Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women. 定量SWATH基于尿液的蛋白质组学分析，用于鉴定有症状的女性子宫内膜癌生物标志物。
Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women.
发表日期：2023 Feb 17
作者：
Kelechi Njoku, Andrew Pierce, Bethany Geary, Amy E Campbell, Janet Kelsall, Rachel Reed, Alexander Armit, Rachel Da Sylva, Liqun Zhang, Heather Agnew, Ivona Baricevic-Jones, Davide Chiasserini, Anthony D Whetton, Emma J Crosbie
来源：
BRITISH JOURNAL OF CANCER
摘要：
能够准确分类症状性女性并用于确定性检测的非侵入性子宫内膜癌检测工具将改善患者护理。尿液作为一种简单易采集的生物流体，在癌症检测中具有吸引力。本研究的目的是确定可区分子宫内膜癌患者和症状对照组的基于尿液的蛋白质组特征。该研究是对症状性绝经后妇女（50例癌症和54例对照组）的前瞻性病例-对照研究。自行收集的小便样本经过质谱处理，并使用顺序窗口获得该理论质谱（SWATH-MS）技术大量测试。机器学习技术用于识别重要的区分性蛋白质，并随后使用逻辑回归将它们组合成多标志物面板。最有区分性的蛋白质单独显示出适度的准确性（AUC>0.70）以检测子宫内膜癌。然而，将最有区分性的蛋白质组合的算法在AUC>0.90的情况下表现良好。最佳表现的诊断模型是一个包括SPRR1B、CRNN、CALML3、TXN、FABP5、C1RL、MMP9、ECM1、S100A7和CFI的10个标记物面板，并以0.92（0.96-0.97）的AUC预测子宫内膜癌。基于尿液的蛋白质特征对早期癌症的检测显示出良好的准确性（AUC为0.92（0.86-0.9））。一个患者友好的基于尿液的检测方法可以为症状性女性提供一种非侵入性的子宫内膜癌检测工具。需要在一个更大的独立队列中进行验证。 © 2023作者。
A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.This was a prospective case-control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96-0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86-0.9)).A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.© 2023. The Author(s).