建立顶空固相微萃取-气相色谱-高分辨质谱法分析尿液中挥发性有机化合物的方法：在乳腺癌生物标志物发现中的应用。

Development of a headspace-solid phase microextraction gas chromatography-high resolution mass spectrometry method for analyzing volatile organic compounds in urine: Application in breast cancer biomarker discovery.

机构信息

Research Center of Analytical Instrumentation, Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an, Shaanxi 710069, China.

Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Xi'an, Shaanxi 710032, China.

出版信息

Clin Chim Acta. 2023 Feb 1;540:117236. doi: 10.1016/j.cca.2023.117236. Epub 2023 Jan 27.

DOI:10.1016/j.cca.2023.117236

PMID:36716910

Abstract

BACKGROUND AND AIM

Breast cancer (BC) is the leading cause of cancer-related death in females. The development of non-invasive methods for the early diagnosis of BC still remains challenge. Here, we aimed to discover the urinary volatile organic compounds (VOCs) pattern of BC patients and identify potential VOC biomarkers for BC diagnosis.

METHODS

Urine samples were analyzed by headspace-solid phase microextraction (HS-SPME) combined with gas chromatography-high resolution mass spectrometry (GC-HRMS). To assure reliable analysis, the factors influencing HS-SPME extraction efficiency were comprehensively investigated and optimized by combing the Plackett-Burman design (PBD) with the central composite design (CCD). The established HS-SPME/GC-HRMS method was validated and applied to analyze urine samples from BC patients (n = 80) and healthy controls (n = 88).

RESULTS

A total number of 134 VOCs belonging to distinct chemical classes were identified by GC-HRMS. BC patients demonstrated unique urinary VOCs pattern. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation between BC patients and healthy controls. Eight potential VOC biomarkers were identified using multivariate and univariate statistical analysis. The predictive ability of candidate VOC biomarkers was further investigated by the random forest (RF) algorithm. The candidate VOC biomarkers yielded 76.3% sensitivity and 85.4% specificity on the training set, and achieved 76.0% sensitivity and 92.3% specificity on the validation set.

CONCLUSIONS

Overall, this work not only established a standardized HS-SPME/GC-HRMS approach for urinary VOCs analysis, but also highlighted the value of urinary VOCs for BC diagnosis. The knowledge gained from this study paves the way for early diagnosis of BC using urine in a non-invasive manner.

摘要

背景与目的

乳腺癌（BC）是女性癌症相关死亡的主要原因。开发用于 BC 早期诊断的非侵入性方法仍然是一个挑战。在这里，我们旨在发现 BC 患者的尿液挥发性有机化合物（VOC）模式，并确定用于 BC 诊断的潜在 VOC 生物标志物。

方法

通过顶空固相微萃取（HS-SPME）与气相色谱-高分辨率质谱（GC-HRMS）相结合分析尿液样本。为了确保可靠的分析，通过结合 Plackett-Burman 设计（PBD）和中心复合设计（CCD）综合考察和优化影响 HS-SPME 萃取效率的因素。验证并应用建立的 HS-SPME/GC-HRMS 方法分析 80 例 BC 患者和 88 例健康对照者的尿液样本。

结果

GC-HRMS 共鉴定出 134 种属于不同化学类别的 VOC。BC 患者表现出独特的尿液 VOC 模式。正交偏最小二乘判别分析（OPLS-DA）显示 BC 患者与健康对照组之间存在明显分离。通过多变量和单变量统计分析鉴定出 8 个潜在的 VOC 生物标志物。使用随机森林（RF）算法进一步研究候选 VOC 生物标志物的预测能力。候选 VOC 生物标志物在训练集上的灵敏度为 76.3%，特异性为 85.4%，在验证集上的灵敏度为 76.0%，特异性为 92.3%。