Characterization of immune landscape and development of a novel N7-methylguanine-related gene signature to aid therapy in recurrent aphthous stomatitis

MicroRNAs (miRNAs) are a recently discovered family of endogenous, noncoding RNA molecules approximately 22 nt in length. miRNAs modulate gene expression post-transcriptionally by binding to complementary sequences in the coding or 3' untranslated region of target messenger RNAs (mRNAs). It is now clear that the biogenesis and function of miRNAs are related to the molecular mechanisms of various clinical diseases, and that they can potentially regulate every aspect of cellular activity, including differentiation and development, metabolism, proliferation, apoptotic cell death, viral infection and tumorgenesis. Here, we review recent advances in miRNA research, and discuss the diverse roles of miRNAs in disease.

Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TF-binding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.