MicroRNAs (miRNAs) and their target genes are aberrantly expressed in many cancers and are linked to carcinogenesis and metastasis, especially among hepatocellular carcinoma (HCC) patients. This study sought to identify new biomarkers related to HCC prognosis using small RNA sequencing from the tumor and matched normal adjacent tissue of 32 patients with HCC. Eight miRNAs were downregulated and 61 were upregulated more than twofold. Of these, five miRNAs, hsa-miR-3180, hsa-miR-5589-5p, hsa-miR-490-5p, hsa-miR-137, and hsa-miR-378i, were significantly associated with 5-year overall survival (OS) rates. Differential upregulation of hsa-miR-3180 and downregulation of hsa-miR-378i in tumor samples supported the finding that low and high concentrations of hsa-miR-3180 (p = 0.029) and hsa-miR-378i (p = 0.047), respectively, were associated with higher 5-year OS. Cox regression analyses indicated that hsa-miR-3180 (HR = 0.08; p = 0.013) and hsa-miR-378i (HR = 18.34; p = 0.045) were independent prognostic factors of poor survival. However, high hsa-miR-3180 expression obtained larger AUCs for OS and progression-free survival (PFS) and had better nomogram prediction than hsa-miR-378i. These findings indicate that hsa-miR-3180 may be associated with HCC progression and could serve as a potential biomarker for this disease.

AddictGene and its easy-to-use web interface was built by integrating gene expression, gene-gene interaction, gene-drug interaction and epigenetic regulatory annotation of differentially expressed genes associated with abused substances. The common pathways and genes of multiple drugs irrespective of stages of addiction was identified.

Background: Methionine has been proven to inhibit addictive behaviors of cocaine dependence. However, the mechanism of methionine response to cocaine CPP is unknown. Recent evidence highlights piRNAs to regulate genes via a miRNA‐like mechanism. Here, next‐generation sequencing is used to study mechanism on methionine response to drug‐induced behaviors though piRNA.Methods: l‐methionine treatment cocaine CPP animal model was used to do non‐coding RNA sequencing. There were four groups to sequence: saline+saline (SS), MET+saline (MS), MET+cocaine (MC), and cocaine+saline. Combining mRNA sequencing data, the network and regulation of piRNA were analyzed with their corresponding mRNA and miRNA.Results: Analysis of the piRNAome reveals that piRNAs inversely regulated their target mRNA genes. KEGG analysis of DE‐piRNA target mRNA genes were enriched in Morphine addiction, GABAergic synapse and Cholinergic synapse pathway. Furthermore, four significantly differential expressed genes Cacna2d3, Epha6, Nedd4l, and Vav2 were identified and regulated by piRNAs in the process of l‐methionine inhibits cocaine CPP. Thereinto, Vav2 was regulated by multiple DE piRNAs by sharing the common sequence: GTCTCTCCAGCCACCTT. Meanwhile, it was found that piRNA positively regulates miRNA and three genes Bcl3, Il20ra, and Insrr were identified and regulated by piRNA through miRNA.Conclusion: The results showed that piRNA negatively regulated target mRNA genes and positively regulated target miRNA genes. Genes located in substance dependence, signal transduction and also nervous functions pathways were identified. When taken together, these data may explain the roles of l‐methionine in counteracting the effects of cocaine CPP via piRNAs.

Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology. Combined with complex nonlinear mapping and self-learning, the BP neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map’s density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters of Sea Urchin mRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressed Sea Urchin genes associated with temperature were determined by calculating the difference in the degree of each gene from different networks. The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes.