Identification of Molecular Subtypes and Immune Infiltration in Endometriosis: a Novel Bioinformatics Analysis and Validation

Overview

Journal Front Immunol

Specialty Allergy & Immunology

Date 2023 Sep 4

PMID 37662927

Authors

Si-Ji Lv

Jia-Ni Sun

Lei Gan

Jing Sun

Affiliations

Soon will be listed here.

Abstract

Introduction: Endometriosis is a worldwide gynacological diseases, affecting in 6-10% of women of reproductive age. The aim of this study was to investigate the gene network and potential signatures of immune infiltration in endometriosis.

Methods: The expression profiles of GSE51981, GSE6364, and GSE7305 were obtained from the Gene Expression Omnibus (GEO) database. Core modules and central genes related to immune characteristics were identified using a weighted gene coexpression network analysis. Bioinformatics analysis was performed to identify central genes in immune infiltration. Protein-protein interaction (PPI) network was used to identify the hub genes. We then constructed subtypes of endometriosis samples and calculated their correlation with hub genes. qRTPCR and Western blotting were used to verify our findings.

Results: We identified 10 candidate hub genes (GZMB, PRF1, KIR2DL1, KIR2DL3, KIR3DL1, KIR2DL4, FGB, IGFBP1, RBP4, and PROK1) that were significantly correlated with immune infiltration. Our study established a detailed immune network and systematically elucidated the molecular mechanism underlying endometriosis from the aspect of immune infiltration.

Discussion: Our study provides comprehensive insights into the immunology involved in endometriosis and might contribute to the development of immunotherapy for endometriosis. Furthermore, our study sheds light on the underlying molecular mechanism of endometriosis and might help improve the diagnosis and treatment of this condition.

Citing Articles

Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis.

Azeze G, Wu L, Alemu B, Lee W, Fung L, Cheung E J Transl Med. 2024; 22(1):685.

PMID: 39061077 PMC: 11282838. DOI: 10.1186/s12967-024-05474-3.

Identification of diagnostic markers related to inflammatory response and cellular senescence in endometriosis using machine learning and in vitro experiment.

Yang P, Miao Y, Wang T, Sun J Inflamm Res. 2024; 73(7):1107-1122.

PMID: 38704432 DOI: 10.1007/s00011-024-01886-5.

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