sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses.

MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients' prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.

Genome-wide analysis of clustering patterns and flanking characteristics for plant microRNA genes.

MicroRNAs (miRNAs) have been proven to play important roles at the post-transcriptional level in animals and plants. To investigate clustering patterns and specific sequence characteristics in the flanking regions of plant miRNA genes, we performed genome-wide analyses of Arabidopsis thaliana, Populus trichocarpa, Oryza sativa and Sorghum bicolor. Our results showed that miRNA pair distances were significantly higher than would have been expected to occur at random and that the number of miRNA gene pairs separated by very short distances of < 1 kb was higher than of protein-coding gene pairs. Analysis of the promoter architecture of different miRNA genes in plants revealed significant differences in the number and distribution of core promoters between intergenic miRNAs and intragenic miRNAs, and between highly conserved miRNAs and low conserved or nonconserved miRNAs. We applied two motif-finding algorithms to search for over-represented, statistically significant sequence motifs, and discovered six species-specific motifs across the four plant species studied. Moreover, we also identified, for the first time, several significantly over-represented motifs that were associated with conserved miRNAs, and these motifs may be useful for understanding the mechanism of origin of new plant miRNAs. The results presented provide a new insight into the transcriptional regulation and processing of plant miRNAs.