Prognostic value and predictive model of tumor markers in stage I to III gastric cancer patients.

Abstract

Preoperative serum tumor markers have been widely used in the diagnosis and treatment of gastric cancer patients. However, few studies have evaluated the prognosis of gastric cancer patients by establishing statistical models with multiple serum tumor indicators. To explore the prognostic value and predictive model of tumor markers in stage I and III gastric cancer patients. From October 2018 to April 2020, a total of 1236 patients with stage I to III gastric cancer after surgery were included in our study. The relationship between serum tumor markers and clinical and pathological data were analyzed. We established a statistical model to predict the prognosis of gastric cancer based on the results of COX regression analysis. Overall survival (OS) was also compared across different stages of gastric cancer. The deadline for follow-up was May 31, 2023. A total of 1236 patients were included in our study. Univariate analysis found that age, clinical stage, T and N stage, tumor location, differentiation, Borrmann type, size, and four serum tumor markers were prognostic factors of OS (P < 0.05). It was shown that clinical stage, tumor size, alpha foetoprotein, carcinoembryonic antigen, CA125 and CA19-9 (P < 0.05) were independent prognostic factors for OS. According to the scoring results obtained from the statistical model, we found that patients with high scores had poorer survival time (P < 0.05). Furthermore, in stage I patients, the 3-year OS for scores 0-3 ranged from 96.85%, 95%, 85%, and 80%. In stage II patients, the 3-year OS for scores 0-4 were 88.6%, 76.5%, 90.5%, 65.5% and 60%. For stage III patients, 3-year OS for scores 0-6 were 70.9%, 68.3%, 64.1%, 50.9%, 38.4%, 18.5% and 5.2%. We also analyzed the mean survival of patients with different scores. For stage I patients, the mean OS was 55.980 months. In stage II, the mean OS was 51.550 months. The mean OS for stage III was 39.422 months. Our statistical model can effectively predict the prognosis of gastric cancer patients.