Downregulation of CRTAC1 in Urothelial Carcinoma Promotes Tumor Aggressiveness and Confers Poor Prognosis

Background: Cartilage acidic protein 1 (CRTAC1) is a glycosylated calcium-binding extracellular matrix protein. The oncological functions of CRTAC1 in urothelial carcinoma (UC) of the urinary bladder (UB) and upper urinary tract (UT) have not yet been elucidated. Based on the published UBUC transcriptome data, we re-evaluated the differential expression profile of calcium ion binding–related genes (GO:0005509), and we found that CRTAC1 was the most significantly downregulated gene in UBUC progression. Therefore, we analyzed the prognostic value and biological significance of CRTAC1 expression in UC. Methods: We used immunohistochemistry to determine the CRTAC1 expression levels in 340 patients with UTUC and 295 patients with UBUC. The CRTAC1 expression was compared with the clinicopathological characteristics, and the prognostic impact of CRTAC1 on metastasis-free survival (MFS) and disease-specific survival (DSS) was evaluated. To study the biological functions of CRTAC1, the proliferation, migration, invasion, and tube formation abilities of UC-derived cells were evaluated. Results: A low CRTAC1 expression significantly correlated with high tumor stage, high histological grade, perineural invasion, vascular invasion, nodal metastasis, and high mitotic rate (all p < 0.01). Moreover, the CRTAC1 immunoexpression status was an independent prognostic factor for MFS and DSS in UBUC and UTUC patients (all p < 0.001) in the multivariate analysis. The exogenous expression of CRTAC1 suppressed the cell proliferation, invasion, and angiogenesis, and downregulated the matrix metallopeptidase 2 (MMP2) level in BFTC909 and T24 cells. Conclusions: CRTAC1 may participate in progression of UC and serve as a prognostic marker for metastasis. Low CRTAC1 expression was significantly associated with aggressive UC characteristics and worse clinical outcomes. The inclusion of CRTAC1 immunoexpression in the standard pathological variables may optimize the risk stratification of patients.