Fangyao Chen1, Hui Zhou2, Chenqiuzi Wu3, Hong Yan4. 1. Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, 76 Yanta Xilu Road, Xi'an, Shaanxi, 710061, China. 2. Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta Xilu Road, Xi'an, Shaanxi, 710061, China. 3. First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta Xilu Road, Xi'an, Shaanxi, 710061, China. 4. Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, 76 Yanta Xilu Road, Xi'an, Shaanxi, 710061, China. Electronic address: yanhxjtu@hotmail.com.
Abstract
OBJECT: This study focused on the identification of prognostic miRNAs for the prediction of tumor recurrence and progress in esophageal cancer. METHODS: MiRNA profiling and clinical characteristics of esophageal cancer patients was downloaded from the TCGA database. Univariate analysis was performed to select potential prognostic miRNAs and covariates. LASSO based logistic regression was conducted to identify the prognostic miRNAs given covariates. Bioinformatics analysis including gene ontology, disease ontology and pathway enrichment analysis were performed. A nomogram was generated based on multivariate logistic regression to illustrate the association between the identified miRNAs and the risk of tumor recurrence and progress. RESULTS: A total of 1881 miRNAs and 10 clinical characteristics were obtained from TCGA database. 18 miRNAs were finally identified in which 6 miRNAs were identified for the first time to be associated with the tumor recurrence and progress of esophageal cancer given covariates. Bioinformatics analysis suggested that the identified miRNAs were associated with the tumor recurrence and progress of esophageal cancer. The association between identified miRNAs and risk of tumor recurrence and progress were presented in a nomogram. CONCLUSION: The 6 newly identified miRNAs may be potential biomarkers for the prediction of tumor recurrence and progress of esophageal cancer.
OBJECT: This study focused on the identification of prognostic miRNAs for the prediction of tumor recurrence and progress in esophageal cancer. METHODS: MiRNA profiling and clinical characteristics of esophageal cancerpatients was downloaded from the TCGA database. Univariate analysis was performed to select potential prognostic miRNAs and covariates. LASSO based logistic regression was conducted to identify the prognostic miRNAs given covariates. Bioinformatics analysis including gene ontology, disease ontology and pathway enrichment analysis were performed. A nomogram was generated based on multivariate logistic regression to illustrate the association between the identified miRNAs and the risk of tumor recurrence and progress. RESULTS: A total of 1881 miRNAs and 10 clinical characteristics were obtained from TCGA database. 18 miRNAs were finally identified in which 6 miRNAs were identified for the first time to be associated with the tumor recurrence and progress of esophageal cancer given covariates. Bioinformatics analysis suggested that the identified miRNAs were associated with the tumor recurrence and progress of esophageal cancer. The association between identified miRNAs and risk of tumor recurrence and progress were presented in a nomogram. CONCLUSION: The 6 newly identified miRNAs may be potential biomarkers for the prediction of tumor recurrence and progress of esophageal cancer.