Department of Epidemiology and Biostatistics, Public Health School, Harbin Medical University, Harbin, China.
Clin Chim Acta. 2012 May 18;413(9-10):861-8. doi: 10.1016/j.cca.2012.01.026. Epub 2012 Jan 30.
Discrimination between epithelial ovarian cancer (EOC) and benign ovarian tumor (BOT) has always been difficult in clinical practice. We investigated the application of metabolomics in distinguishing EOC and BOT and tried to discover valuable biomarkers.
Plasma metabolomic profiling was performed using ultra-performance liquid chromatography mass spectrometry (UPLC/MS). Partial least-squares discriminant analysis was employed to classify EOC and BOT, and reveal their metabolic differences. The area under the receiver-operating characteristic curve (AUC) was utilized to evaluate the predictive performance of the metabolic profiles for external validation set.
The metabolomic profiles consisting of 535 metabolites revealed a clear separation between EOC and BOT, with AUC of 0.86 for the external validation set. 6 metabolic biomarkers were identified, and the plasma concentrations of the 4 ascertained biomarkers (L-tryptophan, LysoPC(18:3), LysoPC(14:0), and 2-Piperidinone) were lower in EOC patients than those in BOT patients. Among them, tryptophan and LysoPC have been suspected to participate in cancer progression, and 2-Piperidinone might be a novel biomarker for EOC.
Metabolomics could be used to discriminate EOC from BOT in clinical practice, and the identified metabolic biomarkers might be important on investigating the biological mechanisms of EOC.
上皮性卵巢癌（EOC）与良性卵巢肿瘤（BOT）的鉴别在临床实践中一直存在困难。我们研究了代谢组学在鉴别 EOC 和 BOT 中的应用，并试图发现有价值的生物标志物。
采用超高效液相色谱-质谱联用技术（UPLC/MS）进行血浆代谢组学分析。采用偏最小二乘判别分析（PLS-DA）对 EOC 和 BOT 进行分类，揭示其代谢差异。采用受试者工作特征曲线（ROC）下面积（AUC）评估代谢谱对外部验证集的预测性能。
由 535 种代谢物组成的代谢组学图谱清晰地区分了 EOC 和 BOT，外部验证集的 AUC 为 0.86。鉴定出 6 种代谢生物标志物，在 EOC 患者中，确定的 4 种生物标志物（L-色氨酸、LysoPC（18：3）、LysoPC（14：0）和 2-哌啶酮）的血浆浓度均低于 BOT 患者。其中，色氨酸和 LysoPC 已被怀疑参与癌症进展，2-哌啶酮可能是 EOC 的一种新型生物标志物。
代谢组学可用于临床鉴别 EOC 和 BOT，所鉴定的代谢标志物可能对研究 EOC 的生物学机制具有重要意义。
