代谢物
代谢组学
结直肠癌
代谢物分析
生物标志物
化学
癌症
病态的
肿瘤科
内科学
计算生物学
生物信息学
医学
生物
生物化学
作者
Zhuozhong Wang,Binbin Cui,Fan Zhang,Yue Yang,Xiaotao Shen,Zhong Li,Weiwei Zhao,Yuanyuan Zhang,Kui Deng,Zhiwei Rong,Kai Yang,Xiwen Yu,Kang Li,Peng Han,Zheng‐Jiang Zhu
标识
DOI:10.1021/acs.analchem.8b05177
摘要
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.
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