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Yan Zheng,1,* Jian Kong,1,* Qun Li,2 Yan Wang,1 Jie Li1 1Department of Geriatrics, The First Hospital of Jilin University, Changchun 130021, Jilin, China; 2Department of Thyroid Surgery, The First Hospital of Jilin University, Changchun 130021, Jilin, China *These authors contributed equally to this work Purpose: The aim of this study was to explore the role of miRNAs in the process of skeletal muscle aging. Materials and methods: We analyzed the miRNA microarray datasets from 19 young and 17 old skeletal muscle samples by bioinformatic analysis. Differentially expressed miRNAs were identified, followed by function and pathway enrichment analysis. The expression of miRNAs were validated by real-time quantitative PCR (RT-qPCR) analysis. Results: A total of 23 miRNAs were found to be differentially expressed in old muscle samples based on two platforms. Gene targets of upregulated miRNAs were significantly enriched in the oxytocin signaling pathway, AMP-activated protein kinase (AMPK) signaling pathway, and Notch signaling pathway. The target genes of downregulated miRNAs were significantly related to gap junction, salivary secretion, and estrogen signaling pathway. has-miR-19a and hsa-miR-34a were significant nodes in the miRNA regulatory network. has-miR-19a was closely related to the AMPK signaling pathway. hsa-miR-34a was closely related to cellular senescence and mitogen-activated protein kinase (MAPK) signaling pathway. PCR analysis showed that the expression of has-miR-34a-5p and has-miR-449b-5p was significantly higher in the patient group than in the control group, while no significant difference was observed in the expression of has-miR-19a-3p and has-miR-144-3p between the two groups. Furthermore, the expression of key target genes involved in cellular senescence (sirtuin 1 [SITRI]), MAPK signaling pathway (vascular endothelial growth factor A [VEGFA]), and AMPK signaling pathway (protein kinase AMP-activated catalytic subunit alpha 1 [PRKAA1] and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 [PFKFB3]) were significantly increased in patients with sarcopenia. Conclusion: has-miR-19a and hsa-miR-34a may play regulatory roles in the aging process of skeletal muscles and may be candidate targets to prevent muscle aging. Further experimental validations are warranted. Keywords: skeletal muscle aging, differentially expressed miRNA, pathway, miRNA regulatory network

Background miRNAs regulate the expression of several genes with one miRNA able to target multiple genes and with one gene able to be simultaneously targeted by more than one miRNA. Therefore, it has become indispensable to shorten the long list of miRNA-target interactions to put in the spotlight in order to gain insight into understanding the regulatory mechanism orchestrated by miRNAs in various cellular processes. A reasonable solution is certainly to prioritize miRNA-target interactions to maximize the effectiveness of the downstream analysis. Results We propose a new and easy-to-use web tool MIENTURNET (MicroRNA ENrichment TURned NETwork) that receives in input a list of miRNAs or mRNAs and tackles the problem of prioritizing miRNA-target interactions by performing a statistical analysis followed by a fully featured network-based visualization and analysis. The statistics is used to assess the significance of an over-representation of miRNA-target interactions and then MIENTURNET filters based on the statistical significance associated with each miRNA-target interaction. In addition, the holistic approach of the network theory is used to infer possible evidences of miRNA regulation by capturing emergent properties of the miRNA-target regulatory network that would be not evident through a pairwise analysis of the individual components. Conclusion MIENTURNET offers the possibility to consistently perform both statistical and network-based analyses by using only a single tool leading to a more effective prioritization of the miRNA-target interactions. This has the potential to avoid researchers without computational and informatics skills to navigate multiple websites and thus to independently investigate miRNA activity in every cellular process of interest in an easy and at the same time exhaustive way thanks to the intuitive web interface. The web application along with a well-documented and comprehensive user guide are freely available at http://userver.bio.uniroma1.it/apps/mienturnet/ without any login requirement.

