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Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P -value ⩽.05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P -value ⩽.05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions. [ABSTRACT FROM AUTHOR]

Altered miRNAs were associated with cigarette smoking. The study aimed to examine the gene expression level of plasma let-7a among healthy smokers and compared it with the non-smokers. Forty subjects were recruited for the present study and classified into 21 smokers and 19 non-smokers, age, and sex were matched. The software that used to design functional primers was MIRprimer. Quantitative real-time PCR was employed to compare the relative expression of plasma let-7a. Results showed that the level of let-7a was down-regulated in smokers to 0.34fold ( p  = 0.006) that of the non-smokers. Plasma let-7a showed an area under curve (AUC) of 0.749 with sensitivity 43% and specificity 100%. In conclusion, plasma let-7a was significantly down-regulated in the smokers, and it might be considered a candidate biomarker to discriminate between smokers and non-smokers. [ABSTRACT FROM AUTHOR]

MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA-gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction. The commonly used features are complementarity to the seed region of the microRNA, site accessibility, and evolutionary conservation. Unfortunately, not all microRNA target sites are conserved or adhere to exact seed complementary, and relying on site accessibility does not guarantee that the interaction exists. Moreover, the study of regulatory interactions composed of the same tissue expression data for microRNAs and mRNAs is necessary to understand the specificity of regulation and function. We developed MicroTarget to predict a microRNA-gene regulatory network using heterogeneous data sources, especially gene and microRNA expression data. First, MicroTarget employs expression data to learn a candidate target set for each microRNA. Then, it uses sequence data to provide evidence of direct interactions. MicroTarget scores and ranks the predicted targets based on a set of features. The predicted targets overlap with many of the experimentally validated ones. Our results indicate that using expression data in target prediction is more accurate in terms of specificity and sensitivity. Available at: . [ABSTRACT FROM AUTHOR]

Background MicroRNAs (miRNAs) are endogenous ∼22 nt RNAs that are identified in many species as powerful regulators of gene expressions. Experimental identification of miRNAs is still slow since miRNAs are difficult to isolate by cloning due to their low expression, low stability, tissue specificity and the high cost of the cloning procedure. Thus, computational identification of miRNAs from genomic sequences provide a valuable complement to cloning. Different approaches for identification of miRNAs have been proposed based on homology, thermodynamic parameters, and cross-species comparisons. Results The present paper focuses on the integration of miRNA classifiers in a meta-classifier and the identification of miRNAs from metagenomic sequences collected from different environments. An ensemble of classifiers is proposed for miRNA hairpin prediction based on four well-known classifiers (Triplet SVM, Mipred, Virgo and EumiR), with non-identical features, and which have been trained on different data. Their decisions are combined using a single hidden layer neural network to increase the accuracy of the predictions. Our ensemble classifier achieved 89.3%accuracy, 82.2% measure, 74% sensitivity, 97% specificity, 92.5% precision and 88.2% negative predictive value when tested on real miRNA and pseudo sequence data. The area under the receiver operating characteristic curve of our classifier is 0.9 which represents a high performance index. The proposed classifier yields a significant performance improvement relative to Triplet-SVM, Virgo and EumiR and a minor refinement over MiPred. The developed ensemble classifier is used for miRNA prediction in mine drainage, groundwater and marine metagenomic sequences downloaded from the NCBI sequence reed archive. By consulting the miRBase repository, 179 miRNAs have been identified as highly probable miRNAs. Our new approach could thus be used for mining metagenomic sequences and finding new and homologous miRNAs. Conclusions The paper investigates a computational tool for miRNA prediction in genomic or metagenomic data. It has been applied on three metagenomic samples from different environments (mine drainage, groundwater and marine metagenomic sequences). The prediction results provide a set of extremely potential miRNA hairpins for cloning prediction methods. Among the ensemble prediction obtained results there are pre-miRNA candidates that have been validated using miRbase while they have not been recognized by some of the base classifiers. [ABSTRACT FROM AUTHOR]