Table 1.
Studies regarding blood-based CTCs as potential biomarkers for bladder cancer
| Authors (year) | Sample type | No. of patients | Laboratory technique | Clinical application | Detection rate | Refs. |
|---|---|---|---|---|---|---|
| Qi et al. (2014) | Serum | 120 | ELISA, RT-qPCR | Diagnostic biomarker | AUC = 81.9%, sensitivity = 82.1%, specificity = 61.9% | [52] |
| Kim et al. (2020) | Peripheral blood | 20 | Whole exome sequencing | Discrimination of MIBC from NMIBC and healthy individuals | KMT2C mutations were detected in 20% of CTCs | [53] |
| Gazzaniga et al. (2014) | Peripheral blood | 102 | CellSearch System | Predict prognosis | DFS (p = 0.005), PFS (p = 0.004) | [56] |
| Soave et al. (2017) | Peripheral blood | 188 | CellSearch System | Predict prognosis | RFS (p < 0.001), CSS (p < 0.001) | [57] |
| Rink et al. (2012) | Peripheral blood | 100 | CellSearch System | Predict prognosis | OS (p = 0.003), CSS (p = 0.002), RFS (p < 0.001) | [58] |
| Gazzaniga et al. (2012) | Peripheral blood | 44 | CellSearch System | Predict prognosis | TFR (p < 0.001) | [59] |
| Rink et al. (2011) | Peripheral blood | 50 | CellSearch System | Predict prognosis | OS (p = 0.001), CSS (p < 0.001), PFS (p < 0.001) | [60] |
| Soave et al. (2017) | Peripheral blood | 226 | CellSearch System | Predict prognosis | OS (p < 0.001), CSS (p < 0.001), RFS (p < 0.001) | [61] |
| Beije et al. (2022) | Peripheral blood | 273 | CellSearch System | Predict treatment response | CTC-positive patients treated with NAC have longer survival times | [63] |
| Osman et al. (2004) | Peripheral blood | 62 | Nested RT-PCR | Monitoring recurrence | Positive predict value = 79%, negative predict value = 50% | [64] |
| Abrahamsson et al. (2017) | Peripheral blood | 88 | CellSearch System | Predict disease progression | Disease progression (p = 0.049) | [65] |
| Haga et al. (2020) | Peripheral blood | 26 | FISHMAN-R system | Predict disease progression | More CTC detected in progressive BC (p = 0.01) | [66] |
| Gradilone et al. (2010) | Peripheral blood | 54 | RT-PCR, CELLection Dynabeads | Monitoring recurrence | DFS (p < 0.001) | [67] |
| Nicolazzo et al. (2017) | Peripheral blood | 54 | RT-PCR, CELLection Dynabeads | Predict prognosis | DFS (p < 0.0001), CSS (p < 0.0001) | [68] |
| Busetto et al. (2017) | Peripheral blood | 155 | CellSearch System | Predict prognosis | TFR (p < 0.0001), TTP (p < 0.0001) | [69] |
| Nicolazzo et al. (2019) | Peripheral blood | 102 | CellSearch System | Predict prognosis | TFR (p < 0.001), TSR (p < 0.001), TTP (p < 0.001) | [70] |
| Fu et al. (2021) | Peripheral blood | 48 | Microfluidic-Assay System | Early risk stratification | More CTC detected in progressive BC (p = 0.024) | [71] |
| Winters et al. (2015) | Peripheral blood | 31 | CellSearch System | Predict treatment response | Evaluate chemotherapy response: CTC declined after chemotherapy | [72] |
| Nicolazzo et al. (2021) | Peripheral blood | 20 | CellSearch System, ScreenCell | Treatment monitoring | Serial evaluation of CTCs can guide treatment selection for suitable PD-L1 inhibitors after BCG failure | [73] |
| Anantharaman et al. (2016) | Peripheral blood | 25 | Algorithmic analysis | Predict treatment response | High PD-L1+ /CD45− CTC burden and low burden of apoptotic CTCs had worse overall survival | [74] |
CTCs Circulating tumor cells, AUC area under the receiver operating characteristics curve, OS Overall survival, CSS Cancer-specific survival, RFS Recurrence-free survival, DFS Disease free survival, PFS Progression free survival, TFR time to first recurrence, TTP time to progression, TSR time to second recurrence, NAC neoadjuvant chemotherapy, BCG Bacillus Calmette–Guérin, PD-L1 Programmed death-ligand 1