A Prognostic Model for Patients with Hepatocellular Carcinoma Based on Hypoxia-Related Long Noncoding RNAs

Abstract

Objective. The prognosis of patients with hepatocellular carcinoma (HCC) varies greatly due to the hypoxic environment and multiple factors driving metastasis progress. In this study, we aimed to correlate the expression of hypoxia-related long noncoding RNAs (lncRNAs) with the survival of patients with HCC to develop a prognostic model for HCC. Methods. The Pearson correlation analysis was used to screen hypoxia-related lncRNAs between differentially expressed lncRNAs by analyzing lncRNA expression profiles in The Cancer Genome Atlas (TCGA) database and 200 hypoxia genes downloaded from the Molecular Signatures Database (M5891). The univariate and multivariate Cox regression analyses were used to identify significantly predictive hypoxia-related lncRNAs, and a prognostic model based on these factors was constructed to predict the survival of HCC. The Kaplan-Meier (K-M) survival analysis and receiver operating characteristic curve (ROC) were performed to evaluate the performance of the model. Results. A total of 490 hypoxia-related lncRNAs were screened out. A prognostic model comprising 10 significantly predictive hypoxia-related lncRNAs was constructed by the multivariate Cox regression analysis. The hypoxia-related risk scores were calculated and were divided into high-risk and low-risk groups. The K-M survival analysis showed a lower overall survival rate of patients in the high-risk group ( P < 0.05 ). ROC analysis showed that the AUC value of hypoxia-related risk score was 0.799, demonstrating that the hypoxia-related risk score was an independent prognosis factor of HCC. Conclusion. Our study indicates that identified 10 key hypoxia-related lncRNAs have potential prognostic values for HCC patients and may provide new targets for the treatment of HCC.