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

Autor: Jianqiang Ding, Jing Li, Yanling Ouyang, Qingbo Liu, Tingshan He, Peng Wang, Zhiqiao Zhang, Yiyan Huang
Jazyk: angličtina
Rok vydání: 2019
Předmět:
0301 basic medicine
Oncology
Male
Cancer Research
Datasets as Topic
Kaplan-Meier Estimate
0302 clinical medicine
prognostic model
Liver Neoplasms
General Medicine
hepatocellular carcinoma
Middle Aged
Prognosis
Gene Expression Regulation
Neoplastic
H2AFX
Liver
030220 oncology & carcinogenesis
Hepatocellular carcinoma
PFKP
competitive endogenous RNA
Original Article
Female
RNA
Long Noncoding
Biological regulation
Adult
medicine.medical_specialty
Carcinoma
Hepatocellular
mRNA
overall survival
Biology
03 medical and health sciences
Clinical Research
Internal medicine
microRNA
medicine
Biomarkers
Tumor
Humans
RNA
Messenger
Aged
Competing endogenous RNA
Proportional hazards model
Gene Expression Profiling
Cancer
Original Articles
medicine.disease
MicroRNAs
Nomograms
030104 developmental biology
Follow-Up Studies
Zdroj: Cancer Science
ISSN: 1349-7006
1347-9032
Popis: 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/.
Databáze: OpenAIRE
Externí odkaz: