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A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis
A large panel of molecular biomarkers have been identified to predict the prognosis of hepatocellular carcinoma (HCC), yet with limited clinical application due to difficult extrapolation. We here generated a genetic risk score system comprised of 12 HCC-specific genes to better predict the prognosi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188480/ https://www.ncbi.nlm.nih.gov/pubmed/30243023 http://dx.doi.org/10.18632/aging.101563 |
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author | Lin, Ting Gu, Jingxian Qu, Kai Zhang, Xing Ma, Xiaohua Miao, Runchen Xiang, Xiaohong Fu, Yunong Niu, Wenquan She, Junjun Liu, Chang |
author_facet | Lin, Ting Gu, Jingxian Qu, Kai Zhang, Xing Ma, Xiaohua Miao, Runchen Xiang, Xiaohong Fu, Yunong Niu, Wenquan She, Junjun Liu, Chang |
author_sort | Lin, Ting |
collection | PubMed |
description | A large panel of molecular biomarkers have been identified to predict the prognosis of hepatocellular carcinoma (HCC), yet with limited clinical application due to difficult extrapolation. We here generated a genetic risk score system comprised of 12 HCC-specific genes to better predict the prognosis of HCC patients. Four genomics profiling datasets (GSE5851, GSE28691, GSE15765 and GSE14323) were searched to seek HCC-specific genes by comparisons between cancer samples and normal liver tissues and between different subtypes of hepatic neoplasms. Univariate survival analysis screened HCC-specific genes associated with overall survival (OS) in the training dataset for next-step risk model construction. The prognostic value of the constructed HCC risk score system was then validated in the TCGA dataset. Stratified analysis indicated this scoring system showed better performance in elderly male patients with HBV infection and preoperative lower levels of creatinine, alpha-fetoprotein and platelet and higher level of albumin. Functional annotation of this risk model in high-risk patients revealed that pathways associated with cell cycle, cell migration and inflammation were significantly enriched. In summary, our constructed HCC-specific gene risk model demonstrated robustness and potentiality in predicting the prognosis of HCC patients, especially among elderly male patients with HBV infection and relatively better general conditions. |
format | Online Article Text |
id | pubmed-6188480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Impact Journals |
record_format | MEDLINE/PubMed |
spelling | pubmed-61884802018-11-09 A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis Lin, Ting Gu, Jingxian Qu, Kai Zhang, Xing Ma, Xiaohua Miao, Runchen Xiang, Xiaohong Fu, Yunong Niu, Wenquan She, Junjun Liu, Chang Aging (Albany NY) Research Paper A large panel of molecular biomarkers have been identified to predict the prognosis of hepatocellular carcinoma (HCC), yet with limited clinical application due to difficult extrapolation. We here generated a genetic risk score system comprised of 12 HCC-specific genes to better predict the prognosis of HCC patients. Four genomics profiling datasets (GSE5851, GSE28691, GSE15765 and GSE14323) were searched to seek HCC-specific genes by comparisons between cancer samples and normal liver tissues and between different subtypes of hepatic neoplasms. Univariate survival analysis screened HCC-specific genes associated with overall survival (OS) in the training dataset for next-step risk model construction. The prognostic value of the constructed HCC risk score system was then validated in the TCGA dataset. Stratified analysis indicated this scoring system showed better performance in elderly male patients with HBV infection and preoperative lower levels of creatinine, alpha-fetoprotein and platelet and higher level of albumin. Functional annotation of this risk model in high-risk patients revealed that pathways associated with cell cycle, cell migration and inflammation were significantly enriched. In summary, our constructed HCC-specific gene risk model demonstrated robustness and potentiality in predicting the prognosis of HCC patients, especially among elderly male patients with HBV infection and relatively better general conditions. Impact Journals 2018-09-21 /pmc/articles/PMC6188480/ /pubmed/30243023 http://dx.doi.org/10.18632/aging.101563 Text en Copyright © 2018 Lin et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Paper Lin, Ting Gu, Jingxian Qu, Kai Zhang, Xing Ma, Xiaohua Miao, Runchen Xiang, Xiaohong Fu, Yunong Niu, Wenquan She, Junjun Liu, Chang A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis |
title | A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis |
title_full | A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis |
title_fullStr | A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis |
title_full_unstemmed | A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis |
title_short | A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis |
title_sort | new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188480/ https://www.ncbi.nlm.nih.gov/pubmed/30243023 http://dx.doi.org/10.18632/aging.101563 |
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