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A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has a dismal long-term outcome. We aimed to construct a multi-gene model for prognosis prediction to inform HCC management. The cancer-specific differentially expressed genes (DEGs) were identified using RNA-seq data of paired tumor and normal tissue. A prognostic sign...

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Autores principales: Zhou, Taicheng, Cai, Zhihua, Ma, Ning, Xie, Wenzhuan, Gao, Chan, Huang, Mengli, Bai, Yuezong, Ni, Yangpeng, Tang, Yunqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372135/
https://www.ncbi.nlm.nih.gov/pubmed/32760725
http://dx.doi.org/10.3389/fcell.2020.00629
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author Zhou, Taicheng
Cai, Zhihua
Ma, Ning
Xie, Wenzhuan
Gao, Chan
Huang, Mengli
Bai, Yuezong
Ni, Yangpeng
Tang, Yunqiang
author_facet Zhou, Taicheng
Cai, Zhihua
Ma, Ning
Xie, Wenzhuan
Gao, Chan
Huang, Mengli
Bai, Yuezong
Ni, Yangpeng
Tang, Yunqiang
author_sort Zhou, Taicheng
collection PubMed
description Hepatocellular carcinoma (HCC) has a dismal long-term outcome. We aimed to construct a multi-gene model for prognosis prediction to inform HCC management. The cancer-specific differentially expressed genes (DEGs) were identified using RNA-seq data of paired tumor and normal tissue. A prognostic signature was built by LASSO regression analysis. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. A 10-gene signature was constructed to stratify the TCGA and ICGC cohorts into high- and low-risk groups where prognosis was significantly worse in the high-risk group across cohorts (P < 0.001 for all). The 10-gene signature outperformed all previously reported models for both C-index and the AUCs for 1-, 3-, 5-year survival prediction (C-index, 0.84 vs 0.67 to 0.73; AUCs for 1-, 3- and 5-year OS, 0.84 vs 0.68 to 0.79, 0.81 to 0.68 to 0.80, and 0.85 vs 0.67 to 0.78, respectively). Multivariate Cox regression analysis revealed risk group and tumor stage to be independent predictors of survival in HCC. A nomogram incorporating tumor stage and signature-based risk group showed better performance for 1- and 3-year survival than for 5-year survival. GSEA revealed enrichment of pathways related to cell cycle regulation among high-risk samples and metabolic processes in the low-risk group. Our 10-gene model is robust for prognosis prediction and may help inform clinical management of HCC.
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spelling pubmed-73721352020-08-04 A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma Zhou, Taicheng Cai, Zhihua Ma, Ning Xie, Wenzhuan Gao, Chan Huang, Mengli Bai, Yuezong Ni, Yangpeng Tang, Yunqiang Front Cell Dev Biol Cell and Developmental Biology Hepatocellular carcinoma (HCC) has a dismal long-term outcome. We aimed to construct a multi-gene model for prognosis prediction to inform HCC management. The cancer-specific differentially expressed genes (DEGs) were identified using RNA-seq data of paired tumor and normal tissue. A prognostic signature was built by LASSO regression analysis. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. A 10-gene signature was constructed to stratify the TCGA and ICGC cohorts into high- and low-risk groups where prognosis was significantly worse in the high-risk group across cohorts (P < 0.001 for all). The 10-gene signature outperformed all previously reported models for both C-index and the AUCs for 1-, 3-, 5-year survival prediction (C-index, 0.84 vs 0.67 to 0.73; AUCs for 1-, 3- and 5-year OS, 0.84 vs 0.68 to 0.79, 0.81 to 0.68 to 0.80, and 0.85 vs 0.67 to 0.78, respectively). Multivariate Cox regression analysis revealed risk group and tumor stage to be independent predictors of survival in HCC. A nomogram incorporating tumor stage and signature-based risk group showed better performance for 1- and 3-year survival than for 5-year survival. GSEA revealed enrichment of pathways related to cell cycle regulation among high-risk samples and metabolic processes in the low-risk group. Our 10-gene model is robust for prognosis prediction and may help inform clinical management of HCC. Frontiers Media S.A. 2020-07-14 /pmc/articles/PMC7372135/ /pubmed/32760725 http://dx.doi.org/10.3389/fcell.2020.00629 Text en Copyright © 2020 Zhou, Cai, Ma, Xie, Gao, Huang, Bai, Ni and Tang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Zhou, Taicheng
Cai, Zhihua
Ma, Ning
Xie, Wenzhuan
Gao, Chan
Huang, Mengli
Bai, Yuezong
Ni, Yangpeng
Tang, Yunqiang
A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma
title A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma
title_full A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma
title_fullStr A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma
title_full_unstemmed A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma
title_short A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma
title_sort novel ten-gene signature predicting prognosis in hepatocellular carcinoma
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372135/
https://www.ncbi.nlm.nih.gov/pubmed/32760725
http://dx.doi.org/10.3389/fcell.2020.00629
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