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通过气相色谱-质谱联用四极杆飞行时间检测尿液中的挥发性有机化合物(VOCs),以区分乳腺癌的局部和转移模型。

Detection of Volatile Organic Compounds (VOCs) in Urine via Gas Chromatography-Mass Spectrometry QTOF to Differentiate Between Localized and Metastatic Models of Breast Cancer.

机构信息

IUPUI, Department of Chemistry and Chemical Biology, Indianapolis, 46202, USA.

Integrated Nanosystems Development Institute, Indianapolis, 46202, USA.

出版信息

Sci Rep. 2019 Feb 21;9(1):2526. doi: 10.1038/s41598-019-38920-0.

Abstract

Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).

摘要

乳腺癌是女性最常见的癌症,目前用于该病的筛查方法不够敏感。挥发性有机化合物(VOCs)包括提供健康和疾病信息的内源性代谢物,这些信息可能有助于开发更好的乳腺癌筛查方法。本研究的目的是通过尿液中 VOCs 的差异,对有肿瘤和无肿瘤的小鼠进行分类,并比较乳腺垫定位的肿瘤和注射到髂动脉的肿瘤细胞。将 4T1.2 肿瘤细胞注射到 BALB/c 小鼠的乳腺垫或髂动脉后,收集尿液,通过固相微萃取浓缩尿液顶空的 VOCs,然后通过气相色谱-质谱四极杆飞行时间分析结果。采用多元和单变量统计分析方法,寻找用于小鼠模型的乳腺癌和转移性乳腺癌的潜在生物标志物。通过五重交叉验证,利用一组 6 种 VOCs 将有无肿瘤的小鼠进行分类,接收器操作特性(ROC)曲线下面积(AUC)为 0.98(95%置信区间[0.85, 1.00])。利用另一组 6 种 VOCs 对乳腺垫和髂动脉肿瘤的小鼠进行分类,ROC AUC 为 0.96(95%置信区间[0.75, 1.00])。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31d/6384920/9be50bab52c5/41598_2019_38920_Fig1_HTML.jpg

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