# A Comparison Study on the Metabolites in PC-3, RWPE-1, and Chrysin-Treated PC-3 Cells
## 3 Results
### Multivariate Analysis  
PLS-DA was performed to clearly differentiate metabolic differences in the samples. Figure 1 presents the PLS-DA-derived score plot based on the first (64%) and second (23.6%) principal PLS components. The PLS-DA score plot indicated a separation between group 2 and groups 1 and 3. The parameters of the cross-validation model were component 3, with R2X = 0.955, R2Y = 0.994, and Q2 (cum) = 0.975. The R2Y and Q2Y demonstrated \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Compounds** & **RT 1 (min)** & **RWPE-1 2** & **PC3** & **PC3\_C** & **Quantitative Ion** & **TMS 3** & **RI 4** \\ \hline
2-Keto-3-methylpentanoic acid & 20.11 & 0.97 & 2.41 & N.D. & 89 & 1 & 1192 \\
2-Ketoisocaprio acid & 20.54 & 2.92 & 4.84 & N.D. & 89 & 1 & 1206 \\
2-PYrolidone-5-carboxylic acid & 28.48 & 167.95 & 8.24 & 159.85 & 84 & 1 & 1499 \\
2,3,4-Tribhydroxybutyric acid & 29.30 & 3.41 & 8.23 & 8.53 & 147 & 4 & 1528 \\ Organic acids (5) & 15.86 & 11,412.12 & 13,860.03 & 13,449.80 & 147 & 2 & 1052 \\ Oxalic acid & 18.37 & 9.73 & 43.08 & N.D. & 147 & 2 & 1134 \\ Succinic acid & 23.43 & 40.90 & 53.30 & 36.89 & 147 & 2 & 1307 \\ Malic acid & 27.94 & 4.38 & 8.24 & 4.73 & 147 & 3 & 1478 \\
2-Hydroxyglutaric acid & 30.29 & 0.97 & 12.59 & N.D. & 129 & 3 & 1562 \\ Sugar acid (1) & & & & & & & \\ Glyceric acid & 23.78 & 0.97 & 0.97 & 6.15 & 189 & 3 & 1320 \\ Sugars (7) & & & & & & & \\ Tagatofuranose & 39.42 & 2.42 & 20.18 & 0.48 & 217 & 5 & 1972 \\ Frutocpyranose & 39.98 & 2.43 & 35.23 & N.D. & 204 & 5 & 1804 \\ Frutocse (1) & 42.58 & 255.01 & 250.75 & 245.14 & 103 & 5 & 1861 \\ Frutocse (2) & 43.02 & 182.02 & 165.35 & 191.22 & 103 & 5 & 1871 \\ Glucose (1) & 43.69 & 0.48 & 192.41 & 170.02 & 319 & 5 & 1885 \\ Glucose (2) & 44.54 & N.D. & 24.22 & 20.78 & 319 & 5 & 1904 \\ Glucose (3) & 47.49 & N.D. & 86.82 & 4.74 & 204 & 5 & 1972 \\ Others (2) & & & & & & & \\ Urea & 21.37 & N.D. & 210.52 & N.D. & 147 & 2 & 1235 \\ Phosphoric acid & 22.18 & 996.83 & 1210.09 & 1281.87 & 299 & 3 & 1263 \\ \hline \hline \end{tabular}  
* RT, retention time; 2 Concentration, fluoranthene equivalent ug/mg, mean values (n = 2); 3 TMS, trimethylsilylation; 4 RI, retention index; 5 N.D., not detected.  
\end{table} 
Table 1: _Cont._the robustness of the PLS-DA model. R2Y describes how well the training set data are mathematically reproduced and varies between 0 and 1, with 1 indicating a perfect fit of the model. Q2Y quantifies the ability of the model to reliably predict the outcome of other experiments, with Q2Y values >0.5 being considered to have good predictability [11]. The main metabolites were identified based on a variable importance plot (VIP) values of 1.0. Among the identified metabolites from cell samples, the highest VIP values (1.40-1.02) came from pentanoic acid (1.40), 2-hydroxybutyric acid (1.38), glyceric acid (1.33), glucose (1.29), 2,3,4-trihydroxybutyric acid (1.26), myoinositol (1.20), lactic acid (1.19), phosphoric acid (1.16), valine (1.12), 2-ketoisocaproic acid (1.12), and mannitol (1.02). The correlation matrix shows the pairwise correlation between all variables (X and Y) in the current workset, scaled and transformed into the workset. Each variable is displayed in one row and one column in the correlation matrix, and the correlation between two variables is shown in the cell where the two variables intersect. When the correlation coefficient was close to zero, there was no linear relationship between the terms. The major metabolite correlation matrix for RWPE-1 was hydroxybutyric acid, whereas that for PC-3 was arabinitol and glycine (Figure 2b). The major metabolite in the correlation matrix of the chrysin-treated PC-3 cells was glyceric acid. Figure 1: PCA plot for each group. The sample numbers used in the PCA plot for group 1: RWPE-1 cells, 2; PC-3 cells, 3; chrysin-treated PC-3 cells, 2; respectively. After drawing the peak information matrix extracted from the total ion graph, the PCA plot was generated by analyzing the principal component with Simca-P 17.0. PCA, principal component analysis.