Drago Denise, Andolfo Annapaola, Mosca Ettore, Orro Alessandro, Nocera Luigi, Cucchiara Vito, Bellone Matteo, Montorsi Francesco, Briganti Alberto
ProMeFa, Proteomics and Metabolomics Facility, Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan 20132, Italy.
Institute of Biomedical Technologies, National Research Council (CNR), Milan 20090, Italy.
Cancer Biol Med. 2021 May 26;18(2):604-15. doi: 10.20892/j.issn.2095-3941.2020.0617.
Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH).
We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy.
We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1-2 and 4-5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3).
A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.
目前正在做出重大努力,以识别用于前列腺癌(PCa)诊断和风险分层的新型生物标志物。代谢组学在生物标志物发现中可能是一种非常有用的方法,因为代谢物是生物样本中疾病特征的重要读出指标。我们旨在确定一种代谢组学特征,该特征能够准确区分患有临床显著性PCa的男性与受良性前列腺增生(BPH)影响的男性。
我们首先对25例BPH患者和25例患有临床显著性PCa(定义为Gleason评分≥3 + 4)的男性的前列腺分泌液尿液(EPS-尿液)进行了非靶向代谢组学分析,使用超高效液相色谱串联质谱法。手术治疗后经组织学确诊。然后将EPS-尿液代谢组学方法应用于一个更大的前瞻性队列,该队列由92例因临床怀疑PCa而在活检前接受多参数磁共振成像的连续患者组成。
我们建立了一种能够准确区分PCa与良性组织的新型代谢组学特征。在前列腺影像报告和数据系统(PI-RADS)分类的所有亚组中,一种代谢组学特征与临床显著性PCa相关(当PI-RADS在1-2范围内时,准确率分别为100%和89.13%,当PI-RADS为3时,在更关键的病例中准确率为87.50%,当PI-RADS在4-5范围内时)。
代谢物和临床变量的组合可以有效地帮助识别可能被当前成像技术忽视的PCa患者。EPS-尿液中的代谢物应有助于确定PCa的诊断途径,从而改善PCa的检测并减少不必要的前列腺活检数量。