Principal component analysis (PCA) is an unsupervised pattern recognition algorithm, which is able to effectively eliminate the interference of artificial factors and indicates the sample distribution in principal component space. Taking advantage of this, all data were analyzed by PCA. A score plot of the first two principal components are shown in Fig 2A, radiated B16 cells and untreated B16 cells can be distinguished by first two principal components (PC1 and PC2), suggesting a metabolic profiling difference between radiated B16 cells and untreated B16 cells.