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A P53-related microRNA model for predicting the prognosis of hepatocellular carcinoma patients.
Fang, Shuang-Sang; Guo, Jin-Cheng; Zhang, Jian-Hua; Liu, Jin-Na; Hong, Shuai; Yu, Bo; Gao, Yuhan; Hu, Su-Pei; Liu, Hai-Zhong; Sun, Liang; Zhao, Yi.
Afiliación
  • Fang SS; Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China.
  • Guo JC; School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • Zhang JH; Key Laboratory of Intelligent Information Processing, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Liu JN; Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China.
  • Hong S; School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • Yu B; Key Laboratory of Intelligent Information Processing, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Gao Y; Department of Blood Transfusion, Peking University People's Hospital, Beijing, China.
  • Hu SP; Key Laboratory of Intelligent Information Processing, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Liu HZ; Key Laboratory of Intelligent Information Processing, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Sun L; Key Laboratory of Intelligent Information Processing, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Zhao Y; Department of Blood Transfusion, Peking University People's Hospital, Beijing, China.
J Cell Physiol ; 235(4): 3569-3578, 2020 04.
Article en En | MEDLINE | ID: mdl-31556110
ABSTRACT
Studies have shown that microRNAs (miRNAs) play a vital role in tumor progression and patients' prognosis. Therefore, we aimed to construct a miRNA model for forecasting the survival of hepatocellular carcinoma (HCC) patients. The gene expression data of 433 patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus public databases were remined by survival analysis and receptor manipulation characteristic curve (ROC). A prognostic model including six miRNAs (hsa-mir-26a-1-3p, hsa-mir-188-5p, hsa-mir-212-5p, hsa-mir-149-5p, hsa-mir-105-5p, and hsa-mir-132-5p) were constructed in the training dataset (TCGA, n = 333). HCC patients were stratified into a high-risk group and a low-risk group with significantly different survival (median 2.75 vs. 8.93 years, log-rank test p < .001). Then we proved its performance of stratification in another independent dataset (GSE116182, median 2.55 vs 6.96 years, log-rank test p = .008). Cox regression analysis showed that the prognostic model was an independent prognostic indicator for HCC patients. Then time-dependent ROC analyses were performed to test the prognostic ability of the model with that of TNM staging, we found the model had a better performance, especially at 5 years (AUC = 0.76). Functional prediction showed that the genes targeted by the six prognostic miRNAs in the prognostic model were highly expressed in the P53-related pathway. In conclusion, we constructed a prognostic miRNA model that could indicate the survival of HCC patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteína p53 Supresora de Tumor / Carcinoma Hepatocelular / MicroARNs / Neoplasias Hepáticas Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Cell Physiol Año: 2020 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteína p53 Supresora de Tumor / Carcinoma Hepatocelular / MicroARNs / Neoplasias Hepáticas Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Cell Physiol Año: 2020 Tipo del documento: Article País de afiliación: China