Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.

中文翻译：

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根治性前列腺切除术后前列腺癌复发的术前代谢特征。

质谱（MS），液相色谱（LC）分离，核磁共振（NMR）光谱学和大数据分析的技术进步已使在“组学”或系统级研究代谢成为可能。在这里，我们将多平台（NMR + LC-MS）代谢组学方法应用于与前列腺癌复发相关的术前代谢改变的研究。迄今为止，尚无法预测哪些患者即使在前列腺癌根治术后仍会复发。对前列腺癌患者术前采集的血清样品中代谢产物丰度的相关分析（n = 40缓解vs n = 40复发）显示出许多途径的显着改变，包括氨基酸代谢，嘌呤和嘧啶合成，三羧酸循环，色氨酸分解代谢，葡萄糖，和乳酸。脂质组学实验表明，对于许多类别的复发患者，脂质丰富度更高，包括甘油三酸酯，溶血磷脂酰胆碱，磷脂酰乙醇胺，磷脂酰肌醇，甘油二酸酯，酰基肉碱和神经酰胺。机器学习方法导致从单个术前血液样本中选择20种代谢物，从而能够在交叉验证条件下以92.6％的准确度，94.4％的敏感性和91.9％的特异性预测复发。