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泌尿系统癌症的快速筛查:尿液的基于质谱的代谢指纹分析。

Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine.

机构信息

Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Chem Commun (Camb). 2022 Aug 18;58(67):9433-9436. doi: 10.1039/d2cc02329f.

Abstract

Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancer management for early diagnosis and treatment. Urine is a highly accessible specimen type and urine metabolic fingerprints (UMFs) reflect underlying metabolite signatures of GU cancers. Herein, rapid screening of GU cancers is performed using high-throughput extraction of UMFs by mass spectrometry and efficient recognition by machine learning (ML). GU cancer patients can be distinguished with an accuracy of 90.1%. Besides, key biomarkers such as citric acid were found remarkably upregulated in cancer groups, indicating the dysregulated pathways. This approach highlights the potential role of ML in clinical application and demonstrates the expanding utility of UMFs in disease screening.

摘要

泌尿系统(GU)癌症是男性中最常见的恶性疾病之一。快速筛查是 GU 癌症管理的关键,可实现早期诊断和治疗。尿液是一种高度可及的标本类型,尿液代谢指纹(UMFs)反映了 GU 癌症潜在的代谢物特征。在此,通过质谱法进行 UMFs 的高通量提取和机器学习(ML)的有效识别,实现了 GU 癌症的快速筛查。GU 癌症患者的准确率可达 90.1%。此外,还发现柠檬酸等关键生物标志物在癌症组中显著上调,表明存在失调的途径。该方法突出了 ML 在临床应用中的潜在作用,并展示了 UMFs 在疾病筛查中的扩展应用。

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