Open Access
Subscription or Fee Access
Using the TCGA Database, Establishing and Validating an RNA-binding Protein-based on Prognostic Model for Liver Cancer
Abstract
Background: The third most common cause of cancer-related death is liver cancer, one of the most prevalent hematological malignancies in the world. The term RNA-binding protein (RBP) refers to a group of proteins that bind to RNA to control metabolic processes. The development of RNA-binding proteins is related to the mortality of liver cancer victims. Methods: RBP gene expression data from liver cancer were extracted using the TCGA database. The differentially expressed RBPs were identified using enrichment analysis and volcano mapping (DE RBPs). Following that, the prognosis-related RBP genes were chosen using single-factor Cox regression analysis. The main prognosis-related RBPs were further selected using multi factor Cox regression analysis, yielding a formula for the patient's risk coefficient. Finally, a nomogram based on the patient's risk score was created and validated. Results: With the clinical information from each sample, we extracted 374 cancer tissue samples and 50 normal tissue samples. We screened 208 upregulated RBPs and 122 down regulated RBPs using enrichment analysis. EEF1E1, NOP56, UPF3B, SF3B4, SMG5, CD3EAP, BRCA1, BARD1, XPO5, CSTF2, EZH2, EXO1, RRP12, PRIM1, LIN28B, NROB1 and TCOF1 were high-risk genes for prognosis, while MRPL46, RCL1, MRPL54, CPEB3, IFIT5, PPARGC1A, EIF2AK On the prognosis-related RBPs, a multivariate Cox regression analysis was done, and the three major prognosis-related RBPs, BARD1, NR0B1, and EIF2AK4, were screened out. The patient risk coefficient was calculated using the formula shown below: risk rating = (1.207BARD1 Exp) + (0.483NR0B1 Exp) + (-0.720EIF2AK4 Exp). Finally, a nomogram based on the risk score was created to predict the survival time of patients from 1 to 5 years. Conclusion: The nomogram has good predictive value for the survival time of liver cancer patients.
References
Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209–49.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7–34.
Zhang T, Liu Z, Zhao X, et al. A novel prognostic score model based on combining systemic and hepatic inflammation markers in the prognosis of HBV-associated hepatocellular carcinoma patients. Artif Cells Nanomed Biotechnol 2019;47:2246–55.
Fu J, Wang H. Precision diagnosis and treatment of liver cancer in China. Cancer Lett 2018;412:283–8.
Pipas JM. DNA Tumor Viruses and Their Contributions to Molecular Biology. J Virol 2019;93:e01524–18.
Wang W, Wang C, Xu H, et al. Aldehyde Dehydrogenase, Liver Disease and Cancer. Int J Biol Sci 2020;16:921–34.
Liu Y, Yang Y, Luo Y, et al. Prognostic potential of PRPF3 in hepatocellular carcinoma. Aging (Albany NY) 2020;12:912–30.
Jiang Y, Chen S, Li Q, et al. TANK-Binding Kinase 1 (TBK1) Serves as a Potential Target for Hepatocellular Carcinoma by Enhancing Tumor Immune Infiltration. Front Immunol 2021;12:612139.
Mohibi S, Chen X, Zhang J. Cancer the 'RBP'eutics RNA-binding proteins as therapeutic targets for cancer. Pharmacol Ther 2019;203:107390.
Gerstberger S, Hafner M, Tuschl T. A census of human RNA-binding proteins. Nat Rev Genet 2014;15:829–45.
11.Neelamraju Y, Gonzalez-Perez A, Bhat-Nakshatri P, et al. Mutational landscape of RNA-binding proteins in human cancers. RNA Biol 2018;15:115–29.
Dang H, Takai A, Forgues M, et al. Oncogenic Activation of the RNA Binding Protein NELFE and MYC Signaling in Hepatocellular Carcinoma. Cancer Cell 2017;32:101–114.e8.
Kang C, Jia X, Liu H. Development and validation of a RNA binding protein gene pair-associated prognostic signature for prediction of overall survival in hepatocellular carcinoma. Biomed Eng Online 2020;19:68.
Zhang X, Zhang J, Gao F, et al. KPNA2-Associated Immune Analyses Highlight the Dysregulation and Prognostic Effects of GRB2, NRAS, and Their RNA-Binding Proteins in Hepatocellular Carcinoma. Front Genet 2020;11:593273.
Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 2015;19:A68–77.
Mayakonda A, Koeffler HP. Maftools: efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. BioRxiv 052662 10.1101/052662. Available online: https://www.biorxiv.org/content/10.1101/052662v1
Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25–9.
Kanehisa M, Sato Y, Kawashima M, et al. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 2016;44:D457–62.
Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021;49:D605–12.
Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498–504.
Wu M, Li X, Zhang T, et al. Identification of a Nine-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Cancer. Front Oncol 2019;9:996.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29–36.
Thul PJ, Åkesson L, Wiking M, et al. A subcellular map of the human proteome. Science 2017;356:eaal3321.
