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

Siyuan Tian,Miao Zhang,Shuoyi Ma,Jiahao Yu,Gui Jia,Jingyi Liu,Keshuai Sun,Ying Han,Yulong Shang,Yansheng Liu,Xia Zhou

doi:10.3389/fonc.2020.597996

Abstract

BackgroundEvidence 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.MethodsDifferently 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.ResultsThe 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.ConclusionsThe RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.

Highlights

Hepatocellular carcinoma (HCC) is classified as one of the most prevalent cancers globally, posing a serious threat to human health
The prognostic risk model was constructed based on two RNA-binding proteins (RBPs) (BOP1 and enhancer of zeste homolog2 (EZH2)), facilitating risk-stratification of HCC patients
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

Summary

Introduction

Hepatocellular carcinoma (HCC) is classified as one of the most prevalent cancers globally, posing a serious threat to human health. Much progress have been made in the diagnosis and treatment of HCC, 5-year survival rate has not been improved largely due to high rates of recurrence and metastasis [2, 3]. Evidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at posttranscriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. It is not fully understood how RBPs affects the prognosis of HCC. We aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients

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Conclusion