Dipartimento di Chimica, Università degli Studi di Torino, Via P. Giuria 7, 10125 Torino, Italy.
Centro Regionale Antidoping e di Tossicologia "A. Bertinaria", Regione Gonzole 10/1, 10043 Orbassano, Italy.
Molecules. 2019 Aug 22;24(17):3063. doi: 10.3390/molecules24173063.
Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares-discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach.
前列腺特异性抗原（PSA）是前列腺癌（PCa）筛查的主要生物标志物，其具有高灵敏度（高于 80%），但其特异性较差（仅为 30%，欧洲截断值为 4ng/mL）。这导致了大量不必要的活检，既给患者带来了风险，也给国家医疗保健系统带来了成本。因此，最近人们努力发现新的用于 PCa 筛查的生物标志物，包括我们提出的使用多变量统计学解释多参数尿甾体谱的建议。这种方法已经扩展到通过应用非靶向性尿液代谢组学来研究新的疑似生物标志物。从 91 名患者（43 名患有 PCa；48 名患有良性增生）中采集尿液样本，在碱性和酸性条件下进行去共轭、提取，用不同的试剂衍生化，并用不同的气相色谱柱进行分析。从全扫描电子碰撞质谱获得三维数据。使用 PARADISe 软件结合 NIST 库计算 PARAFAC2 模型，提取有意义的成分（疑似生物标志物），并生成半定量数据集。在变量选择后，建立了偏最小二乘判别分析分类模型，表现出有希望的性能。所选的生物标志物需要进一步验证，可能还需要再次进行靶向研究。
