Objective: To explore an effective
long non-coding RNA (
lncRNA) signature in predicting the prognosis of
hepatocellular carcinoma through the analysis on
RNA sequencing data of
hepatocellular carcinoma patients and peritumoral tissues in the
Cancer Genome Atlas (TCGA) database. Methods: The clinical characteristics and
RNA sequencing data of 377
hepatocellular carcinoma patients were obtained from TCGA database by the end of February 2018. Then, differentially expressed lncRNAs between 50 pairs of
tumor and peritumoral tissues were explored using student's t-test. Next, a
lncRNA signature was established through LASSO Cox regression analysis. All the patients were divided into four groups (<P(25), P(25)-, P(50)-, ≥P(75)) based on the cut-off quartiles signature. Finally, compared with the control group (<P(25)), the hazard ratios (HRs) of three groups (P(25)-, P(50)-, ≥P(75)) were calculated by using Cox regression. The survival outcomes of patients in the four groups were compared to evaluate the capacity of the
lncRNA signature model. Results: A total of 951 differentially expressed lncRNAs were identified between
tumor and peritumoral tissues. A three-
lncRNA signature, including LNCSRLR, MKLN1-AS and ZFPM2-AS1, was established to predict the prognosis of
hepatocellular carcinoma patients. The outcome suggested that the death risk of the ≥P(75) group was 1.57 times larger than that of the <P(25) group (95%CI: 1.06-2.31, P<0.05). Conclusion: The three-
lncRNA signature, which established by LNCSRLR, MKLN1-AS and ZFPM2-AS1, was significantly associated with the prognosis of
hepatocellular carcinoma patients based on TCGA database data.