The abnormal expression of noncoding RNAs has attracted increasing interest in the field of hepatocellular carcinoma progression. However, the underlying molecular mechanisms mediated by noncoding RNAs in these processes are unclear. Here, we obtained the expression profiles of long noncoding RNAs, microRNAs, and mRNAs from the Gene Expression Omnibus database and identified hepatocarcinogenesis-specific differentially expressed transcripts. Next, we identified significant Gene Ontology and pathway terms that the differentially expressed transcripts involved in. Using functional analysis and target prediction, we constructed a hepatocellular carcinoma-associated deregulated competitive endogenous RNA network to reveal the potential mechanisms underlying tumor progression. By analyzing The Cancer Genome Atlas dataset, six key long noncoding RNAs showed significant association with overall survival as well as strong correlation with some microRNAs and mRNAs in the competitive endogenous RNA network. We further validated the above results and determined their diagnostic and prognostic value in clinical samples. Importantly, by large-scale analyses, we identified a cluster of long noncoding RNAs, GBAP1, MCM3AP-AS1, SLC16A1-AS1, C3P1, DIO3OS, and HNF4A-AS1 as candidate biomarkers for the diagnosis and prognosis of hepatocellular carcinoma, which will improve our understanding of competitive endogenous RNA-mediated regulatory mechanisms underlying hepatocellular carcinoma development and will provide novel therapeutic targets in the future.

Bladder cancer (BC) is the ninth most common lethal malignancy worldwide. Great efforts have been devoted to clarify the pathogenesis of BC, but the underlying molecular mechanisms remain unclear. To screen for the genes associated with the progression and carcinogenesis of BC, three datasets were obtained from the Gene Expression Omnibus. A total of 37 tumor and 16 non-cancerous samples were analyzed to identify differentially expressed genes (DEGs). Subsequently, 141 genes were identified, including 55 upregulated and 86 downregulated genes. The protein-protein interaction network was established using the Search Tool for Retrieval of Interacting Genes database. Hub gene identification and module analysis were performed using Cytoscape software. Hierarchical clustering of hub genes was conducted using the University of California, Santa Cruz Cancer Genomics Browser. Among the hub genes, kinesin family member 11 (KIF11) was identified as one of the most significant prognostic biomarkers among all the candidates. The Kaplan Meier Plotter database was used for survival analysis of KIF11. The expression profile of KIF11 was analyzed using the ONCOMINE database. The expression levels of KIF11 in BC samples and bladder cells were measured using reverse transcription-quantitative pCR, immunohistochemistry and western blotting. In summary, KIF11 was significantly upregulated in BC and might act as a potential prognostic biomarker. The present identification of DEGs and hub genes in BC may provide novel insight for investigating the molecular mechanisms of BC.