Diagnosis of bladder cancer and prediction of survival by urinary metabolomics

Abstract

Bladder cancer (BC) is a common cancer but diagnostic modalities, such as cystoscopy and urinary cytology, have limitations. Here, high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-QTOFMS) was used to profile urine metabolites of 138 patients with BC and 121 control subjects (69 healthy people and 52 patients with hematuria due to non-malignant diseases). Multivariate statistical analysis revealed that the cancer group could be clearly distinguished from the control groups on the basis of their metabolomic profiles, even when the hematuric control group was included. Patients with muscle-invasive BC could also be distinguished from patients with non-muscle-invasive BC on the basis of their metabolomic profiles. Successive analyses identified 12 differential metabolites that contributed to the distinction between the BC and control groups, and many of them turned out to be involved in glycolysis and betaoxidation. The association of these metabolites with cancer was corroborated by microarray results showing that carnitine transferase and pyruvate dehydrogenase complex expressions are significantly altered in cancer groups. In terms of clinical applicability, the differentiation model diagnosed BC with a sensitivity and specificity of 91.3% and 92.5%, respectively, and comparable results were obtained by receiver operating characteristic analysis (AUC = 0.937). Multivariate regression also suggested that the metabolomic profile correlates with cancer-specific survival time. The excellent performance and simplicity of this metabolomics-based approach suggests that it has the potential to augment or even replace the current modalities for BC diagnosis.

Figure 1. OPLS-DA score plots (A & C) and 3D scatter plots (B & D)

Each symbol represents the metabolomic profile of an individual sample. (A & B) Comparison of normal controls (black) and patients with bladder cancer (red). The open black boxes represent the control patients with benign hematuria. (C & D) Comparison of the two types of bladder cancer. NMIBCs are represented by black boxes while MIBCs are represented by red dots.

Figure 2. Box plots of the levels of potential metabolomic markers that could be used to distinguish BC patients from control subjects

The p-values of Student's t-test are indicated.

Figure 3. Cross-validation with an OPLS-DA model and multivariate ROC analysis

(A) Schematic depiction of the overall procedure of cross-validation analysis. The test set was created by randomly selecting one third of the entire sample. A prediction model was built with the rest of the samples (training set), after which the models were used to predict the cancer status of the test set. Diagnostic performance was assessed by either OPLS-DA or PLS-DA based ROC curve analysis. (B) Prediction of the cancer status using the OPLS-DA model. The boxes represent BC patients while the dots represent control subjects. The green samples represent the test set. The samples represented by open green symbols are mispredicted samples. The dichotomic decision of prediction was made by using the a priori value of 0.5 for the Y variable from the OPLS-DA model. Of the 46 cancer samples, 42 were predicted to be from cancer patients (91.3% sensitivity) while 37 of the 40 control samples were predicted to be from control subjects (92.5% specificity). (C) PLS-DA-based ROC curve analysis using the same test set revealed a sensitivity of 85% and specificity of 85%. The area under the curve (AUC) value was 0.937.

Figure 4. Prediction of cancer-specific survival time

The PLS prediction model was obtained with three components (R² = 0.991, Q² = 0.404). The X-axis corresponds to the predicted values that were calculated by using the PLS regression followed by the leave-one-out prediction. The Y-axis corresponds to the actual cancer-specific survival time. The R-squared value of the linear regression is 0.405 and the p-value is 0.0046.

Figure 5. Pathways that may be altered in patients with BC compared to control subjects

The metabolites detected in this study are indicated in red. The genes whose levels are modulated in our microarray analysis are indicated in blue ellipses.