Biochimica et Biophysica Acta-Gene Regulatory Mechanisms最新文献

Circular RNAs (circRNAs) are endogenous covalently closed single-stranded RNAs produced by reverse splicing of pre-mRNA. Emerging evidence suggests that circRNAs contribute to cancer progression by modulating the oncogenic STAT3 signaling pathway, which plays key roles in human malignancies. STAT3 signaling-related circRNAs expression appears to be extensively dysregulated in diverse cancer types, where they function either as tumor suppressors or oncogenes. However, the biological effects of STAT3 signaling-related circRNAs and their associations with cancer have not been systematically studied before. Given this, shedding light on the interaction between circRNAs and STAT3 signaling pathway in human malignancies may provide several novel insights into cancer therapy. In this review, we provide a comprehensive introduction to the molecular mechanisms by which circRNAs regulate STAT3 signaling in cancer progression, and the crosstalk between STAT3 signaling-related circRNAs and other signaling pathways. We also further discuss the role of the circRNA/STAT3 axis in cancer chemotherapy sensitivity.

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Importance

Over the past decade, regulatory non-coding RNAs (ncRNAs) produced by RNA Pol II have been revealed as meaningful players in various essential cellular functions. In particular, thousands of ncRNAs are produced at transcriptional regulatory elements such as enhancers and promoters, where they may exert multiple functions to regulate proper development, cellular programming, transcription or genomic stability. Here, we review the mechanisms involving these regulatory element-associated ncRNAs, and particularly enhancer RNAs (eRNAs) and PROMoter uPstream Transcripts (PROMPTs). We contextualize the mechanisms described to the processing and degradation of these short lived RNAs. We summarize recent findings explaining how ncRNAs operate locally at promoters and enhancers, or further away, either shortly after their production by RNA Pol II, or through post-transcriptional stabilization. Such discoveries lead to a converging model accounting for how ncRNAs influence cellular fate, by acting on transcription and chromatin structure, which may further involve factors participating to 3D nuclear organization.