Yu-Shen Wu1, Huapeng Lin2, Duke Chen3, Ziying Yi1, Beilei Zeng1, Yicheng Jiang4, Guosheng Ren5. 1. Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China. 2. Department of Intensive Care Unit, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, PR China. 3. Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China. 4. Department of Oncology, The People's Hospital of Chongqing Hechuan, Chongqing, China. 5. Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China; Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China. Electronic address: rengs726@126.com.
Abstract
BACKGROUND: The microRNAs (miRNAs) have been validated as prognostic markers in many cancers. The aim of this study was to identify new miRNA prognostic biomarkers in endometrial cancer (EC) and to develop an expression-based miRNA signature to provide survival risk prediction for EC patients. METHODS: From TCGA database, the miRNA datasets of EC and clinical information were downloaded in April 2018. Using univariate and multivariate Cox regression analyses identify prognostic factors. Using area under the curve (AUC) of receiver operating characteristic (ROC) curve assess the sensitivity and specificity of prognostic model. RESULTS: 530 patients were randomly divided into training set and testing set. Among 561 differentially expressed miRNAs, 4 miRNAs (miR-4758, miR-876, miR-142, miR-190b) were demonstrated to be predictive biomarkers of overall survival (OS) for EC patients in training set. Based on the risk score of 4-miRNA model, patients in the training set were divided into high-risk and low-risk groups with significantly different OS. This 4-miRNA model was validated in testing and entire set. The AUC for the ROC curves in the entire set was 0.704. Meanwhile, multivariate Cox regression combined with other traditional clinical parameters indicated that the 4-miRNA model can be used as an independent OS prognostic factor. Functional enrichment analysis revealed that these miRNAs are involved in biological processes and pathways that are closely related to cancer. CONCLUSION: A robust 4-miRNA signature as an independent prognostic factor for OS in EC patients was established.
BACKGROUND: The microRNAs (miRNAs) have been validated as prognostic markers in many cancers. The aim of this study was to identify new miRNA prognostic biomarkers in endometrial cancer (EC) and to develop an expression-based miRNA signature to provide survival risk prediction for EC patients. METHODS: From TCGA database, the miRNA datasets of EC and clinical information were downloaded in April 2018. Using univariate and multivariate Cox regression analyses identify prognostic factors. Using area under the curve (AUC) of receiver operating characteristic (ROC) curve assess the sensitivity and specificity of prognostic model. RESULTS: 530 patients were randomly divided into training set and testing set. Among 561 differentially expressed miRNAs, 4 miRNAs (miR-4758, miR-876, miR-142, miR-190b) were demonstrated to be predictive biomarkers of overall survival (OS) for EC patients in training set. Based on the risk score of 4-miRNA model, patients in the training set were divided into high-risk and low-risk groups with significantly different OS. This 4-miRNA model was validated in testing and entire set. The AUC for the ROC curves in the entire set was 0.704. Meanwhile, multivariate Cox regression combined with other traditional clinical parameters indicated that the 4-miRNA model can be used as an independent OS prognostic factor. Functional enrichment analysis revealed that these miRNAs are involved in biological processes and pathways that are closely related to cancer. CONCLUSION: A robust 4-miRNA signature as an independent prognostic factor for OS in EC patients was established.
Authors: Xinlu Zhang; Yaping Wang; Shujun Zhao; Qiaohong Qin; Min Zhang; Yi Jiang; Hai Zhu; Hongyu Li Journal: Am J Transl Res Date: 2021-05-15 Impact factor: 4.060