Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite-set were compared pathways across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. 135 metabolites were significantly different (adjusted p<0.05) in tumor vs normal tissue, and pathway analysis identified one sugar metabolism pathway (adjusted p=0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted p<0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytical challenges that future studies should consider. Implications: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

中文翻译：

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肿瘤组织和血清中前列腺癌 Gleason 评分的代谢组学

Gleason 评分是衡量前列腺肿瘤分化的指标，是诊断时致命性前列腺癌的最强预测指标。肿瘤和患者血清的代谢组学分析可以识别侵袭性疾病的生物标志物，并导致开发一种侵入性较小的检测方法来进行主动监测。进行了前列腺组织和血清样品的代谢组学分析。代谢物水平和代谢物组在 Gleason 评分中进行了比较。机器学习算法经过训练和调整，可以从代谢物数据中预测转化或分化状态。135 种代谢物在肿瘤与正常组织中存在显着差异（调整后的 p<0.05），通路分析确定了一种糖代谢途径（调整后的 p=0.03）。机器学习识别出预测肿瘤与正常组织的特征（AUC 为 0.82 ± 0.08）。在肿瘤组织中，25 种代谢物与 Gleason 评分相关（未经调整的 p<0.05），4 种在高级别增加，而其余的在低级别富集。虽然焦谷氨酰胺和 1,5-脱水葡萄糖醇在同一患者的组织和血清之间存在相关性（分别为 0.73 和 0.72），但没有代谢物始终与血清中的 Gleason 评分相关。在肿瘤组织中鉴定了先前报道的以及具有不同丰度的新代谢物。然而，没有获得 Gleason 评分的“代谢物特征”。这可能是由于未来研究应考虑的研究设计和分析挑战。启示：代谢分析可以区分良性和肿瘤组织。