Abstract

Objective The metabolism and prognostic value of serum concentrations of the aminoterminal (PINP) and carboxyterminal (PICP) propeptides of type I collagen and the PICP:PINP ratio in relation to the carboxyterminal telopeptide of type I collagen (ICTP) in ovarian cancer were evaluated. Methods Fifty patients with epithelial ovarian cancer were evaluated with serial measurements of serum concentrations of PICP, PINP, and ICTP before the operation and at 3-month intervals during the first year after the operation. For statistical analysis the patients were divided into two groups according to clinical outcome (alive vs dead) and clinical behavior (fast progression vs others). Results The serum PINP concentration before the operation was increased and the PICP/PINP ratio was lowered in patients with poor prognosis (PP) compared to those with good prognosis (GP) and in patients with fast progression compared to others. The serum PICP concentration did not differ between the groups. The circulating ICTP concentration was significantly higher in the PP-group than in the GP group. In Kaplan–Meier analysis the PICP:PINP ratio divided the PP and GP patients ( P = 0.0004). In multivariate regression analysis, the independent prognostic variables were clinical stage ( P = 0.014, 95% confidence interval (CI) 1.31–11.19) and preoperative serum ICTP concentration ( P = 0.048, CI 1.01–5.91). When serum ICTP concentration was excluded from the equation, the PICP:PINP ratio ( P = 0.012, CI 1.29–7.83), together with clinical stage ( P = 0.013, CI 1.31–10.37), was found to be an independent prognostic variable. When the early and advanced stage patients were analyzed separately, the PICP:PINP ratio was a significant prognostic variable in multivariate analysis in early stage patients and in advanced stages during the first 4 years of follow-up. Conclusion A low PICP:PINP ratio was associated with fast progression and poor clinical outcome in ovarian cancer. Evaluation of the PICP:PINP ratio together with ICTP may be valuable in predicting the clinical outcome of ovarian cancer.

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