[Exploring of a prognostic long non-coding RNA signature of hepatocellular carcinoma by using public database]

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.