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

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Abstract

Objective

To understand the association between chronic inflammation, cellular senescence, and immunological infiltration in endometriosis.

Methods

Datasets from GEO comprising 108 endometriosis and 97 healthy human samples and the human endometrial stromal cell. Differentially expressed genes were identified using Limma and WGCNA. Inflammatory response-related subtypes were constructed using consensus clustering analysis. The CIBERSORT algorithm and correlation analyses assessed immune cell infiltration. LASSO, SVM-RFE, and RF identified diagnostic genes. Functional enrichment analysis and multifactor regulatory networks established functional effects. Nomograms, internal and external validations, and in vitro experiments validated the diagnostic genes.

Results

Inflammatory response subtypes were highly correlated with the immune activities of B and NK cells. Sixteen genes were associated with inflammatory response and cellular senescence and six diagnostic genes (NLK, RAD51, TIMELESS, TBX3, MET, and BTG3) were identified. The six diagnostic gene models had an area under the curve of 0.828 and their expression was significantly downregulated in endometriosis samples. Low expression of NLK and BTG3 promoted the proliferation, migration, and invasion of endometriotic cells.

Conclusions

Inflammatory response subtypes were successfully constructed for endometriosis. Six diagnostic genes related to inflammatory response and cellular senescence were identified and validated. Our study provides novel insights for inflammatory response in endometriosis and markers for endometriosis diagnosis and treatment.

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Data availability

Data will be made available on request.

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Funding

This work was supported by grants from the Science and Technology Commission of Shanghai Municipality (22Y11906100) to Jing Sun and Shanghai Outstanding Academic Leaders Plan to Jing Sun (Year 2019).

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Author notes

  1. Pusheng Yang, Yaxin Miao and Tao Wang have contributed equally to this work.

Authors and Affiliations

  1. Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Tongji University, Shanghai, 200092, China

    Pusheng Yang, Yaxin Miao, Tao Wang & Jing Sun

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  1. Pusheng Yang
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  2. Yaxin Miao
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  3. Tao Wang
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Contributions

Pusheng Yang Conceptualization, Methodology, Software, Visualization, Validation and Writing – original draft. Yaxin Miao, Visualization, Validation. Tao Wang, Validation. Jing Sun: Conceptualization, Supervision, Writing – review & editing. All authors contributed to manuscript revision, read, and approved the submitted version.

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Correspondence to Jing Sun.

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Yang, P., Miao, Y., Wang, T. et al. Identification of diagnostic markers related to inflammatory response and cellular senescence in endometriosis using machine learning and in vitro experiment. Inflamm. Res. (2024). https://doi.org/10.1007/s00011-024-01886-5

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  • DOI: https://doi.org/10.1007/s00011-024-01886-5

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