A Prognostic Model Based on RNA Binding Protein Predicts Clinical Outcomes in Hepatocellular Carcinoma Patients

Abstract

Dysregulation of RNA binding proteins (RBPs) is closely associated with tumor events. However, the function of RBPs in hepatocellular carcinoma (HCC) has not been fully elucidated. The RNA sequences and relevant clinical data of HCC were retrieved from the The Cancer Genome Atlas (TCGA) database to identify distinct RBPs. Subsequently, univariate and multivariate cox regression analysis was performed to evaluate the overall survival (OS)-associated RBPs. The expression levels of prognostic RBP genes and survival information were analyzed using a series of bioinformatics tool. A total of 365 samples with 1,542 RBPs were included in this study. One hundred and eighty-seven differently RBPs were screened, including 175 up-regulated and 12 down-regulated. The independent OS-associated RBPs of NHP2, UPF3B, and SMG5 were used to develop a prognostic model. Survival analysis showed that low-risk patients had a significantly longer OS and disease-free survival (DFS) when compared to high-risk patients (HR: 2.577, 95% CI: 1.793–3.704, P < 0.001 and HR: 1.599, 95% CI: 1.185–2.159, P = 0.001, respectively). The International Cancer Genome Consortium (ICGC) database was used to externally validate the model, and the OS of low-risk patients were found to be longer than that of high-risk patients (P < 0.001). The Nomograms of OS and DFS were plotted to help in clinical decision making. These results showed that the model was effective and may help in prognostic stratification of HCC patients. The prognostic prediction model based on RBPs provides new insights for HCC diagnosis and personalized treatment.