCNA), protein–protein interaction (PPI) network construction, and three machine learning algorithms. These methods led to the identification of four hub genes: Von Willebrand factor (VWF), vascular cell adhesion molecule 1 (VCAM1), endothelial PAS domain protein 1 (EPAS1), and coagulation factor VIII (F8). Unsupervised cluster analysis was conducted to categorize samples into ERS clusters, and the CIBERSORT algorithm was used to calculate immune infiltration scores, revealing two stable clusters. Cluster B was defined as “immune-enriched” with significantly higher immune scores, while Cluster A was “less immune-enriched”. Functional enrichment analysis of differentially expressed genes (DEGs) between the clusters highlighted cell adhesion and regulation of immune cell activation as key to cluster-specific phenotypes. A diagnostic model built with the four hub genes showed robust utility via validation curves, confirming their clinical relevance. DEGs from each cluster were screened in the Connectivity Map database to identify cluster-specific therapeutic agents. RT-qPCR and immunohistochemistry (IHC) validated that both mRNA and protein levels of the four hub genes were elevated in endometriosis tissues, supporting the bioinformatics findings. Overall, this study links ERS-related hub genes to endometriosis subtyping, immune infiltration, and diagnostics, providing a basis for personalized treatments and a potential clinical tool.