The Prognostic Value of the Circulating Tumor Cell-Based Four mRNA Scoring System: A New Non-Invasive Setting for the Management of Bladder Cancer

Abstract

Bladder cancer (BC) is one of the most expensive lifetime cancers to treat because of the high recurrence rate, repeated surgeries, and long-term cystoscopy monitoring and treatment. The lack of an accurate classification system predicting the risk of recurrence or progression leads to the search for new biomarkers and strategies. Our pilot study aimed to identify a prognostic gene signature in circulating tumor cells (CTCs) isolated by ScreenCell devices from muscle invasive and non-muscle invasive BC patients. Through the PubMed database and Cancer Genome Atlas dataset, a panel of 15 genes modulated in BC with respect to normal tissues was selected. Their expression was evaluated in CTCs and thanks to the univariate and multivariate Cox regression analysis, EGFR, TRPM4, TWIST1, and ZEB1 were recognized as prognostic biomarkers. Thereafter, by using the risk score model, we demonstrated that this 4-gene signature significantly grouped patients into high- and low-risk in terms of recurrence free survival (HR = 2.704, 95% CI = 1.010−7.313, Log-rank p < 0.050). Overall, we identified a new prognostic signature that directly impacted the prediction of recurrence, improving the choice of the best treatment for BC patients.

Keywords: TRPM4; bladder cancer; circulating tumor cells; mRNA scoring system; recurrence.

Figure 1

The flowchart of the data processing, analysis, and validation in the current study.

Figure 2

The selection of potential biomarkers. (A) The comparison of the mRNA expression level of the selected potential biomarker candidates between the normal and tumor tissues from thee TCGA-GTEx database using GEPIA. T = Tumor, N = normal bladder. (B) The protein–protein interaction network derived from STRING connects the selected markers (PPI enrichment, p value < 1.0 × 10⁻¹⁶).

Figure 2

The selection of potential biomarkers. (A) The comparison of the mRNA expression level of the selected potential biomarker candidates between the normal and tumor tissues from thee TCGA-GTEx database using GEPIA. T = Tumor, N = normal bladder. (B) The protein–protein interaction network derived from STRING connects the selected markers (PPI enrichment, p value < 1.0 × 10⁻¹⁶).

Figure 3

The characterization and functionality of the CTCs from BC patients. (A) The representative confocal microscopy image of the isolated CTCs stained with anti-human pan-CK and anti-human CD45 Abs. DAPI was used to counteract the nuclei. Magnification 60×. (B) The representative light microscopy image of the CTCs, stained with toluidine blue, migrated on the membrane to the bottom face. The arrow indicates the pores of the transwell. (C) The representative confocal image of the CTCs and T24 cells that invaded the Matrigel in the transwell invasion assay. Cells were stained with pan-CK and DAPI, Magnification 60×. (D) The percentage of patients who displayed CTCs with an invasive phenotype, as demonstrated by the transwell assay.

Figure 4

The expression of biomarker candidates in the CTCs isolated from BC patients. The expression was evaluated by ddPCR compared with the NHB and BC biopsies. Genes found to be expressed at significant higher/lower levels in CTCs with respect to the BC biopsies or NHB, are shown. The gene expression levels, evaluated by ddPCR, are expressed as fold changes with respect to NHB, used as calibrators (=1). NS = not statistically significant, p ≤ 0.05 was considered as statistically significant.

Figure 5

The gene expression analysis of the selected biomarkers. (A) The analysis was performed in the CTCs by stratifying patients according to high grade (HG) and low grade (LG). The Mann–Whitney t-test. p ≤ 0.05 was considered as statistically significant. (B,C) The Spearman correlation matrix of the selected biomarker candidates. NS = not statistically significant, p ≤ 0.05 was considered as statistically significant.

Figure 6

The development of the four-mRNA-based prognostic model. (A) The Kaplan–Meier analysis for RFS by stratifying patients into high- and low-expressing for the EGFR, TRPM4, TWIST1, and ZEB1 levels. (B) The Kaplan–Meier and (C) ROC curve analysis for RFS by stratifying patients into the high- and low-risk score according to the four-mRNA-based signature. (D) The representative images of CTCs stained with anti-human EGFR, TRPM4, TWIST1, and ZEB1 Abs followed by Alexa 594-conjugated secondary Ab. DAPI was used to counteract the nuclei. Magnification 60×.