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

Pusheng Yang, Yaxin Miao, Tao Wang, Jing Sun
Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024 · vol. 73(7) , pp. 1107–1122 · doi:10.1007/s00011-024-01886-5 · PMID:38704432 · W4396633325
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⚙ AI-generated summary by claude@2026-06+body, 2026-06-06 ⓘ

Machine learning and in vitro experiments identified six diagnostic genes (NLK, RAD51, TIMELESS, TBX3, MET, and BTG3) associated with inflammatory response and cellular senescence in endometriosis.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-06 ⓘ

This study investigated associations between chronic inflammation, cellular senescence, and immune-cell infiltration in endometriosis using two GEO datasets (108 endometriosis vs 97 healthy samples) plus human endometrial stromal cells. Using limma and WGCNA to identify differentially expressed genes, consensus clustering to define inflammatory response subtypes, CIBERSORT to estimate immune infiltration, and machine-learning feature selection (LASSO, SVM-RFE, RF) with validation via internal/external tests and in vitro experiments, the authors found inflammatory-response subtypes strongly correlated with B and NK cell immune activities. Sixteen genes linked inflammatory response and cellular senescence, and six downregulated diagnostic genes (NLK, RAD51, TIMELESS, TBX3, MET, BTG3) produced an AUC of 0.828; in vitro, low NLK and BTG3 promoted endometriotic-cell proliferation, migration, and invasion. A key caveat is that all diagnostic discovery/validation relied on retrospective GEO transcriptomic data rather than prospective cohorts. This paper is centrally about endometriosis — it identifies inflammatory-response and cellular-senescence–related diagnostic gene markers and validates their functional effects in endometriotic cells.

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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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Condition tags

endometriosis

MeSH descriptors

Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence Cellular Senescence

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