Development of a Correlative Strategy To Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in Plasma Using Untargeted Metabolomics
Development of a Correlative Strategy To Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in Plasma Using Untargeted Metabolomics
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使用非靶向代谢组学开发血浆中结直肠肿瘤组织衍生的代谢生物标志物的相关策略
DOI：
10.1021/acs.analchem.8b05177
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发表时间：
2019
影响因子：
7.4
通讯作者：
Zhu Zheng-Jiang
中科院分区：
文献类型：
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作者：
Wang Zhuozhong;Cui Binbin;Zhang Fan;Yang Yue;Shen Xiaotao;Li Zhong;Zhao Weiwei;Zhang Yuanyuan;Deng Kui;Rong Zhiwei;Yang Kai;Yu Xiwen;Li Kang;Han Peng;Zhu Zheng-Jiang
利用非靶向代谢组学对生物体液进行代谢谱分析为发现用于临床癌症诊断的代谢物生物标志物提供了有希望的选择。然而，在生物流体中发现的代谢物生物标志物可能不一定反映肿瘤组织的病理状态，这使得这些生物标志物难以重现。在这项研究中，我们开发了一种新的分析策略，通过整合单变量和多变量相关分析方法来发现血浆样品中的肿瘤组织衍生（TTD）代谢物。具体而言，非靶向代谢组学首先用于分析来自34名结直肠癌（CRC）患者的一组配对组织和血浆样本。然后，使用单变量相关分析来选择组织和血浆之间的相关代谢物对，并使用随机森林回归模型来定义血浆样品中的243个TTD代谢物。结直肠癌血浆中TTD代谢产物能够准确反映肿瘤组织的病理状态，具有发现代谢物生物标志物的巨大潜力。因此，我们使用来自CRC患者和性别匹配的息肉对照的一组146个血浆样品进行了临床研究，以发现来自TTD代谢物的代谢物生物标志物。因此，8种代谢产物被选为潜在的生物标志物，用于CRC诊断具有高灵敏度和特异性。对于手术后的CRC患者，由代谢物生物标志物定义的生存风险评分在预测总生存时间（p= 0.022）和无进展生存时间（p= 0.002）方面也表现良好。总之，我们开发了一种新的分析策略，其有效地发现血浆中的肿瘤组织相关代谢物生物标志物，用于癌症诊断和预后。
The metabolic profiling of biofluids using untargeted metabolomics provides a promising choice to discover metabolite biomarkers for clinical cancer diagnosis. However, metabolite biomarkers discovered in biofluids may not necessarily reflect the pathological status of tumor tissue, which makes these biomarkers difficult to reproduce. In this study, we developed a new analysis strategy by integrating the univariate and multivariate correlation analysis approach to discover tumor tissue derived (TTD) metabolites in plasma samples. Specifically, untargeted metabolomics was first used to profile a set of paired tissue and plasma samples from 34 colorectal cancer (CRC) patients. Next, univariate correlation analysis was used to select correlative metabolite pairs between tissue and plasma, and a random forest regression model was utilized to define 243 TTD metabolites in plasma samples. The TTD metabolites in CRC plasma were demonstrated to accurately reflect the pathological status of tumor tissue and have great potential for metabolite biomarker discovery. Accordingly, we conducted a clinical study using a set of 146 plasma samples from CRC patients and gender-matched polyp controls to discover metabolite biomarkers from TTD metabolites. As a result, eight metabolites were selected as potential biomarkers for CRC diagnosis with high sensitivity and specificity. For CRC patients after surgery, the survival risk score defined by metabolite biomarkers also performed well in predicting overall survival time (p= 0.022) and progression-free survival time (p= 0.002). In conclusion, we developed a new analysis strategy which effectively discovers tumor tissue related metabolite biomarkers in plasma for cancer diagnosis and prognosis.