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尿中前列腺癌两性离子和带正电荷化合物的非靶向代谢组学研究。

Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine.

机构信息

Dipartimento di Chimica, Sapienza Università di Roma, Piazzale Aldo Moro 5, 00185, Rome, Italy.

Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain.

出版信息

Anal Chim Acta. 2021 May 8;1158:338381. doi: 10.1016/j.aca.2021.338381. Epub 2021 Mar 12.

Abstract

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-year-old men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate Curve-Resolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research.

摘要

前列腺癌是全球导致癌症相关死亡的主要原因之一,主要发生在 50 岁以上的男性中。如今,迫切需要发现替代前列腺特异性抗原的生物标志物,因为它不能区分前列腺增生和有临床意义的前列腺癌。在本文中,收集并分析了 32 份前列腺增生和 41 份前列腺癌尿液样本。使用包含石墨化碳黑固相萃取、亲水相互作用色谱分离和非靶向高分辨率质谱分析的分析平台,研究了极性和带正电荷的代谢物。尽管之前的研究报道了这些化合物在癌症生物标志物发现中的重要性,但它们在常见代谢组学实验中经常被忽视。通过 UHPLC-HRMS 生成的复杂代谢组学大数据集,使用基于 MS 感兴趣区域数据选择的 ROIMCR 程序进行分析,并通过多变量曲线分辨交替最小二乘化学计量学方法对其进行分析。这种方法允许对两个数据集差异表达的代谢物进行解析和初步鉴定。其中,氨基酸和肉碱衍生物被初步鉴定,突出了所提出的方法在癌症生物标志物研究中的重要性。

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