Evidence map›Paper›PMID 41188339›Full record

ArticleScientific reports2025

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

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

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Erqing Huang *Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Ling Zhang *Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Jie LouDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Xiaoli WangDepartment of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. 2003xh1074@hust.edu.cn.
Lijuan ChenDepartment of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. chenlj@hust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

EndometriosisEndoplasmic Reticulum StressCluster AnalysisComputational BiologyFemaleGene Expression ProfilingGene Regulatory NetworksHumansProtein Interaction MapsBioinformatic analysisEndometriosisEndoplasmic reticulum stressImmune infiltrationStromal cells

Identifiers

PMID41188339
PMCPMC12586515

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.