4.8 Article

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

期刊

ANALYTICAL CHEMISTRY
卷 94, 期 34, 页码 11821-11830

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.2c02072

关键词

-

资金

  1. National Natural Science Foundation of China (NSFC) [91959201]

向作者/读者索取更多资源

The application of rapid and accurate diagnostic methods can significantly improve the survival rates of colorectal cancer. In this study, a non-targeted metabolomic approach based on iEESI-MS was used to identify metabolite ions associated with the progression of colorectal cancer. A support vector machine model was built using 10 differential metabolite ions to distinguish early-stage colorectal cancer from normal tissues, with a high prediction accuracy of 92.6%. The biomarker panel, specifically lysophosphatidylcholine (LPC) (18:0), showed good diagnostic potential in differentiating early-stage colorectal cancer from advanced-stage colorectal cancer.
The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据