Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies

代谢组学 前列腺癌 生物标志物发现 肌氨酸 生物标志物 医学 癌症 代谢组 癌症生物标志物 前列腺 计算生物学 生物信息学 蛋白质组学 生物 内科学 基因 氨基酸 甘氨酸 生物化学
作者
Ana Rita Lima,Maria de Lourdes Bastos,Márcia Carvalho,Paula Guedes de Pinho
出处
期刊:Translational Oncology [Elsevier]
卷期号:9 (4): 357-370 被引量:104
标识
DOI:10.1016/j.tranon.2016.05.004
摘要

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.
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