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Inflammation Research

04-05-2024 | Endometriosis | Original Research Paper

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

Authors: Pusheng Yang, Yaxin Miao, Tao Wang, Jing Sun

Published in: Inflammation Research

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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.
Appendix
Available only for authorised users
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Metadata
Title
Identification of diagnostic markers related to inflammatory response and cellular senescence in endometriosis using machine learning and in vitro experiment
Authors
Pusheng Yang
Yaxin Miao
Tao Wang
Jing Sun
Publication date
04-05-2024
Publisher
Springer International Publishing
Keyword
Endometriosis
Published in
Inflammation Research
Print ISSN: 1023-3830
Electronic ISSN: 1420-908X
DOI
https://doi.org/10.1007/s00011-024-01886-5
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WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

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