ArticleJournal of biological engineering2024
Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms.
Article in Journal of biological engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deubiquitinating enzymes in cervical cancer: Molecular mechanisms and therapeutic implications (Review).International journal of oncology · 2026Review
- Single-nucleus analysis of menstrual fluid highlights gene expression differences in epithelial cells of endometriosis donors.BMC medicine · 2026Article
- Neutrophil-Related Gene Signatures for Ischemic Stroke Diagnosis.Current medicinal chemistry · 2026Article
- Endo-MedSAM: a promptable vision foundation model adaptation for uterus segmentation on pelvic MRI in endometriosis.Frontiers in reproductive health · 2026Article
- Article
- A Machine Learning-Based Prediction Model for Deep Infiltrating Endometriosis.International journal of women's health · 2026Article
- Identification of shared pathogenetic mechanisms between endometriosis and RSA based on comprehensive bioinformatics analysis.Journal of assisted reproduction and genetics · 2025Article
- Machine Learning-Derived Diverse Regulated Cell Death Patterns for Therapeutic Target Identification in Glaucoma.International journal of general medicine · 2025Article
- Unravelling the Intricate Link: Mast Cells and Estrogen-Induced Pain Sensitization in Endometriosis.International journal of biological sciences · 2025Review
- Investigating the molecular mechanisms of resveratrol in treating diabetic foot ulcers: a comprehensive analysis of network pharmacology and experiment validation.Frontiers in molecular biosciences · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study addresses the challenges in the early diagnosis of deep infiltrating endometriosis (DIE) by exploring the potential role of the deubiquitinating enzyme USP14. By analyzing the GSE141549 dataset from the Gene Expression Omnibus (GEO) database, using bioinformatics methods and three machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), the key feature gene USP14 was identified. The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786). Further analysis revealed the important role of USP14 in muscle function, cellular growth factor response, and maintenance of chromosome structure, and its close association with various immune cell functions. Immunohistochemical staining confirmed the high expression of USP14 in DIE tissues. This study provides a new molecular target for the early diagnosis of DIE, which holds significant clinical implications and potential application value.
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Registered trials
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