Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, China.
Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, National Clinical Research Center for Obstetric & Gynecologic Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
BMC Womens Health. 2023 Nov 8;23(1):583. doi: 10.1186/s12905-023-02730-4.
This study aimed to reveal the urine metabolic change of endometrial cancer (EC) patients during fertility-sparing treatment and establish non-invasive predictive models to identify patients with complete remission (CR).
This study enrolled 20 EC patients prior to treatment (PT) and 22 patients with CR, aged 25-40 years. Eligibility criteria consisted of stage IA high-grade EC, lesions confined to endometrium, normal hepatic and renal function, normal urine test, no contraindication for fertility-sparing treatment and no prior therapy. Urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), a technique chosen for its high sensitivity and resolution, allows for rapid, accurate identification and quantification of metabolites, providing a comprehensive metabolic profile and facilitating the discovery of potential biomarkers. Analytical techniques were employed to determine distinct metabolites and altered metabolic pathways. The statistical analyses were performed using univariate and multivariate analyses, logistic regression and receiver operating characteristic (ROC) curves to discover and validate the potential biomarker models.
A total of 108 different urine metabolomes were identified between CR and PT groups. These metabolites were enriched in ascorbate and aldarate metabolism, one carbon pool by folate, and some amino acid metabolisms pathways. A panel consisting of Baicalin, 5beta-1,3,7 (11)-Eudesmatrien-8-one, Indolylacryloylglycine, Edulitine, and Physapubenolide were selected as biomarkers, which demonstrated the best predictive ability with the AUC values of 0.982/0.851 in training/10-fold-cross-validation group, achieving a sensitivity of 0.975 and specificity of 0.967, respectively.
The urine metabolic analysis revealed the metabolic changes in EC patients during the fertility-sparing treatment. The predictive biomarkers present great potential diagnostic value in fertility-sparing treatments for EC patients, offering a less invasive means of monitoring treatment efficacy. Further research should explore the mechanistic underpinnings of these metabolic changes and validate the biomarker panel in larger, diverse populations due to the small sample size and single-institution nature of our study.
本研究旨在揭示子宫内膜癌（EC）患者在保留生育力治疗过程中的尿液代谢变化，并建立非侵入性预测模型以识别完全缓解（CR）患者。
本研究纳入了 20 名治疗前（PT）和 22 名 CR 的 EC 患者，年龄 25-40 岁。入选标准包括 IA 期高级别 EC、病变局限于子宫内膜、肝肾功能正常、尿液检查正常、无保留生育力治疗禁忌证且无既往治疗史。使用超高效液相色谱-质谱联用（UPLC-MS）分析尿液样本，该技术具有灵敏度高、分辨率高的特点，可快速、准确地鉴定和定量代谢物，提供全面的代谢谱，并有助于发现潜在的生物标志物。采用分析技术确定了不同的代谢物和改变的代谢途径。采用单变量和多变量分析、逻辑回归和受试者工作特征（ROC）曲线进行统计分析，以发现和验证潜在的生物标志物模型。
CR 和 PT 组之间共鉴定出 108 种不同的尿液代谢物。这些代谢物在抗坏血酸和醛酸盐代谢、叶酸一碳池和一些氨基酸代谢途径中富集。贝加灵、5β-1,3,7（11）-Eudesmatrien-8-酮、吲哚基丙烯酰甘氨酸、依度利汀和筋骨草内酯被选为生物标志物，它们在训练/10 倍交叉验证组中的 AUC 值分别为 0.982/0.851，具有最佳的预测能力，分别达到 0.975 的灵敏度和 0.967 的特异性。
尿液代谢分析揭示了 EC 患者在保留生育力治疗过程中的代谢变化。预测生物标志物在 EC 患者的保留生育力治疗中具有很大的诊断价值，为监测治疗效果提供了一种非侵入性的方法。由于本研究的样本量小且为单中心性质，应进一步研究这些代谢变化的机制基础，并在更大、更多样化的人群中验证生物标志物组合。
