Establishment and verification of a prognostic model of liver cancer by RNA-binding proteins based on the TCGA database.
Article
Apizi, Anwaier, Wang, Lin, Wusiman, Laibijiang et al. (2022). Establishment and verification of a prognostic model of liver cancer by RNA-binding proteins based on the TCGA database.
. Translational Cancer Research, 11(7), 1925-1937. 10.21037/tcr-21-2820
Apizi, Anwaier, Wang, Lin, Wusiman, Laibijiang et al. (2022). Establishment and verification of a prognostic model of liver cancer by RNA-binding proteins based on the TCGA database.
. Translational Cancer Research, 11(7), 1925-1937. 10.21037/tcr-21-2820
Globally, liver cancer is one of the most common malignant tumors and is the third leading cause of cancer deaths. RNA-binding protein (RBP) is a general term for a class of proteins that bind to RNA to regulate metabolic processes. The expression of RNA-binding proteins is related to the prognosis of liver cancer patients.
Methods
The RBP gene expression data of liver cancer were extracted from the TCGA database. First, the differentially expressed RBPs (DE RBPs) were selected through enrichment analysis and volcano mapping. Then, the prognosis-related RBP genes were selected through single-factor Cox regression analysis. The key prognosis-related RBPs were further screened by multifactor Cox regression analysis, and a formula for the patient's risk coefficient was obtained. Finally, based on the patient's risk score, a nomogram was established and verified.
Results
We extracted 374 cancer tissue samples and 50 normal tissue samples with the clinical information from each sample. Through enrichment analysis, we screened 208 upregulated RBPs and 122 downregulated RBPs. Prognosis-related high-risk genes were EEF1E1, NOP56, UPF3B, SF3B4, SMG5, CD3EAP, BRCA1, BARD1, XPO5, CSTF2, EZH2, EXO1, RRP12, PRIM1, LIN28B, NROB1 and TCOF1, and the low-risk genes were MRPL46, RCL1, MRPL54, CPEB3, IFIT5, PPARGC1A, EIF2AK4, SEPSECS, ACO1, SECISBP2 L and ZCCHC24. Further multivariate Cox regression analysis was performed on the prognosis-related RBPs, and the three key prognosis-related RBPs were screened out, which were BARD1, NR0B1 and EIF2AK4. A patient risk coefficient calculation formula was obtained: risk score = (1.207×BARD1 Exp) + (0.483×NR0B1Exp) + (-0.720×EIF2AK4 Exp). Finally, a nomogram was established based on the risk score to predict the survival time of patients from 1 to 5 years.
Conclusions
The nomogram has good predictive value for the survival time of liver cancer patients.
