Discrimination of serum samples of prostate cancer and benign prostatic hyperplasia with 1H-NMR metabolomics†

IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL Analytical Methods Pub Date : 2024-09-12 DOI:10.1039/D4AY01109K
Mohammed Zniber, Parastoo Vahdatiyekta and Tan-Phat Huynh
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Abstract

Prostate cancer continues to be a prominent health concern for men globally. Current screening techniques, primarily the prostate-specific antigen (PSA) test and digital rectal examination (DRE), possess inherent limitations, with prostate biopsy being the definitive diagnostic procedure. The invasive nature of the biopsy and other drawbacks of current screening tests create the need for non-invasive and more accurate diagnostic methods. This study utilized 1H-NMR (Proton Nuclear Magnetic Resonance) based serum metabolomics to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Serum samples from 40 PCa and 41 BPH patients were analysed using 1H-NMR spectroscopy. PepsNMR was utilized for preprocessing the raw NMR data, and the binned spectra were examined for patterns distinguishing PCa and BPH. Principal component analysis (PCA) showed a moderate separation between PCa and BPH, highlighting the distinct metabolic profiles of both conditions. A logistic regression model was then developed, which demonstrated good performance in distinguishing between the two conditions. The results showed significant variance in multiple metabolites between PCa and BPH, such as isovaleric acid, ethylmalonic acid, formate, and glutamic acid. This research underlines the potential of 1H-NMR-based serum metabolomics as a promising tool for improved prostate cancer screening, offering an alternative to the limitations of current screening methods.

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利用 1H-NMR 代谢组学鉴别前列腺癌和良性前列腺增生的血清样本
前列腺癌仍然是全球男性关注的一个突出健康问题。目前的筛查技术,主要是前列腺特异性抗原(PSA)检测和数字直肠检查(DRE),具有固有的局限性,而前列腺活检则是最终的诊断程序。活检的侵入性和目前筛查测试的其他缺点突出表明,需要非侵入性和更准确的诊断方法。本研究利用基于 1H-NMR(核磁共振)的血清代谢组学来区分前列腺癌(PCa)和良性前列腺增生(BPH)。研究人员使用 1H-NMR 光谱分析了 40 名 PCa 患者和 41 名良性前列腺增生患者的血清样本。利用 PepsNMR 对原始 NMR 数据进行了预处理,并对二进制光谱进行了研究,以找出区分 PCa 和 BPH 的模式。主成分分析(PCA)显示 PCa 和良性前列腺增生症之间有适度的分离,突出了这两种病症不同的代谢特征。此外,还建立了一个逻辑回归模型,该模型在区分两种病症方面表现良好。结果表明,PCa 和良性前列腺增生症的多种代谢物存在明显差异,如异戊酸、乙基丙二酸、甲酸和谷氨酸。这项研究强调了基于 1H-NMR 的血清代谢组学作为改进前列腺癌筛查工具的潜力,为克服当前筛查方法的局限性提供了一种替代方法。
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来源期刊
Analytical Methods
Analytical Methods CHEMISTRY, ANALYTICAL-FOOD SCIENCE & TECHNOLOGY
CiteScore
5.10
自引率
3.20%
发文量
569
审稿时长
1.8 months
期刊介绍: Early applied demonstrations of new analytical methods with clear societal impact
期刊最新文献
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