Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence.
Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence.
复制标题
DOI：
10.1158/1055-9965.epi-15-1223
复制
发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
Mucci LA
中科院分区：
文献类型：
--
作者：
Kelly RS;Vander Heiden MG;Giovannucci E;Mucci LA
代谢物谱作为一种鉴定预测、诊断和预后生物标志物的手段，越来越多地用于前列腺癌的研究。这篇综述提供了对当前文献的总结和评论。确定了33项探索疾病预测、诊断、进展或治疗反应的前列腺癌人类病例对照研究。除了一个人之外，所有人都证明了代谢产物谱分析能够区分癌症和良性肿瘤，肿瘤侵袭性，复发病例以及对治疗反应良好的病例。在量化生物标志物区分能力的研究子集中，报告了高AUC，其在诊断、预后和疾病复发（包括PSA检测）方面可能优于当前的金标准。独立研究报告的代谢物和相关途径之间存在大量相似性，并强调了异常细胞生长、密集细胞增殖和脂质代谢失调的重要作用。因此，证据的重要性表明，前列腺癌发生和进展特有的代谢改变可能代表潜在的代谢生物标志物。然而，复制和验证最有前途的生物标志物是目前缺乏和一些突出的方法学问题仍有待解决，以最大限度地利用代谢组学在前列腺癌的研究。
Metabolite profiling is being increasing employed in the study of prostate cancer as a means of identifying predictive, diagnostic and prognostic biomarkers. This review provides a summary and critique of the current literature. Thirty-three human case-control studies of prostate cancer exploring disease prediction, diagnosis, progression or treatment response were identified. All but one demonstrated the ability of metabolite profiling to distinguish cancer from benign, tumor aggressiveness, cases who recurred, and those who responded well to therapy. In the subset of studies where biomarker discriminatory ability was quantified, high AUCs were reported that would potentially outperform the current gold standards in diagnosis, prognosis and disease recurrence, including PSA testing. There were substantial similarities between the metabolites and the associated pathways reported as significant by independent studies, and important roles for abnormal cell growth, intensive cell proliferation and dysregulation of lipid metabolism were highlighted. The weight of the evidence therefore suggests metabolic alterations specific to prostate carcinogenesis and progression that may represent potential metabolic biomarkers. However, replication and validation of the most promising biomarkers is currently lacking and a number of outstanding methodological issues remain to be addressed in order to maximize the utility of metabolomics in the study of prostate cancer.