Introduction

The inherent sensitivity of metabolomics allows the detection of subtle alterations in biological pathways, making it a powerful tool to study biomarkers and the mechanisms that underlie cancer.

Objectives

The purpose of this work was to characterize the urinary metabolic profile of prostate cancer (PCa) patients and cancer-free controls to obtain a holistic coverage of PCa metabolome.

Methods

Two groups of samples, a training set (n = 41 PCa and n = 42 controls) and an external validation set (n = 18 PCa and n = 18 controls) were analyzed using a dual analytical platform, namely gas chromatography-mass spectrometry (GC–MS) and proton nuclear magnetic resonance spectroscopy (¹H NMR).

Results

The multivariate analysis models revealed a good discrimination between cases and controls with an AUC higher than 0.8, a sensitivity ranging from 67 to 89%, a specificity ranging from 74 to 89% and an accuracy from 73 to 86%, considering the training and external validation sets. A total of 28 metabolites (15 from GC–MS and 13 from ¹H NMR) accounted for the separation. These discriminant metabolites are involved in 14 biochemical pathways, indicating that PCa is highly linked to dysregulation of metabolic pathways associated with amino acids and energetic metabolism.

Conclusion

These findings confirmed the complementary information provided by GC–MS and ¹H NMR, enabling a more comprehensive picture of the altered metabolites, underlying pathways and deepening the understanding of PCa development and progression.

中文翻译：

---

通过结合GC-MS和1H NMR分析平台获得的关于尿液前列腺癌代谢组的新发现。

介绍

代谢组学的内在敏感性允许检测生物学途径中的细微变化，使其成为研究生物标志物和癌症基础机制的有力工具。

目标

这项工作的目的是表征前列腺癌（PCa）患者和无癌对照的尿液代谢状况，以全面了解PCa代谢组。

方法

使用双重分析平台，即气相色谱-质谱联用仪，分析了两组样品，即训练组（n = 41 PCa和n = 42对照）和外部验证组（n = 18 PCa和n = 18对照）。 GC-MS）和质子核磁共振波谱（¹ H NMR）。

结果

多元分析模型显示，考虑到训练和外部因素，AUC高于0.8，灵敏度在67％到89％之间，特异性在74％到89％之间，准确度在73％到86％之间的情况下，病例和对照之间存在良好的区别。验证集。总共28种代谢物（GC-MS中的15种和¹ H NMR中的13种）被认为是分离。这些判别性代谢物涉及14个生化途径，表明PCa与氨基酸和能量代谢相关的代谢途径失调高度相关。

结论

这些发现证实了GC-MS和¹ H NMR所提供的补充信息，从而使人们能够更全面地了解代谢物的变化，潜在的途径并加深对PCa发育和进展的了解。