Kupec Tomas, Wittenborn Julia, Kuo Chao-Chung, Najjari Laila, Senger Rebecca, Meyer-Wilmes Philipp, Stickeler Elmar, Maurer Jochen
Department of Gynecology and Obstetrics, University Hospital RWTH Aachen, 52074 Aachen, Germany.
Genomics Facility, Interdisciplinary Center for Clinical Research (IZKF), University Hospital RWTH Aachen, 52074 Aachen, Germany.
J Clin Med. 2025 Jul 21;14(14):5154. doi: 10.3390/jcm14145154.
: Endometriosis is a chronic gynecological condition marked by ectopic endometrial-like tissue, leading to inflammation, pain, and infertility. Diagnosis is often delayed by up to 10 years. Identifying non-invasive biomarkers could facilitate earlier detection. MicroRNAs, known for their stability in biological fluids and role in disease processes, have emerged as potential diagnostic tools. This pilot study investigated whether serum miRNA profiling can differentiate endometriosis from other causes of chronic pelvic pain. : Serum samples from 52 patients (36 with laparoscopically confirmed endometriosis and 16 controls) treated for chronic pelvic pain at a University Endometriosis Centre were analyzed. High-throughput miRNA sequencing was performed. Feature selection reduced 4285 miRNAs to the 20 most informative MiRNAs. Machine learning models, including logistic regression, decision tree, random forest, and support vector machine, were trained and evaluated. : Among the tested machine learning models, support vector machine achieved the best overall performance (accuracy 0.71, precision 0.80), while logistic regression and random forest showed the highest AUC values (0.84 and 0.81, respectively), indicating strong diagnostic potential of serum miRNA profiling. : This study demonstrates the feasibility of using serum miRNA profiling combined with machine learning for the non-invasive classification of endometriosis. The identified miRNA signature shows strong diagnostic potential and could contribute to earlier and more accurate detection of the disease.
子宫内膜异位症是一种慢性妇科疾病，其特征是存在异位的子宫内膜样组织，可导致炎症、疼痛和不孕。诊断往往会延迟长达10年。识别非侵入性生物标志物有助于早期检测。微小RNA以其在生物体液中的稳定性及其在疾病过程中的作用而闻名，已成为潜在的诊断工具。这项初步研究调查了血清微小RNA谱是否能将子宫内膜异位症与慢性盆腔疼痛的其他病因区分开来。
对在一所大学子宫内膜异位症中心接受慢性盆腔疼痛治疗的52名患者（36名经腹腔镜确诊为子宫内膜异位症，16名作为对照）的血清样本进行了分析。进行了高通量微小RNA测序。特征选择将4285种微小RNA减少到20种信息最丰富的微小RNA。对包括逻辑回归、决策树、随机森林和支持向量机在内的机器学习模型进行了训练和评估。
在测试的机器学习模型中，支持向量机的整体性能最佳（准确率0.71，精确率0.80），而逻辑回归和随机森林的AUC值最高（分别为0.84和0.81），表明血清微小RNA谱具有很强的诊断潜力。
这项研究证明了将血清微小RNA谱与机器学习相结合用于子宫内膜异位症非侵入性分类的可行性。所识别的微小RNA特征显示出很强的诊断潜力，可能有助于更早、更准确地检测该疾病。
