Tokarz Janina, Adamski Jerzy, Rižner Tea Lanišnik
Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, German Research Centre for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.
German Centre for Diabetes Research, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany.
J Pers Med. 2020 Dec 21;10(4):294. doi: 10.3390/jpm10040294.
This systematic review analyses the contribution of metabolomics to the identification of diagnostic and prognostic biomarkers for uterine diseases. These diseases are diagnosed invasively, which entails delayed treatment and a worse clinical outcome. New options for diagnosis and prognosis are needed. PubMed, OVID, and Scopus were searched for research papers on metabolomics in physiological fluids and tissues from patients with uterine diseases. The search identified 484 records. Based on inclusion and exclusion criteria, 44 studies were included into the review. Relevant data were extracted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist and quality was assessed using the QUADOMICS tool. The selected metabolomics studies analysed plasma, serum, urine, peritoneal, endometrial, and cervico-vaginal fluid, ectopic/eutopic endometrium, and cervical tissue. In endometriosis, diagnostic models discriminated patients from healthy and infertile controls. In cervical cancer, diagnostic algorithms discriminated patients from controls, patients with good/bad prognosis, and with/without response to chemotherapy. In endometrial cancer, several models stratified patients from controls and recurrent from non-recurrent patients. Metabolomics is valuable for constructing diagnostic models. However, the majority of studies were in the discovery phase and require additional research to select reliable biomarkers for validation and translation into clinical practice. This review identifies bottlenecks that currently prevent the translation of these findings into clinical practice.
本系统评价分析了代谢组学在子宫疾病诊断和预后生物标志物识别中的作用。这些疾病通过侵入性手段诊断，这会导致治疗延迟和临床结局较差。因此需要新的诊断和预后方法。检索了PubMed、OVID和Scopus数据库，以查找有关子宫疾病患者生理体液和组织中代谢组学的研究论文。检索共识别出484条记录。根据纳入和排除标准，44项研究被纳入本评价。按照PRISMA（系统评价和Meta分析的首选报告项目）清单提取相关数据，并使用QUADOMICS工具评估质量。所选的代谢组学研究分析了血浆、血清、尿液、腹腔液、子宫内膜、宫颈阴道液、异位/在位子宫内膜和宫颈组织。在子宫内膜异位症中，诊断模型可区分患者与健康对照和不孕对照。在宫颈癌中，诊断算法可区分患者与对照、预后良好/不良的患者以及对化疗有/无反应的患者。在子宫内膜癌中，有几个模型可区分患者与对照以及复发患者与未复发患者。代谢组学在构建诊断模型方面具有重要价值。然而，大多数研究仍处于发现阶段，需要进一步研究以选择可靠的生物标志物进行验证并转化为临床实践。本评价确定了目前阻碍这些研究结果转化为临床实践的瓶颈。