Caruso S, Calatayud AL, Pilet J, et al. Analysis of Liver Cancer Cell Lines Identifies Agents With Likely Efficacy Against Hepatocellular Carcinoma and Markers of Response. Gastroenterology 2019;157:760–76.
Yao M, Yang JL, Wang L, et al. Carcinoembryonic type specific markers and liver cancer immunotherapy. Zhonghua Gan Zang Bing Za Zhi 2020;28:466–70.
Xu RH, Wei W, Krawczyk M, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat Mater 2017;16:1155–61.
Gu Y, Zheng F, Zhang Y, et al. Novel Nomogram Based on Inflammatory Markers for the Preoperative Prediction of Microvascular Invasion in Solitary Primary Hepatocellular Carcinoma. Cancer Manag Res 2022;14:895–907.
Wang Z, Lei X. Matrix factorization with neural network for predicting circRNA-RBP interactions. BMC Bioinformatics 2020;21:229.
Zang J, Lu D, Xu A. The interaction of circRNAs and RNA binding proteins: An important part of circRNA maintenance and function. J Neurosci Res 2020;98:87–97.
Zhang M, Wang T, Xiao G, et al. Large-Scale Profiling of RBP-circRNA Interactions from Public CLIP-Seq Datasets. Genes (Basel) 2020;11:54.
Hentze MW, Castello A, Schwarzl T, et al. A brave new world of RNA-binding proteins. Nat Rev Mol Cell Biol 2018;19:327–41.
Alpatov R, Lesch BJ, Nakamoto-Kinoshita M, et al. A chromatin-dependent role of the fragile X mental retardation protein FMRP in the DNA damage response. Cell 2014;157:869–81.
Zhu YJ, Zheng B, Luo GJ, et al. Circular RNAs negatively regulate cancer stem cells by physically binding FMRP against CCAR1 complex in hepatocellular carcinoma. Theranostics 2019;9:3526–40.
Zhu D, Chen J, Hou T. Development and Validation of a Prognostic Model of RNA-Binding Proteins in Colon Adenocarcinoma: A Study Based on TCGA and GEO Databases. Cancer Manag Res 2021;13:7709–22.
Wu LC, Wang ZW, Tsan JT, et al. Identification of a RING protein that can interact in vivo with the BRCA1 gene product. Nat Genet 1996;14:430–40.
Otsuka K, Yoshino Y, Qi H, et al. The Function of BARD1 in Centrosome Regulation in Cooperation with BRCA1/OLA1/RACK1. Genes (Basel) 2020;11:842.
Son HJ, Choi EJ, Yoo NJ, et al. Somatic frameshift mutations of cancer-related genes KIF3C and BARD1 in colorectal cancers. Pathol Res Pract 2019;215:152579.
Zhang YQ, Pilyugin M, Kuester D, et al. Expression of oncogenic BARD1 isoforms affects colon cancer progression and correlates with clinical outcome. Br J Cancer 2012;107:675–83.
Ring KL, Bruegl AS, Allen BA, et al. Germline multigene hereditary cancer panel testing in an unselected endometrial cancer cohort. Mod Pathol 2016;29:1381–9.
Hu C, Hart SN, Bamlet WR, et al. Prevalence of Pathogenic Mutations in Cancer Predisposition Genes among Pancreatic Cancer Patients. Cancer Epidemiol Biomarkers Prev 2016;25:207–11.
Muscatelli F, Strom TM, Walker AP, et al. Mutations in the DAX-1 gene give rise to both X-linked adrenal hypoplasia congenita and hypogonadotropic hypogonadism. Nature 1994;372:672–6.
Zanaria E, Muscatelli F, Bardoni B, et al. An unusual member of the nuclear hormone receptor superfamily responsible for X-linked adrenal hypoplasia congenita. Nature 1994;372:635–41.
Suntharalingham JP, Buonocore F, Duncan AJ, et al. DAX-1 (NR0B1) and steroidogenic factor-1 (SF-1, NR5A1) in human disease. Best Pract Res Clin Endocrinol Metab 2015;29:607–19.
Zhang H, Slewa A, Janssen E, et al. The prognostic value of the orphan nuclear receptor DAX-1 (NROB1) in nodenegative breast cancer. Anticancer Res 2011;31:443–9.
Ma L, Bao R. Pulmonary capillary hemangiomatosis: a focus on the EIF2AK4 mutation in onset and pathogenesis. Appl Clin Genet 2015;8:181–8.
Best DH, Sumner KL, Smith BP, et al. EIF2AK4 Mutations in Patients Diagnosed with Pulmonary Arterial Hypertension. Chest 2017;151:821–8.
Huang Y, Chen S, Qin W, et al. A Novel RNA Binding Protein-Related Prognostic Signature for Hepatocellular Carcinoma. Front Oncol 2020;10:580513.
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Research & Reviews: A Journal of Bioinformatics