A novel molecular and clinical staging model to predict survival for patients with esophageal squamous cell carcinoma

// Wei Wang 1, * , Zhiwei Wang 2, 3, * , Jun Zhao 4 , Min Wei 2 , Xinghua Zhu 5 , Qi He 2 , Tianlong Ling 3 , Xiaoyan Chen 6 , Ziang Cao 3 , Yixin Zhang 1 , Lei Liu 1 , Minxin Shi 1 1 Department of Surgery, The Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China 2 Department of Breast, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University, Shanghai, China 3 Department of Thoracic Surgery, Shanghai Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China 4 Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, Jiangsu Province, China 5 Department of Pathology, The Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China 6 Department of Pathology, Shanghai Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China * These authors have contributed equally to this work Correspondence to: Lei Liu, email: liulei5602@126.com Minxin Shi, email: Docshimx@126.com Keywords: esophageal squamous cell carcinoma, UBE2C, MGP, survival Received: December 08, 2015 Accepted: July 26, 2016 Published: August 18, 2016 ABSTRACT Current prognostic factors fail to accurately determine prognosis for patients with esophageal squamous cell carcinoma (ESCC) after surgery. Here, we constructed a survival prediction model for prognostication in patients with ESCC. Candidate molecular biomarkers were extracted from the Gene Expression Omnibus (GEO), and Cox regression analysis was performed to determine significant prognostic factors. The survival prediction model was constructed based on cluster and discriminant analyses in a training cohort (N=205), and validated in a test cohort (N=207). The survival prediction model consisting of two genes (UBE2C and MGP) and two clinicopathological factors (tumor stage and grade) was developed. This model could be used to accurately categorize patients into three groups in the test cohort. Both disease-free survival and overall survival differed among the diverse groups (P<0.05). In summary, we have developed and validated a predictive model that is based on two gene markers in conjunction with two clinicopathological variables, and which can accurately predict outcomes for ESCC patients after surgery.