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Growing evidence implicates that miRNAs can interact with long non-coding RNAs (lncRNAs) to regulate target mRNAs through competitive interactions. However, this mechanism that regulate tumorigenesis and cancer progression remains largely unexplored. Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs), which play a significant role in regulating gene expression. The purpose of our study was to determine potential lncRNA biomarkers to predict the prognosis of HCC by comprehensive analysis of a ceRNA network. The edgeR package was used to obtain the differentially expressed RNA datasets by analyzing 370 HCC tissues and 50 adjacent non-HCC tissues from The Cancer Genome Atlas (TCGA). Through investigating the differentially expressed between HCC tissues and adjacent non-HCC tissues, a total of 947 lncRNAs, 52 miRNAs, and 1,650 mRNAs were obtained. The novel constructed ceRNA network incorporated 99 HCC-specific lncRNAs, four miRNAs, and 55 mRNAs. Survival analysis identified 22 differentially expressed mRNAs, four miRNAs, and nine lncRNAs which were associated with overall survival (OS) time in HCC (p < 0.05), and further exploration was performed to assess the correlation of these differentially expressed genes with tumor stage. The Interpretation of the potential functions of these differentially expressed genes in HCC was realized by Gene ontology (GO) and KEGG pathway enrichment analyses. Seven lncRNAs were confirmed based on univariate Cox regression analysis, lasso COX regression analysis and multivariate Cox regression analysis to construct a predictive model in HCC patients which were related to the prognosis of OS. In summary, ceRNAs contributed to explore the mechanism of tumorigenesis and development, and a model with seven lncRNAs might be potential biomarker to predict the prognosis of HCC. These findings supported the need to studies on the mechanisms involved in the regulation of HCC by ceRNAs.