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Identifying Immune Cell Infiltration and Hub Genes Related to M2 Macrophages in Endometriosis by Bioinformatics Analysis

  • Gynecologic Oncology: Original Article
  • Published:
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Abstract

Endometriosis (EM) is a chronic, estrogen-dependent inflammatory disease. Presently, the pathophysiology of EM is still unclear, and numerous studies have established that the immune system plays a major role in the pathophysiology of EM. Six microarray datasets were downloaded from the GEO public database. A total of 151 endometrial samples (72 ectopic endometria and 79 controls) were included in this study. CIBERSORT and ssGSEA were applied to calculate the immune infiltration of EM and control samples. Moreover, we validated four different correlation analyses to explore immune microenvironment of EM and finally identified M2 macrophage-related hub genes and further conducted the specific immunologic signaling pathway analysis by GSEA. The logistic regression model was investigated by ROC and further validated by two external datasets. From the results of the two immune infiltration assays, we concluded that M2 macrophages, regulatory T cells (Tregs), M1 macrophages, activated B cells, T follicular helper cells, activated dendritic cells, and resting NK cells have a significant difference between control and EM tissues. Through multidimensional correlation analysis, we found that macrophages play an important central role in cell-to-cell interactions, especially M2 macrophages. Four immune-related hub genes, namely FN1, CCL2, ESR1, and OCLN, are closely related to M2 macrophages and play a crucial role in the occurrence and immune microenvironment of endometriosis. The combined AUC of ROC prediction model in test and validation sets were 0.9815 and 0.8206, respectively. We conclude that M2 macrophages play a central role in the immune-infiltrating microenvironment of EM.

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Data Availability

This study’s six GEO datasets (GSE7305, GSE23339, GSE11691, GSE25628, GSE37837, and GSE120103 datasets) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Immune-related genes (IRGs) were obtained from the Immunology Database and Analysis Portal (ImmPort) (https://immport.org).

Code Availability

All code used during the study are available from the corresponding author by request.

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Acknowledgements

We appreciate the unrestricted use of the GEO databases.

Funding

This work was supported by the National Key Research and Development Program of Ningbo (grant number 202003N4289) and Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (grant number 2021440659). This work was supported by the Science and Technology Programs of Ningbo (grant no. 202003N4289) and the Medical and Health Science and Technology Program of Zhejiang Province, China (grant no. 2021440659).

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Authors and Affiliations

  1. Department of Obstetrics and Gynecology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315010, China

    Tianhong Zhu, Yongming Du, Bohong Jin, Fubin Zhang & Yutao Guan

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  1. Tianhong Zhu
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Contributions

Tianhong Zhu, Yongming Du, and Yutao Guan contributed to conception and design. Tianhong Zhu and Bohong Jin carried out the analysis. Tianhong Zhu and Yutao Guan wrote the paper. Tianhong Zhu and Fubin Zhang collected and processed the data. Yutao Guan reviewed and edited the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yutao Guan.

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Ethics Approval

This study is conducted based on the GEO database and is not involved in animal or human experiments. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues in this study.

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The authors declare no competing interests.

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Supplementary Table 1 Validating the expression of 10 hub genes with GSE120103.

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Zhu, T., Du, Y., Jin, B. et al. Identifying Immune Cell Infiltration and Hub Genes Related to M2 Macrophages in Endometriosis by Bioinformatics Analysis. Reprod. Sci. 30, 3388–3399 (2023). https://doi.org/10.1007/s43032-023-01227-7

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  • DOI: https://doi.org/10.1007/s43032-023-01227-7

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