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检测宫颈阴道液和血浆中的子宫内膜癌:利用蛋白质组学和机器学习发现生物标志物。

Detection of endometrial cancer in cervico-vaginal fluid and blood plasma: leveraging proteomics and machine learning for biomarker discovery.

机构信息

Division of Cancer Sciences, University of Manchester, School of Medical Sciences, Faculty of Biology, Medicine and Health, 5th Floor Research, St Mary's Hospital, Road, Manchester, M13 9WL, UK; Stoller Biomarker Discovery Centre, Institute of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; Department of Clinical Oncology, Christie NHS Foundation Trust, Manchester, UK.

North Wales Medical School, Bangor University, Bangor, Gwynedd, LL57 2DG, UK.

出版信息

EBioMedicine. 2024 Apr;102:105064. doi: 10.1016/j.ebiom.2024.105064. Epub 2024 Mar 20.

Abstract

BACKGROUND

The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from controls in cervico-vaginal fluid and blood plasma.

METHODS

Blood plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from symptomatic post-menopausal women with (n = 53) and without (n = 65) endometrial cancer. Digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins. The best diagnostic model was determined based on accuracy and model parsimony.

FINDINGS

A protein signature derived from cervico-vaginal fluid more accurately discriminated cancer from control samples than one derived from plasma. A 5-biomarker panel of cervico-vaginal fluid derived proteins (HPT, LG3BP, FGA, LY6D and IGHM) predicted endometrial cancer with an AUC of 0.95 (0.91-0.98), sensitivity of 91% (83%-98%), and specificity of 86% (78%-95%). By contrast, a 3-marker panel of plasma proteins (APOD, PSMA7 and HPT) predicted endometrial cancer with an AUC of 0.87 (0.81-0.93), sensitivity of 75% (64%-86%), and specificity of 84% (75%-93%). The parsimonious model AUC values for detection of stage I endometrial cancer in cervico-vaginal fluid and blood plasma were 0.92 (0.87-0.97) and 0.88 (0.82-0.95) respectively.

INTERPRETATION

Here, we leveraged the natural shed of endometrial tumours to potentially develop an innovative approach to endometrial cancer detection. We show proof of principle that endometrial cancers secrete unique protein signatures that can enable cancer detection via cervico-vaginal fluid assays. Confirmation in a larger independent cohort is warranted.

FUNDING

Cancer Research UK, Blood Cancer UK, National Institute for Health Research.

摘要

背景

子宫腔和下生殖道的解剖连续性使得可以利用源自子宫的生物材料来检测基于非侵入性采样方法的子宫内膜癌。由于其简单性和易于采集,血浆是一种有吸引力的用于癌症检测的生物流体。在这项生物标志物发现研究中,我们旨在鉴定能够准确区分宫颈癌和对照者的宫颈阴道液和血浆中的蛋白质组特征。

方法

从有(n=53)和无(n=65)子宫内膜癌症状的绝经后妇女中采集血浆和 Delphi Screener 收集的宫颈阴道液样本。使用序贯窗口采集所有理论质谱(SWATH-MS)为每个样本生成数字化蛋白质图谱。使用机器学习来识别最具区分性的蛋白质。基于准确性和模型简约性来确定最佳诊断模型。

结果

源自宫颈阴道液的蛋白质特征比源自血浆的蛋白质特征更能准确区分癌症与对照样本。源自宫颈阴道液的 5 种生物标志物蛋白质(HPT、LG3BP、FGA、LY6D 和 IGHM)的蛋白质标志物面板预测子宫内膜癌的 AUC 为 0.95(0.91-0.98),灵敏度为 91%(83%-98%),特异性为 86%(78%-95%)。相比之下,源自血浆的 3 种蛋白质标志物(APOD、PSMA7 和 HPT)的蛋白质标志物面板预测子宫内膜癌的 AUC 为 0.87(0.81-0.93),灵敏度为 75%(64%-86%),特异性为 84%(75%-93%)。在宫颈阴道液和血浆中检测 I 期子宫内膜癌的简约模型 AUC 值分别为 0.92(0.87-0.97)和 0.88(0.82-0.95)。

解释

在这里,我们利用子宫内膜肿瘤的自然脱落,有可能开发出一种创新的子宫内膜癌检测方法。我们证明了原理,即子宫内膜癌分泌独特的蛋白质特征,可通过宫颈阴道液检测实现癌症检测。需要在更大的独立队列中进行确认。

资助

英国癌症研究中心、英国血液癌症协会、英国国家健康研究所。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a59b/10960138/acec8148e17b/gr1.jpg

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