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Updated: Aug 17, 2025
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
Published on: September 28, 2017
Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma
1Department of General Surgery, Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China. aacj2020@163.net.
Objective:
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC.
Materials And Methods:
Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves.
Results:
Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients.
Conclusions:
The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC.
