# A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer
## 3 Results  
Results were obtained by analyzing serum samples from 230 subjects. Age and sex distribution was not statistically different between the three studied classes (healthy subjects [CTRL], high-grade [BCH], and low-grade [BCL] bladder cancer patients). On the contrary, current or past cigarette smoking was more frequent in bladder cancer (BCH and BCL) patients compared to controls. Moreover, muscle invasive cancer was correlated with more frequent high-grade cancer compared to low grade. The characteristics of the three classes of subjects are summarized in Table 1.  
Statistical analysis revealed that 19 metabolites showed a statistically different fold change \(\geq\pm\)2 (Figure 1A) in CTRL vs. all BC patients. These metabolites are: phosphate, glycine, diethylene glycol, urea, oxalic acid, pyroglutamic acid, propanoic acid, glyceraldehyde-3-phosphate, dihydroxyacetone phosphate, ornithine, proline, glutamic acid, phenol, valine, phenylalanine, 1-methylhypoxanthine, arachidic acid, glutamine, and creatinine. \begin{table}
\begin{tabular}{c c c c} \hline  & **Low Grade Bladder** & **High Grade Bladder** & **Control** \\  & **Cancer (BCL) (\(\mu\) = 55)** & **Cancer (BCH) (\(\mu\) = 94)** & **(\(\mu\) = 81)** \\ \hline Age (years) 1 ** & 68.4 (64.0–73.0) 46–87 & 73.3 (69.0–79.0) 49–88 & 67.3 (58.0–73.0) 47–87 \\ Men & 46 (83.6%) & 81 (86.2%) & 67 (82.7%) \\ Women & 9 (16.4%) & 13 (13.8%) & 14 (173.3%) \\ Non muscle invasive & 55 (100\%) & 69 (73.4%) 5 & \\ cancer (NMIC) & (46 pTa; 9 pT1) 3 [FOOTNOTE:3]Footnote 3: The betaFigure 1: (A) Volcano plot comparing metabolite signals in healthy subjects (CTRL) and bladder cancer patients (BC): A. phosphate, B. glycine, C. diethylene glycol, D. urea, E. oxalic acid, F. pyroglutamic acid, G. propanoic acid, H. glyceraldehyde-3-phosphate, I. dihydroxyacetone phosphate, J. ornithinene, K. proline, L. glutamine acid M. phenol, N. valine, O. phenylalanine, P.1-methylhypoxanthine, Q. arachidic acid, R. glutamine, S. creatinine; (B) Volcano plot comparing metabolite signals in high-grade (BCH) and low-grade (BCL) BC patients: A. amino malonic acid, B. valine, C. proline, D. pentanoic acid; (C) Heatmap of the metabolites selected by the genetic algorithm (Glucose 1 and Glucose 2 represent two different derivatization products obtained by the visualization process). Analyzing only BC patients divided into high- and low-grade indicates that only 4 metabolites showed a statistically significant fold change \(\geq\pm 2\). These were: amino malonic acid, valine, proline, and pentanoic acid (Figure 1B). Moreover, Figure 1C reports the GA selected metabolites, showing a reduced abundance of glucose, citric, stearic, glutamic and phenylpyruvate acid in BC serum as well as an increased amount of propanoic, oxalic, and lactic acid, glyceraldehyde-3-phosphate, dihydroxyacetone phosphate, ornithine, and glycine (Figure 1C). The PLS-DA scatter plots showing the graphical representation of the classifications are reported in Figure 2. PLS-DA classification models attempt to discriminate patients based on the presence or absence of BC (Figure 2A1), cancer grading (high [BCH] or low [BCL]) (Figure 2B1) and muscle invasiveness (non-muscle invasive cancer [NMIC] or muscle-invasive cancer [MIC]) (Figure 2C1). Figure 2 (panels A2, B2, C2, and A3, B3) shows histograms related to the permutation test assessing the models' significance and the metabolites showing a VIP-score higher than 2.0. According to the permutation test, only CTRL vs. BC and BCH vs. BCL PLS-DA models had a \(p\)-value < 0.05, while NMIC vs. MIC model resulted without statistical significance. Fitting the R\({}^{2}\) value and its cross-validated homologue Q\({}^{2}\) resulted, respectively, in 0.901 and 0.842 for CTRL vs. BC model, 0.534 and 0.357 for BCH vs. BCL model, 0.201 and \(-\)0.07 for NMIC vs. MIC model.