Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1783648895310495744 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7870868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tiansiyuan systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT liujingyi systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT sunkeshuai systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT liuyansheng systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT yujiahao systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT mashuoyi systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT zhangmiao systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT jiagui systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT zhouxia systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT shangyulong systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma AT hanying systematicconstructionandvalidationofanrnabindingproteinassociatedmodelforprognosispredictioninhepatocellularcarcinoma |