Systematic Analysis of the Function and Prognostic Value of RNA-Binding Proteins in Hepatocellular Carcinoma

Abstract

Background: RNA-binding proteins (RBPs) are abnormally expressed in a variety of malignant tumors and are closely related to tumorigenesis, tumor progression, and prognosis. The role of RBPs in hepatocellular carcinoma (HCC) is unclear. Based on the cancer genome atlas (TCGA) database, we conducted a systematic bioinformatics analysis of abnormally expressed RBPs in HCC, with the aim of identifying the prognostic markers and potential therapeutic targets.Methods: HCC RNA sequencing data downloaded from TCGA database were used to determine the differentially expressed RBPs in livery cancer and normal tissues, followed by performing functional enrichment analysis and visualization of interaction relationships. Univariate and multivariate Cox regression analyses were subsequently used to identify RBPs that were significantly related to the prognosis to construct a prognostic model. The predictive performance of the prognostic model was evaluated by survival analysis and receiver operating characteristic (ROC) curve analysis and verified in the test cohort. Human protein atlas online database was used to verify the expression level of RBPs in the prognostic model.Results: In total, 82 differentially expressed RBPs were identified, including 55 upregulated and 27 downregulated RBPs. Further functional enrichment and interaction analyses showed that the differentially expressed RBPs were mainly related to regulating of mRNA metabolic process, RNA catabolic, mRNA catabolic process, and macromolecule methylation. Five RBP genes, LIN28B, SMG5, PPARGC1A, LARP1B, and ANG were identified as prognostic-related genes and used to construct the prognostic model. The predictive ability of the prognostic model was verified in the test cohort. ROC curve analysis showed that the prognostic model had good sensitivity and specificity. Independent prognostic analysis showed that the risk score may be an independent prognostic factor for HCC.Conclusion: This study constructed a reliable prognostic prediction model by analyzing the differentially expressed RBPs of HCC, facilitating the identification of HCC prognostic biomarkers and therapeutic targets.