Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 18:14:1130738.
doi: 10.3389/fimmu.2023.1130738. eCollection 2023.

Identification of molecular subtypes and immune infiltration in endometriosis: a novel bioinformatics analysis and In vitro validation

Si-Ji Lv  1 , Jia-Ni Sun  1 , Lei Gan  2 , Jing Sun  1
Affiliations

Identification of molecular subtypes and immune infiltration in endometriosis: a novel bioinformatics analysis and In vitro validation

Si-Ji Lv et al. Front Immunol. .

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.

Keywords: WGCNA; endometriosis; immune cell subset; immune infiltration; molecular subtype; signature.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of methodologies applied to explore the biological characteristics of endometriosis.
Figure 2
Figure 2
Evaluation of immune cell infiltration and immune correlation analysis. (A) Barplot shows the proportion of 22 types of immune cells in endometriosis samples. The column of the graph is sample. (B) Correlation heat map of 22 immune cell infiltrates; Blue represents positive, red represents negative. The depth of the color indicates the strength of the correlation. (C) Changes in enrichment of 10 immune-related pathways. (D) Expression differences of HLA and KIR family genes.
Figure 3
Figure 3
Differential analysis based on immune cell subtypes. (A) PCA analysis based on immune cell subtypes, cluster1 is green, cluster 2 is yellow. (B) Volcano map of differential analysis based on immune cell subtype, blue dots represent low expression and red dots represent high expression. (C) Heat map based on differential analysis of immune cell subsets, cluster1 is green, cluster 2 is red; blue dots represent low expression and red dots represent high expression. PCA, principal component analysis.
Figure 4
Figure 4
Construction and module analysis of the weighted co-expression network. (A) Sample clustering tree based on Euclidean distance. (B) Network topology analysis under various soft threshold power settings. Left: The X-axis represents the soft threshold power, and the Y-axis represents the fitting index of scale-free topological model. Right: The X-axis represents the soft threshold power, and the Y-axis reflects the average connectivity (degree). (C) Clustering tree of genes with different similarities based on topological overlap, and assigned module colors. (D) Association with Module-trait. Each row corresponds to a module, and each column corresponds to a feature. Each cell contains the corresponding correlation and P values. This table is color-coded according to the correlation of the color legends. (E) The intersection Venn diagram of core modules and central genes of WGCNA. WGCNA, weighted gene co-expression network based on immune cell subsets.
Figure 5
Figure 5
Central genes in GO, KEGG, and PATHWAY enrichment analysis. (A) Dot plot of central genes GO analysis. (B) Dot plot of central genes KEGG analysis. (C) Loop diagram of central genes GO analysis. (D) Loop diagram of central genes KEGG analysis. (E) Enrichment diagram of central genes in the complement and coagulation cascade pathways. (F) Enrichment diagram of central genes in the natural killer cell-mediated cytotoxicity pathway. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 6
Figure 6
GSEA and GSVA enrichment analysis of central genes. (A) Cytokine–cytokine receptor interaction. (B) Natural killer cell-mediated cytotoxicity. (C) Cell cycle checkpoints. (D) Cell cycle mitosis. (E) Homology-directed repair. (F) Mitotic prometaphase. (G, H) GSVA analysis, green represents Cluster1, red represents Cluster2, small blue squares represent low expression, and small red squares represent high expression. GSEA, gene set enrichment analysis; GSVA, gene set variation analysis; BP, Biological Process; CC, Cellular Component; MF, Molecular Function.
Figure 7
Figure 7
Characteristics based on endometriosis-related molecular typing. (A) Consensus CDF. (B) Delta region. (C) Consistent matrix at k = 3. The rows and columns of the matrix represent samples. (D) The PCA diagram verifies the stability and reliability of classification. PCA, principal component analysis.
Figure 8
Figure 8
Expression validation of hub genes. (A) qRT-PCR was performed to determine mRNA expression of hub genes in normal eutopic endometrial tissue and ovarian endometriosis tissue. (B) Western blotting was performed to determine the protein expression of RBP4 in normal eutopic endometrial tissue and ovarian endometriosis tissue. EMS, endometriosis. Data are expressed as the mean ± SEM. *P < 0.05, **P < 0.01; ns, non-significant.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Noh EJ, Kim DJ, Lee JY, Park JH, Kim JS, Han JW, et al. . Ureaplasma urealyticum infection contributes to the development of pelvic endometriosis through toll-like receptor 2. Front Immunol (2019) 10:2373. doi: 10.3389/fimmu.2019.02373 - DOI - PMC - PubMed
    1. Zondervan KT, Becker CM, Missmer SA. Endometriosis. N Engl J Med (2020) 382(13):1244–56. doi: 10.1056/NEJMra1810764 - DOI - PubMed
    1. Macer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstet Gynecol Clin North Am (2012) 39(4):535–49. doi: 10.1016/j.ogc.2012.10.002 - DOI - PMC - PubMed
    1. Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN, Vigano P. Endometriosis. Nat Rev Dis Primers (2018) 4(1):9. doi: 10.1038/s41572-018-0008-5 - DOI - PubMed
    1. Lin X, Dai Y, Tong X, Xu W, Huang Q, Jin X, et al. . Excessive oxidative stress in cumulus granulosa cells induced cell senescence contributes to endometriosis-associated infertility. Redox Biol (2020) 30:101431. doi: 10.1016/j.redox.2020.101431 - DOI - PMC - PubMed

Publication types

Substances