Prognostic Significance of preoperative serum CA125, CA19-9, CA72-4, CEA, and AFP in Patients with Endometrial cancer

Abstract

ObjectiveTo determine preoperative serum CA125, CA19-9, CA72-4, CEA, and AFP with prognostic value, and to establish a risk score based on CA125, CEA, AFP levels for predicting the overall survival (OS) and progression-free survival (PFS) of endometrial cancer (EC) patients. MethodsA retrospective cohort study with 2081 EC patients was conducted at Shengjing Hospital of China Medical University. Patient baseline information, tumor characteristics, and data on five serum biomarkers (CA125, CA19-9, CA72-4, CEA, and AFP) were collected. Hazard ratios (HRs) and 95% confidence intervals (CIs) were determined using univariate or multivariate Cox proportional hazard models. log-rank test and Kaplan-Meier analysis were used to compared survival, Data were randomly divided into a training cohort (50%, N = 1041) and an external validation cohort (50%, n = 1040). the least absolute shrinkage and selection operator (Lasso)-Cox regression model was used to screen the independent factors for establishing risk score. And develop nomograms for survival rate prediction. ResultsMultivariate analysis showed Elevated CA125 (P<0.0001) AFP (P <0.0001) and CEA(P=0.037) were identified as independent biomarkers for PFS. Increased CA125 (P = 0.003) AFP (P <0.0001) and CEA(P=0.014) were independent factors associated with OS. CA125, AFP and CEA were thus incorporated in an innovative Risk score (RS) by Lasso-Cox regression model, The RS was also an independent indicator for PFS (P<0.0001) and OS (P<0.0001). Furthermore, we developed and validated nomogram based on Cox regression models. The discriminative ability and calibration of the nomograms revealed good predictive ability, as indicated by the calibration plots. ConclusionThis study suggests that the risk score based on preoperative serum levels of CA125, CEA, and AFP was prognostic biomarkers for predicting progression-free survival and overall survival for EC patients. Nomograms based on the RS and clinicopathological features accurately predict Prognosis of EC patients.