Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry

Abstract

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

Figure 1

Metabolomic analysis of BC and NC urine samples in the training set. (a) PCA and (b) OPLS-DA score plots of tumor (violet) and control (orange) urine samples. (c) The receiver operator characteristic (ROC) curves. (D–G) The box-and-whisker plots of selected metabolites were observed in the control and BC urine samples.

Figure 2

Metabolomic analysis of HG/LG BC and NCs of urine samples in the training set. (a) PCA and (b) OPLS-DA score plots of HG BC (violet) and control (orange) urine samples. (c) PCA and (d) OPLS-DA score plots of LG BC (green) and control (orange) urine samples. (e, h) The box-and-whisker plots of selected metabolites were observed in the control, HG, and LG BC urine samples.

Figure 3

Metabolomic analysis of pTa/pT1/pT2 BC and NCs of urine samples in the training set. (a) PCA and (b) OPLS-DA score plots of pTa BC (blue) and control (orange) urine samples. (c) PCA and (d) OPLS-DA score plots of pT1 BC (violet) and control (orange) urine samples. (e) PCA and (f) OPLS-DA score plots of pT2 BC (green) and control (orange) urine samples. (g–k) The box-and-whisker plots of selected metabolites were observed in control, pTa, pT1, and pT2 BC urine samples.

Figure 4

Metabolomic analysis of female/male BC and NCs of urine samples. (a) PCA and (b) OPLS-DA score plots of female BC (violet) and control female (orange) urine samples in the training set. (c) PCA and (d) OPLS-DA score plots of male BC (blue) and control male (green) urine samples. (e–h). The box-and-whisker plots of selected metabolites were observed in control, male, and female BC urine samples.

Figure 5

Metabolomic analysis of female/male BC and NCs of urine samples. (a) PCA and (b) OPLS-DA score plots of BC patients aged 40 to 60 (violet) and the control group aged 40 to 60 (orange) of urine samples. (c) PCA and (d) OPLS-DA score plots of BC patients aged 61 to 70 (green) and the control group aged 61 to 70 (orange) of urine samples. (e) PCA and (b) OPLS-DA score plots of BC patients aged 71 to 90 (blue) and the control group aged 71 to 90 (orange) of urine samples. (g–j) The box-and-whisker plots of selected metabolites were observed in control BC urine samples from people of different ages.

Figure 6

Analysis of the topology of selected statistically significant metabolites in BC. (a) Pathway analysis based on KEGG, with bubble area corresponding to the impact of each pathway and color representing significance from red to white, from greatest to least. (1) tryptophan metabolism; (2) pantothenate and CoA biosynthesis; (3) tyrosine metabolism (4) vitamin B6 metabolism (5) citrate cycle (TCA cycle); (6) beta-alanine metabolism; (b) Quantitative enrichment analysis based on SMPDB.