Table 5.
Studies regarding circulating metabolomics and proteomics in blood as potential biomarkers for bladder cancer
| Authors (year) | Sample type | No. of patients | Laboratory technique | Clinical application | Detection rate | Refs. |
|---|---|---|---|---|---|---|
| Cao et al. (2012) | Serum | 112 | 1H NMR measurements | Discrimination of patients with BC from healthy individuals | Detected abnormal serum metabolic profiles in 100% of patients | [196] |
| Bansal et al. (2013) | Serum | 99 | 1H NMR measurements | Diagnostic biomarker | Sensitivity = 94%, specificity = 97% | [197] |
| Tan et al. (2017) | Serum | 172 | UHPLC coupled with Q-TOF MS | Diagnostic biomarker | AUC = 0.961 | [198] |
| Schwamborn et al. (2009) | Serum | 248 | MALDI-TOF–MS | Diagnostic biomarker | Sensitivity = 96.4%, specificity = 86.5% | [199] |
| Bansal et al. (2014) | Serum | 90 | MALDI-TOF–MS | Diagnostic biomarker | AUC = 0.946 (S100A8 and S100A9) | [200] |
| Bansal et al. (2016) | Serum | 160 | ELISA, MARS | Diagnostic biomarker | AUC = 0.957 (S100A9) | [201] |
| Amara et al. (2019) | Serum | 87 | LC–MS | Predict disease progression | OS (p = 0.0065) | [207] |
| Minami et al. (2010) | Serum | 2 | Reversed-phase high-performance liquid chromatography | Predict prognosis | RFS (p = 0.026), CSS (p = 0.041) | [208] |
| Lemańska-Perek et al. (2019) | Plasma | 6 | MALDI-TOF–MS | Predict disease progression | Increasing abundance in progressing BC | [209] |
AUC area under the receiver operating characteristics curve, ELISA enzyme linked immunosorbent assay, OS overall survival, RFS recurrence-free survival, CSS cancer-specific survival, NMR nuclear magnetic resonance, LC–MS liquid chromatography–mass spectrometry, Q-TOF MS quadrupole time of flight mass spectrometry, UHPLC ultra-high performance liquid chromatography, MALDI-TOF–MS matrix-assisted laser desorption/ionization time of flight mass spectrometry