Deciphering metabolomic networks has been demonstrated to provide valuable information for diagnosing and monitoring diseases. Herein, we report a technique to monitor untargeted urine metabolites to evaluate prostate cancer aggressiveness and treatment outcome. Direct chemical profiling of urine was achieved by a combined procedure of hyphenating laser diode thermal desorption with atmospheric pressure chemical ionization mass spectrometry (LDTD-APCI-MS). We describe a conceptually new approach to monitoring preoperative urinary metabolic alterations associated with prostate cancer recurrence. By evaluating mass/charge (m/z) ratios and peak intensities of ions detected by mass spectroscopy of urine samples, we revealed that intensities at m/z 313.2740 (±0.0003) and 341.3054 (±0.0006) attributable to monoacylglycerol backbone fragments from glycerides can be statistically correlated to disease progression.

中文翻译：

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非靶向尿液代谢物质谱分析辅助多变量统计分析预测前列腺癌治疗结果

已证明破译代谢组学网络可为诊断和监测疾病提供有价值的信息。在这里，我们报告了一种监测非靶向尿液代谢物的技术，以评估前列腺癌的侵袭性和治疗结果。尿液的直接化学分析是通过将激光二极管热解吸与大气压化学电离质谱 (LDTD-APCI-MS) 相结合的方法实现的。我们描述了一种概念上新的方法来监测与前列腺癌复发相关的术前尿液代谢改变。通过评估尿样质谱检测到的离子的质荷比( m / z ) 和峰值强度，我们揭示了m / z处的强度313.2740 (±0.0003) 和 341.3054 (±0.0006) 归因于来自甘油酯的单酰基甘油骨架片段可以与疾病进展在统计学上相关。