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Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

BACKGROUND: Evidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors...

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Autores principales: Tian, Siyuan, Liu, Jingyi, Sun, Keshuai, Liu, Yansheng, Yu, Jiahao, Ma, Shuoyi, Zhang, Miao, Jia, Gui, Zhou, Xia, Shang, Yulong, Han, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870868/
https://www.ncbi.nlm.nih.gov/pubmed/33575212
http://dx.doi.org/10.3389/fonc.2020.597996
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author Tian, Siyuan
Liu, Jingyi
Sun, Keshuai
Liu, Yansheng
Yu, Jiahao
Ma, Shuoyi
Zhang, Miao
Jia, Gui
Zhou, Xia
Shang, Yulong
Han, Ying
author_facet Tian, Siyuan
Liu, Jingyi
Sun, Keshuai
Liu, Yansheng
Yu, Jiahao
Ma, Shuoyi
Zhang, Miao
Jia, Gui
Zhou, Xia
Shang, Yulong
Han, Ying
author_sort Tian, Siyuan
collection PubMed
description BACKGROUND: Evidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients. METHODS: Differently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability. RESULTS: The prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis. CONCLUSIONS: The RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.
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spelling pubmed-78708682021-02-10 Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma Tian, Siyuan Liu, Jingyi Sun, Keshuai Liu, Yansheng Yu, Jiahao Ma, Shuoyi Zhang, Miao Jia, Gui Zhou, Xia Shang, Yulong Han, Ying Front Oncol Oncology BACKGROUND: Evidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients. METHODS: Differently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability. RESULTS: The prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis. CONCLUSIONS: The RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making. Frontiers Media S.A. 2021-01-26 /pmc/articles/PMC7870868/ /pubmed/33575212 http://dx.doi.org/10.3389/fonc.2020.597996 Text en Copyright © 2021 Tian, Liu, Sun, Liu, Yu, Ma, Zhang, Jia, Zhou, Shang and Han 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 Oncology
Tian, Siyuan
Liu, Jingyi
Sun, Keshuai
Liu, Yansheng
Yu, Jiahao
Ma, Shuoyi
Zhang, Miao
Jia, Gui
Zhou, Xia
Shang, Yulong
Han, Ying
Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma
title Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma
title_full Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma
title_fullStr Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma
title_full_unstemmed Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma
title_short Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma
title_sort systematic construction and validation of an rna-binding protein-associated model for prognosis prediction in hepatocellular carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870868/
https://www.ncbi.nlm.nih.gov/pubmed/33575212
http://dx.doi.org/10.3389/fonc.2020.597996
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