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

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

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.

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.

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. Similar content being viewed by others Data availability Data will be made available on request.

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Corresponding author Ethics declarations Conflict of interest The authors declare no competing interests. Additional information Responsible Editor: John Di Battista. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Below is the link to the electronic supplementary material. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article 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. 73, 1107–1122 (2024). https://doi.org/10.1007/s00011-024-01886-5 Received: Revised: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s00011-024-01886-5