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论文题目: Protein coding gene CRNKL1 as a potential prognostic biomarker in esophageal adenocarcinoma
英文论文题目: Protein coding gene CRNKL1 as a potential prognostic biomarker in esophageal adenocarcinoma
第一作者: Li, Z; Yao, QL; Zhao, SJ; Wang, Z; Li, YX
英文第一作者: Li, Z; Yao, QL; Zhao, SJ; Wang, Z; Li, YX
联系作者: Wang, Z; Li, YX (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, Key Lab Computat Biol, Shanghai, Peoples R China.
英文联系作者: Wang, Z; Li, YX (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, Key Lab Computat Biol, Shanghai, Peoples R China.
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发表年度: 2017
卷: 76
期:
页码: 1-6
摘要: Background: Esophageal adenocarcinoma (EAC) is one of the most aggressive gastroesophageal cancers. PTGS2, EGFR, ERBB2 and TPS3 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.
英文摘要: Background: Esophageal adenocarcinoma (EAC) is one of the most aggressive gastroesophageal cancers. PTGS2, EGFR, ERBB2 and TPS3 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.
刊物名称: ARTIFICIAL INTELLIGENCE IN MEDICINE
英文刊物名称: ARTIFICIAL INTELLIGENCE IN MEDICINE
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学科: Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics
英文学科: Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics
影响因子: 2.009
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论文类别: Article
英文论文类别: Article
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