Journal
JOURNAL OF CELLULAR AND MOLECULAR MEDICINE
Volume 22, Issue 12, Pages 5928-5938Publisher
WILEY
DOI: 10.1111/jcmm.13863
Keywords
hepatocellular carcinoma; overall survival; risk score
Categories
Funding
- International Science and Technology Cooperation Projects [2016YFE0107100, 2015DFA30650]
- CAMS Innovation Fund for Medical Science (CIFMS) [2017-I2M-4-003]
- Capital Special Research Project for Health Development [2014-2-4012]
- Beijing Natural Science Foundation [L172055]
- National Ten-thousand Talent Program
- Beijing Science and Technology Cooperation Special Award Subsidy Project
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With the development of new advances in hepatocellular carcinoma (HCC) management and noninvasive radiological techniques, high-risk patient groups such as those with hepatitis virus are closely monitored. HCC is increasingly diagnosed early, and treatment may be successful. In spite of this progress, most patients who undergo a hepatectomy will eventually relapse, and the outcomes of HCC patients remain unsatisfactory. In our study, we aimed to identify potential gene biomarkers based on RNA sequencing data to predict and improve HCC patient survival. The gene expression data and clinical information were acquired from The Cancer Genome Atlas (TCGA) database. A total of 339 differentially expressed genes (DEGs) were obtained between the HCC (n = 374) and normal tissues (n = 50). Four genes (CENPA, SPP1, MAGEB6 and HOXD9) were screened by univariate, Lasso and multivariate Cox regression analyses to develop the prognostic model. Further analysis revealed the independent prognostic capacity of the prognostic model in relation to other clinical characteristics. The receiver operating characteristic (ROC) curve analysis confirmed the good performance of the prognostic model. Then, the prognostic model and the expression levels of the four genes were validated using the Gene Expression Omnibus (GEO) dataset. A nomogram comprising the prognostic model to predict the overall survival was established, and internal validation in the TCGA cohort was performed. The predictive model and the nomogram will enable patients with HCC to be more accurately managed in trials testing new drugs and in clinical practice.
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