Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Univesità Cattolica del Sacro Cuore, 00168 Rome, Italy.
Unity of Chemistry, Biochemistry and Clinical Molecular Biology, Department of Diagnostic and Laboratory Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
Curr Oncol. 2023 May 10;30(5):4904-4921. doi: 10.3390/curroncol30050370.
Prostate cancer (PCa) continues to be the second most common malignant tumour and the main cause of oncological death in men. Investigating endogenous volatile organic metabolites (VOMs) produced by various metabolic pathways is emerging as a novel, effective, and non-invasive source of information to establish the volatilomic biosignature of PCa. In this study, headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) was used to establish the urine volatilomic profile of PCa and identify VOMs that can discriminate between the two investigated groups. This non-invasive approach was applied to oncological patients (PCa group, = 26) and cancer-free individuals (control group, = 30), retrieving a total of 147 VOMs from various chemical families. This included terpenes, norisoprenoid, sesquiterpenes, phenolic, sulphur and furanic compounds, ketones, alcohols, esters, aldehydes, carboxylic acid, benzene and naphthalene derivatives, hydrocarbons, and heterocyclic hydrocarbons. The data matrix was subjected to multivariate analysis, namely partial least-squares discriminant analysis (PLS-DA). Accordingly, this analysis showed that the group under study presented different volatomic profiles and suggested potential PCa biomarkers. Nevertheless, a larger cohort of samples is required to boost the predictability and accuracy of the statistical models developed.
前列腺癌（PCa）仍然是第二常见的恶性肿瘤，也是男性癌症死亡的主要原因。研究各种代谢途径产生的内源性挥发性有机代谢物（VOMs），作为一种新的、有效的、非侵入性的信息来源，以建立 PCa 的挥发组生物标志物，正变得越来越重要。在本研究中，采用顶空固相微萃取结合气相色谱-质谱法（HS-SPME/GC-MS）来建立 PCa 的尿液挥发组图谱，并鉴定出能够区分两组研究对象的 VOMs。这种非侵入性的方法应用于肿瘤患者（PCa 组，n=26）和无癌症个体（对照组，n=30），从各种化学家族中提取了总共 147 种 VOMs。其中包括萜类、异戊二烯、倍半萜、酚类、硫和呋喃化合物、酮类、醇类、酯类、醛类、羧酸、苯和萘衍生物、碳氢化合物和杂环碳氢化合物。将数据矩阵进行多元分析，即偏最小二乘判别分析（PLS-DA）。因此，该分析表明，所研究的组具有不同的挥发组图谱，并提示了潜在的 PCa 生物标志物。然而，需要更大的样本队列来提高所开发统计模型的可预测性和准确性。
