Objective:To evaluate preoperative serum ferritin (SF) in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC).Methods:A retrospective analysis was conducted on 280 ESCC patients undergoing radical resection of esophageal cancer at Zhejiang Cancer Hospital from Jan 2008 to Dec 2013. Univariate and multivariate analysis were used to investigate the relationship between preoperative SF level and patients′ clinicopathologic characteristics. Kaplain-Meier method was used to analyze the relationship between preoperative SF level and the prognosis.Results:There were 183 cases (65.4%) with low SF level and 97cases (34.6%) with high SF level. The 1-, 3-and 5-year survival rates in low SF patients were 78.7%, 50.3%, 43.2% and that in high SF were 69.1%, 35.1%, 32.0%, respectively (χ 2=4.697, P=0.031). Univariate analysis demonstrated that intravascular cancer embolus, nerve infiltration and the level of preoperative SF were related to ESCC patients prognosis (all P<0.05). The multivariate analysis showed that carcinoma cell embolus ( OR=1.662, 95% CI: 1.239-2.229, P=0.001), nerve infiltration ( OR=1.823, 95% CI: 1.361-2.443, P<0.001) and the level of preoperative SF ( OR=1.504, 95% CI: 1.113-2.032, P=0.008) were independent risk factors for ESCC patients prognosis. Conclusion:Preoperative SF level closely associates with the prognosis of ESCC patients.

The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma(ESCC) that combines plasma metabolomics with machine learning algorithms.Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls.The dataset was split into a training set and a test set.After identification of differential me-tabolites in training set,single-metabolite-based receiver operating characteristic (ROC) curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls.Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites.Finally,twelve differential plasma metabolites (six up-regulated and six down-regulated) were annotated.The pre-dictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows:arachidonic acid (accuracy:0.887),sebacic acid (accuracy:0.867),indoxyl sulfate (accuracy:0.850),phosphatidylcholine (PC) (14:0/0:0) (accuracy:0.825),deoxycholic acid(accuracy:0.773),and trimethylamine N-oxide (accuracy:0.653).The prediction accuracies of the ma-chine learning models in the test set were partial least-square (accuracy:0.947),random forest (accu-racy:0.947),gradient boosting machine (accuracy:0.960),and support vector machine (accuracy:0.980).Additionally,survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor(hazard ratio (HR):1.752),while PC (14:0/0:0) (HR:0.577) was a favorable prognostic factor for ESCC.This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC.