Abstract

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and can be induced by hepatitis B virus (HBV) infection. The aim of the present study was to screen prognosis-associated long noncoding RNAs (lncRNAs) and construct a risk score system for the disease. The RNA-sequencing data of patients with HCC (including 100 HCC samples and 26 normal samples) were extracted from The Cancer Genome Atlas (TCGA) database. In addition, GSE55092, GSE19665 and GSE10186 datasets were downloaded from the Gene Expression Omnibus database. Combined with weighted gene co-expression network analysis, the identification and functional annotation of stable modules was performed. Using the MetaDE package, the consensus differentially expressed RNAs (DE-RNAs) were analyzed. To construct a risk score system, prognosis-associated lncRNAs and the optimal lncRNA combination were separately analyzed by survival and penalized packages. Finally, pathway enrichment analysis for the nodes in an lncRNA-mRNA network was conducted via Gene Set Enrichment Analysis. A total of four stable modules and 3,051 consensus DE-RNAs were identified. The stable modules were significantly associated with the histological grades of HCC, tumor, node and metastasis stage, pathological stage, recurrence and exposure to radiation therapy. A 9-lncRNA optimal combination [DiGeorge syndrome critical region gene 9, glucosidase, β, acid 3 (GBA3), HLA complex group 4, N-acetyltransferase 8B, neighbor of breast cancer 1 gene 2, prostate androgen-regulated transcript 1, ret finger protein like 1 antisense RNA 1, solute carrier family 22 member 18 antisense and T-cell leukemia/lymphoma 6] was selected from the 14 prognosis-associated lncRNAs, and was further supported by the validation dataset, GSE10186. The lncRNA-mRNA co-expression network revealed lncRNA GBA3 as a positive regulator of phosphoenolpyruvate carboxykinase 2, an important enzyme in the metabolic pathway of gluconeogenesis. A risk score system was established based on the optimal 9 lncRNAs, which may be valuable for predicting the prognosis of patients with HBV-positive HCC and improving understanding of mechanisms associated with the pathogenesis of this disease. On the contrary, a larger, independent cohort of patients is required to further validate the risk-score system.

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