Development and Validation of a RNA Binding Protein-Associated Prognostic Model for Hepatocellular Carcinoma.
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ABSTRACT: Background
Dysregulation of RNA binding proteins (RBPs) has been identified in multiple malignant tumors correlated with tumor progression and occurrence. However, the function of RBPs is not well understood in hepatocellular carcinoma (HCC).Methods
The RNA sequence data of HCC was extracted out of the Cancer Genome Atlas (TCGA) database and different RBPs were calculated between regular and cancerous tissue. The study explored the expression and predictive value of the RBPs systemically with a series of bioinformatic analyzes.Results
A total of 330 RBPs, including 208 up-regulated and 122 down-regulated RBPs, were classified differently. Four RBPs (MRPL54, EZH2, PPARGC1A, EIF2AK4) were defined as the forecast related hub gene and used to construct a model for prediction. Further study showed that the high-risk subgroup is poor survived (OS) compared to the model-based low-risk subgroup. The area of the prognostic model under the time-dependent receiver operator characteristic (ROC) curve is 0.814 in TCGA training group and 0.729 in validation group, indicating a strong prognostic model. We also created a predictive nomogram and a web-based calculator (https://dxyjiang.shinyapps.io/RBPpredict/) based on the 4 RBPs and internal validation in the TCGA cohort, which displayed a beneficial predictive ability for HCC.Conclusions
Our results provide new insights into HCC pathogenesis. The 4-RBP gene signature showed a reliable HCC prediction ability with possible applications in therapeutic decision making and personalized therapy.
SUBMITTER: Wang M
PROVIDER: S-EPMC8111555 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
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