J Cancer 2020; 11(14):4132-4144. doi:10.7150/jca.40621

Research Paper

The Prediction of Survival in Hepatocellular Carcinoma Based on A Four Long Non-coding RNAs Expression Signature

Zongxing Yang1*, Yuhan Yang2*, Gang Zhou2, Yan Luo2, Wenjun Yang2, Youliang Zhou2, Jin Yang2✉

1. The Second Department of Infectious Disease, Xixi Hospital of Hangzhou, the Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310023, P.R. China.
2. Center for Translational Medicine, the affiliated hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang 310015, P.R. China.
* These authors contributed equally to this work.

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Citation:
Yang Z, Yang Y, Zhou G, Luo Y, Yang W, Zhou Y, Yang J. The Prediction of Survival in Hepatocellular Carcinoma Based on A Four Long Non-coding RNAs Expression Signature. J Cancer 2020; 11(14):4132-4144. doi:10.7150/jca.40621. Available from http://www.jcancer.org/v11p4132.htm

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Abstract

Prognostic stratification in hepatocellular carcinoma (HCC) patients is still challenging. Long non-coding RNAs (lncRNAs) have been proven to play a crucial role in tumorigenesis and progression of cancers. The aim of this study is to develop a useful prognostic index based on lncRNA signature to identify patients at high risk of disease progression. We obtained lncRNA expression profiles from three publicly available datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). By the risk scoring method, we built an individualized four-lncRNA signature (HCCLnc-4) to predict survival of HCC patients in the discovery set (ROC curve, AUC: 0.83, 95% CI: 0.65-1.00, P < 0.05, Kaplan-Meier analysis and log-rank test, P < 0.01). Similar prognostic value of HCCLnc-4 has been further verified in two other independent sets. Stratified analysis and multivariate Cox regression analysis suggested the independence of HCCLnc-4 for prediction of HCC patient survival from traditional clinicopathological factors. Area under curve (AUC) analysis suggested that HCCLnc-4 could compete sufficiently with, or might be even better than classical pathological staging systems to predict HCC patient prognosis in the same data sets. Functional analysis and network analysis suggested the potential implication of lncRNA biomarkers. Our study developed and validated the lncRNA prognostic index of HCC patients, warranting further clinical evaluation and preventive interventions.

Keywords: Hepatocellular carcinoma, Prognosis, lncRNA, Gene expression signature.