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A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma

Growing evidence indicates that long non-coding RNAs (lncRNAs) may be potential biomarkers and therapeutic targets for many disease conditions, including cancer. In this study, we constructed a risk score system of three lncRNAs (LOC101927051, LINC00667 and NSUN5P2) for predicting the prognosis of s...

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Autores principales: Gu, Jingxian, Zhang, Xing, Miao, Runchen, Ma, Xiaohua, Xiang, Xiaohong, Fu, Yunong, Liu, Chang, Niu, Wenquan, Qu, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6075433/
https://www.ncbi.nlm.nih.gov/pubmed/30018179
http://dx.doi.org/10.18632/aging.101497
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author Gu, Jingxian
Zhang, Xing
Miao, Runchen
Ma, Xiaohua
Xiang, Xiaohong
Fu, Yunong
Liu, Chang
Niu, Wenquan
Qu, Kai
author_facet Gu, Jingxian
Zhang, Xing
Miao, Runchen
Ma, Xiaohua
Xiang, Xiaohong
Fu, Yunong
Liu, Chang
Niu, Wenquan
Qu, Kai
author_sort Gu, Jingxian
collection PubMed
description Growing evidence indicates that long non-coding RNAs (lncRNAs) may be potential biomarkers and therapeutic targets for many disease conditions, including cancer. In this study, we constructed a risk score system of three lncRNAs (LOC101927051, LINC00667 and NSUN5P2) for predicting the prognosis of small hepatocellular carcinoma (sHCC) (maximum tumor diameter ≤5 cm). The prognostic value of this sHCC risk model was confirmed in TCGA HCC samples (TNM stage I and II). Stratified survival analysis revealed that the suitable patient groups of the sHCC lncRNA-signature included HBV-infected and cirrhotic patients with better physical conditions yet lower levels of albumin and higher levels of alpha-fetoprotein preoperatively. Besides, Asian patients with no family history of HCC or history of alcohol consumption can be predicted more precisely. Molecular functional analysis indicated that PYK2 pathway was significantly enriched in the high-risk patients. Pathway enrichment analysis indicated that the two lncRNAs (LINC00667 and NSUN5P2) associated with poor prognosis were closely related to cell cycle. The nomogram based on the lncRNA-signature for RFS prediction in sHCC patients exhibited good performance in recurrence risk stratification. In conclusion, we identified a novel three-lncRNA-expression-based risk model for predicting the prognosis of sHCC.
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spelling pubmed-60754332018-08-06 A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma Gu, Jingxian Zhang, Xing Miao, Runchen Ma, Xiaohua Xiang, Xiaohong Fu, Yunong Liu, Chang Niu, Wenquan Qu, Kai Aging (Albany NY) Research Paper Growing evidence indicates that long non-coding RNAs (lncRNAs) may be potential biomarkers and therapeutic targets for many disease conditions, including cancer. In this study, we constructed a risk score system of three lncRNAs (LOC101927051, LINC00667 and NSUN5P2) for predicting the prognosis of small hepatocellular carcinoma (sHCC) (maximum tumor diameter ≤5 cm). The prognostic value of this sHCC risk model was confirmed in TCGA HCC samples (TNM stage I and II). Stratified survival analysis revealed that the suitable patient groups of the sHCC lncRNA-signature included HBV-infected and cirrhotic patients with better physical conditions yet lower levels of albumin and higher levels of alpha-fetoprotein preoperatively. Besides, Asian patients with no family history of HCC or history of alcohol consumption can be predicted more precisely. Molecular functional analysis indicated that PYK2 pathway was significantly enriched in the high-risk patients. Pathway enrichment analysis indicated that the two lncRNAs (LINC00667 and NSUN5P2) associated with poor prognosis were closely related to cell cycle. The nomogram based on the lncRNA-signature for RFS prediction in sHCC patients exhibited good performance in recurrence risk stratification. In conclusion, we identified a novel three-lncRNA-expression-based risk model for predicting the prognosis of sHCC. Impact Journals 2018-07-13 /pmc/articles/PMC6075433/ /pubmed/30018179 http://dx.doi.org/10.18632/aging.101497 Text en Copyright © 2018 Gu 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
Gu, Jingxian
Zhang, Xing
Miao, Runchen
Ma, Xiaohua
Xiang, Xiaohong
Fu, Yunong
Liu, Chang
Niu, Wenquan
Qu, Kai
A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma
title A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma
title_full A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma
title_fullStr A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma
title_full_unstemmed A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma
title_short A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma
title_sort three-long non-coding rna-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6075433/
https://www.ncbi.nlm.nih.gov/pubmed/30018179
http://dx.doi.org/10.18632/aging.101497
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