A novel multidimensional signature predicts prognosis in hepatocellular carcinoma patients

The abnormal expression of microRNAs (miRNAs) or protein‐coding genes (PCGs) have been found to be associated with the prognosis of hepatocellular carcinoma (HCC) patients. Using bioinformatics analysis methods including Cox’s proportional hazards regression analysis, the random survival forest algo...

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Bibliographic Details
Published in:Journal of cellular physiology 2019-07, Vol.234 (7), p.11610-11619
Main Authors: Wang, Song, Zhang, Jian‐Hua, Wang, Huan, Yang, Lu, Hong, Shuai, Yu, Bo, Guo, Jin‐Cheng, Liu, Jing, Zhu, Yan‐Bing
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Bioinformatics
Carcinoma, Hepatocellular - diagnosis
Carcinoma, Hepatocellular - genetics
Datasets
Deoxyribonucleic acid
DNA
DNA polymerase
DNA-directed DNA polymerase
Female
Gene expression
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Genes
Genomes
Hazards
Hepatocellular carcinoma
Humans
Kaplan-Meier Estimate
Liver cancer
Liver Neoplasms - diagnosis
Liver Neoplasms - genetics
Male
Medical prognosis
Metastases
microRNAs
MicroRNAs - genetics
MicroRNAs - metabolism
Middle Aged
miRNA
Multivariate Analysis
Neoplasm Staging
Open Reading Frames - genetics
Patients
Polymerase chain reaction
Prognosis
prognostic
Proportional Hazards Models
Proteins
protein‐coding genes
Rank tests
Regression analysis
Ribonucleic acid
Risk Factors
Risk groups
RNA
signature
Survival
Training
Young Adult
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Summary:The abnormal expression of microRNAs (miRNAs) or protein‐coding genes (PCGs) have been found to be associated with the prognosis of hepatocellular carcinoma (HCC) patients. Using bioinformatics analysis methods including Cox’s proportional hazards regression analysis, the random survival forest algorithm, Kaplan–Meier, and receiver operating characteristic (ROC) curve analysis, we mined the gene expression profiles of 469 HCC patients from The Cancer Genome Atlas (n = 379) and Gene Expression Omnibus (GSE14520; n = 90) public database. We selected a signature comprising one protein‐coding gene (PCG; DNA polymerase μ) and three miRNAs (hsa‐miR‐149‐5p, hsa‐miR‐424‐5p, hsa‐miR‐579‐5p) with highest accurate prediction (area under the ROC curve [AUC] = 0.72; n = 189) from the training data set. The signature stratified patients into high‐ and low‐risk groups with significantly different survival (median 27.9 vs. 55.2 months, log‐rank test, p 
ISSN:0021-9541
1097-4652
DOI:10.1002/jcp.27818