Polyamines, one of the most important kind of biomarkers in cancer research, were investigated in order to characterize different cancer types. An integrative approach which combined ultra-high performance liquid chromatography—tandem mass spectrometry detection and multiple statistical data processing strategies including outlier elimination, binary logistic regression analysis and cluster analysis had been developed to discover the characteristic biomarkers of lung and liver cancer. The concentrations of 14 polyamine metabolites in biosamples from lung (n = 50) and liver cancer patients (n = 50) were detected by a validated UHPLC-MS/MS method. Then the concentrations were converted into independent variables to characterize patients of lung and liver cancer by binary logic regression analysis. Significant independent variables were regarded as the potential biomarkers. Cluster analysis was engaged for further verifying. As a result, two values was discovered to identify lung and liver cancer, which were the product of the plasma concentration of putrescine and spermidine; and the ratio of the urine concentration of S-adenosyl-l-methionine and N-acetylspermidine. Results indicated that the established advanced method could be successfully applied to characterize lung and liver cancer, and may also enable a new way of discovering cancer biomarkers and characterizing other types of cancer.

Headache is a common episodic or chronic neurologic disorder. Treatment options and diagnosis are restricted by an incomplete understanding of disease pathology and the lack of diagnostic markers. Wu-Zhu-Yu decoction (WZYD), a traditional Chinese medicine (TCM) formula containing four TCM herbs, is commonly used in the treatment of headache in China. To deeply understand more about headache and investigate the pain-relief mechanism of WZYD, a comprehensive metabolomics study combined with multivariate data processing strategy was carried out. An LC-high resolution mass spectrometry-based metabolomics approach was applied to characterize metabolic biomarker candidates. Multiple pattern recognition including principal component analysis-discriminant analysis, partial least squares-discriminant analysis and hierarchical cluster analysis were used to determine groups and confirm important variables. A total of 17 potential biomarkers were characterized and related metabolic pathways were identified. The study demonstrated that the established metabolomics strategy is a powerful approach for investigating the mechanism of headache attack and WZYD. In addition, the approach may highlight biomarkers and metabolic pathways and can capture subtle metabolite changes from headache, which may lead to an improved mechanism understanding of central nervous system diseases and TCM treatment.