Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary's Hospital, Manchester M13 9WL, UK.
Obstetrics and Gynaecology Department, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK.
Carcinogenesis. 2022 May 19;43(4):311-320. doi: 10.1093/carcin/bgac016.
Currently, the only definitive method for diagnosing ovarian cancer involves histological examination of tissue obtained at time of surgery or by invasive biopsy. Blood has traditionally been the biofluid of choice in ovarian cancer biomarker discovery; however, there has been a growing interest in exploring urinary biomarkers, particularly as it is non-invasive. In this systematic review, we present the diagnostic accuracy of urinary biomarker candidates for the detection of ovarian cancer. A comprehensive literature search was performed using the MEDLINE/PubMed and EMBASE, up to 1 April 2021. All included studies reported the diagnostic accuracy using sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. Risk of bias and applicability of included studies were assessed using the QUADAS-2 tool. Twenty-seven studies were included in the narrative synthesis. Protein/peptide biomarkers were most commonly described (n = 18), with seven studies reporting composite scores of multiple protein-based targets. The most frequently described urinary protein biomarker was HE4 (n = 5), with three studies reporting a sensitivity and specificity > 80%. Epigenetic (n = 1) and metabolomic/organic compound biomarkers (n = 8) were less commonly described. Overall, six studies achieved a sensitivity and specificity of >90% and/or an AUC > 0.9. Evaluation of urinary biomarkers for the detection of ovarian cancer is a dynamic and growing field. Currently, the most promising biomarkers are those that interrogate metabolomic pathways and organic compounds, or quantify multiple proteins. Such biomarkers require external validation in large, prospective observational studies before they can be implemented into clinical practice.
目前,诊断卵巢癌的唯一明确方法是在手术时或通过侵入性活检获取组织进行组织学检查。传统上,血液一直是卵巢癌生物标志物发现的首选生物流体;然而,人们对探索尿液生物标志物越来越感兴趣,尤其是因为它是非侵入性的。在本系统评价中,我们展示了用于检测卵巢癌的尿液生物标志物候选物的诊断准确性。使用 MEDLINE/PubMed 和 EMBASE 进行了全面的文献搜索,截至 2021 年 4 月 1 日。所有纳入的研究均报告了使用敏感性和/或特异性和/或接收者操作特征 (ROC) 曲线检测卵巢癌的诊断准确性。使用 QUADAS-2 工具评估了纳入研究的偏倚风险和适用性。在叙述性综合中纳入了 27 项研究。蛋白质/肽生物标志物最常被描述(n = 18),有 7 项研究报告了多个基于蛋白质的靶标组合评分。最常描述的尿液蛋白生物标志物是 HE4(n = 5),有 3 项研究报告的敏感性和特异性>80%。较少描述的是表观遗传(n = 1)和代谢组学/有机化合物生物标志物(n = 8)。总体而言,有 6 项研究达到了>90%的敏感性和特异性和/或 AUC>0.9。对尿液生物标志物检测卵巢癌的评估是一个充满活力和不断发展的领域。目前,最有前途的生物标志物是那些探索代谢组学途径和有机化合物或定量多种蛋白质的生物标志物。在将这些生物标志物纳入临床实践之前,需要在大型前瞻性观察研究中对其进行外部验证。