The competitive endogenous RNA regulatory network reveals potential prognostic biomarkers for overall survival in hepatocellular carcinoma

• Published in: Cancer science Vol. 110; no. 9; pp. 2905 - 2923
• Main Authors: Zhang, Zhiqiao, Li, Jing, He, Tingshan, Ouyang, Yanling, Huang, Yiyan, Liu, Qingbo, Wang, Peng, Ding, Jianqiang
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.09.2019; John Wiley and Sons Inc
• Subjects: Adult; Aged; Biomarkers; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Carcinoma, Hepatocellular - genetics; Carcinoma, Hepatocellular - mortality; competitive endogenous RNA; Datasets; Datasets as Topic; Female; Follow-Up Studies; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genomes; Hepatocellular carcinoma; Humans; Kaplan-Meier Estimate; Liver - pathology; Liver cancer; Liver Neoplasms - genetics; Liver Neoplasms - mortality; Male; Medical prognosis; MicroRNAs; MicroRNAs - genetics; MicroRNAs - metabolism; Middle Aged; miRNA; Mortality; mRNA; Nomograms; Original; overall survival; Prognosis; prognostic model; Risk factors; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; RNA, Messenger - genetics; RNA, Messenger - metabolism; Studies; Survival; overall survival; mRNA; competitive endogenous RNA; prognostic model; hepatocellular carcinoma
• ISSN: 1347-9032; 1349-7006
• DOI: 10.1111/cas.14138

The aim of the present study is to construct a competitive endogenous RNA (ceRNA) regulatory network by using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with hepatocellular carcinoma (HCC), and to construct a prognostic model for predicting overall survival (OS) of HCC patients. Differentially expressed lncRNAs, miRNAs, and mRNAs were explored between HCC tissues and normal liver tissues. A prognostic model was built for predicting OS of HCC patients and receiver operating characteristic curves were used to evaluate the performance of the prognostic model. There were 455 differentially expressed lncRNAs, 181 differentially expressed miRNAs, and 5035 differentially expressed mRNAs. A ceRNA regulatory network was constructed based on 43 lncRNAs, 37 miRNAs, and 105 mRNAs. Eight mRNA biomarkers (H2AFX, SQSTM1, ITM2A, PFKP, TPD52L1, ACSL4, STRN3, and CPEB3) were identified as independent risk factors by multivariate Cox regression and were used to develop a prognostic model for OS. The C‐indexes in the model group were 0.776 (95% confidence interval [CI], 0.730‐0.822), 0.745 (95% CI, 0.699‐0.791), and 0.789 (95% CI, 0.743‐0.835) for 1‐, 3‐, and 5‐year OS, respectively. The current study revealed potential molecular biological regulation pathways and prognostic biomarkers by the ceRNA regulatory network. A prognostic model based on prognostic mRNAs in the ceRNA network might be helpful to predict the individual mortality risk for HCC patients. The individual mortality risk calculator can be used by visiting the following URL: https://zhangzhiqiao.shinyapps.io/Smart_cancer_predictive_system_HCC/. The current study revealed the potential cellular and molecular biological regulation pathways and prognostic biomarkers by a competitive endogenous RNA (ceRNA) regulatory network. A prognostic model based on mRNAs in the ceRNA network might be helpful to predict the individual mortality risk for overall survival in patients with hepatocellular carcinoma. The web calculator is available at the following URL: https://zhangzhiqiao.shinyapps.io/Smart_cancer_predictive_system_HCC/.