Purpose: This study aimed to identify the underlying mechanisms in pancreatic cancer (PC) carcinogenesis and those as potential prognostic biomarkers, which can also be served as new therapeutic targets of PC. Methods: Differentially expressed genes (DEGs) were identified between PC tumor tissues and adjacent normal tissue samples from a public GSE62452 dataset, followed by functional and pathway enrichment analysis. Then, protein–protein interaction (PPI) network was constructed and prognosis-related genes were screened based on genes in the PPI network, before which prognostic gene-related miRNA regulatory network was constructed. Functions of the prognostic gene in the network were enriched before which Kaplan–Meier plots were calculated for significant genes. Moreover, we predicted related drug molecules based on target genes in the miRNA regulatory network. Furthermore, another independent GSE60979 dataset was downloaded to validate the potentially significant genes. Results: In the GSE62452 dataset, 1017 significant DEGs were identified. Twenty-six important prognostic-related genes were found using multivariate Cox regression analysis. Through pathway enrichment analysis and miRNA regulatory analysis, we found that the 5 genes, such as Interleukin 22 Receptor Subunit Alpha 1 (IL22RA1), BCL2 Like 1 (BCL2L1), STAT1, MYC Proto-Oncogene (MYC), and Signal Transducer And Activator Of Transcription 2 (STAT2), involved in the Jak-STAT signaling pathway were significantly associated with prognosis. Moreover, the expression change of these 5 genes was further validated using another microarray dataset. Additionally, we identified camptothecin as an effective drug for PC. Conclusion: IL22RA1, BCL2L1, STAT1, MYC, and STAT2 involved in the Jak-STAT signaling pathway may be significantly associated with prognosis of PC.

Off-pump coronary artery bypass (OPCAB) surgery is the most effective treatment for coronary heart disease. The aim of this study was to explore the effects of OPCAB on the basis of the associated molecular mechanisms. GSE12486 expression profiles downloaded from the Gene Expression Omnibus database (GEO) were analyzed to identify the differentially expressed genes (DEGs). Principal component analysis (PCA) was conducted based on the expression profiles of DEGs. Function and pathway enrichment of upregulated DEGs was performed, followed by protein-protein interaction (PPI) network construction. Gene Set Enrichment Analysis (GSEA) was used for miRNA enrichment analysis based on expression profiles and prediction of their association with the disease. Cytoscape was applied to construct miRNA regulatory networks of DEGs. In total 64 DEGs were identified, including 63 upregulated and 1 downregulated gene. The first principal component in the PCA analysis was able to distinguish between pre- and post-OPCAB samples. Upregulated DEGs mainly enriched 20 Gene Ontology terms, such as chemokine activity, and 5 pathways including the chemokine signaling pathway. The constructed PPI network contained 234 edges and 55 nodes, and 10 upregulated hub nodes, including FBJ murine osteosarcoma viral oncogene homolog (FOS), were screened. A total of 36 miRNAs, including MIR-224 and MIR-7, were screened by GSEA enrichment analysis. A miRNA regulatory network including 176 edges and 97 nodes was constructed, showing the regulatory relationships between miRNAs and DEGs. For example, early growth response 2 (EGR2) was regulated by 8 miRNAs including MIR-150, MIR-142-3P, MIR-367 and MIR-224. The identified DEGs might play important roles in patients pre- and post-OPCAB surgery via the regulation of associated genes.

Six members of the microRNA-17 (miR-17) family were mapped to three different chromosomes, although they share the same seed sequence and are predicted to target common genes, among which are those encoding hypoxia-inducible factor-1α (HIF1A) and VEGFA. Here, we evaluated the in vivo expression profile of the miR-17 family in the murine retinopathy of prematurity (ROP) model, whereby Vegfa expression is highly enhanced at the early stage of retinal neovascularization, and we found simultaneous reduction of all miR-17 family members at this stage. Using gene reporter assays, we observed binding of these miRs to specific sites in the 3' UTRs of Hif1a and Vegfa. Furthermore, overexpression of these miRs decreased HIF1A and VEGFA expression in vitro. Our data indicate that this miR-17 family elicits a regulatory synergistic down-regulation of Hif1a and Vegfa expression in this biological model. We propose the existence of a coordinated regulatory network, in which diverse miRs are synchronously regulated to target the Hif1a transcription factor, which in turn, potentiates and reinforces the regulatory effects of the miRs on Vegfa to trigger and sustain a significant physiological response.

Background: MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer. Methods: The human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results. Results: Using this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression. Conclusions: Our analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources., Published in at http://dx.doi.org/10.1214/10-AOAS360 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)