Zhen Li1, Qianlan Yao1, Songjian Zhao1, Zhen Wang2, Yixue Li3. 1. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, China. 2. Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. Electronic address: zwang01@sibs.ac.cn. 3. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, China; Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. Electronic address: yxli@sibs.ac.cn.
Abstract
BACKGROUND: Esophageal adenocarcinoma (EAC) is one of the most aggressive gastroesophageal cancers. PTGS2, EGFR, ERBB2 and TP53 are the traditional EAC prognostic biomarkers, but they are still limited in their ability to effectively predict the overall survival. OBJECTIVES: To identify an improved biomarker for predicting the prognosis of EAC by using the expression profile. MATERIALS AND METHODS: Differential co-expression analysis and differential expression analysis were performed to identify the related genes of EAC. The 5-fold cross-validation was used to select a prognostic biomarker from the 532 EAC related genes. RESULTS: CRNKL1 was identified as a prognostic biomarker to predict the survival of EAC patients. It could significantly stratify EAC patients into high-risk and low-risk groups and was much better than the traditional biomarkers. Furthermore, ROC curve also verified that CRNKL1 with the highest area under the curve (AUC), reaching a sensitivity of 83.33% and a specificity of 78.57%. CONCLUSIONS: Our research proposed that CRNKL1 might be a novel prognostic biomarker with better predictive ability by comparing with the traditional biomarkers, which provided a preferable opportunity in the clinical applications of EAC.
BACKGROUND:Esophageal adenocarcinoma (EAC) is one of the most aggressive gastroesophageal cancers. PTGS2, EGFR, ERBB2 and TP53 are the traditional EAC prognostic biomarkers, but they are still limited in their ability to effectively predict the overall survival. OBJECTIVES: To identify an improved biomarker for predicting the prognosis of EAC by using the expression profile. MATERIALS AND METHODS: Differential co-expression analysis and differential expression analysis were performed to identify the related genes of EAC. The 5-fold cross-validation was used to select a prognostic biomarker from the 532 EAC related genes. RESULTS:CRNKL1 was identified as a prognostic biomarker to predict the survival of EAC patients. It could significantly stratify EAC patients into high-risk and low-risk groups and was much better than the traditional biomarkers. Furthermore, ROC curve also verified that CRNKL1 with the highest area under the curve (AUC), reaching a sensitivity of 83.33% and a specificity of 78.57%. CONCLUSIONS: Our research proposed that CRNKL1 might be a novel prognostic biomarker with better predictive ability by comparing with the traditional biomarkers, which provided a preferable opportunity in the clinical applications of EAC.