A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes

Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories

The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver.

Identification of Radiation-Induced miRNA Biomarkers Using the CGL1 Cell Model System

MicroRNAs (miRNAs) have emerged as a potential class of biomolecules for diagnostic biomarker applications. miRNAs are small non-coding RNA molecules, produced and released by cells in response to various stimuli, that demonstrate remarkable stability in a wide range of biological fluids, in extreme pH fluctuations, and after multiple freeze–thaw cycles. Given these advantages, identification of miRNA-based biomarkers for radiation exposures can contribute to the development of reliable biological dosimetry methods, especially for low-dose radiation (LDR) exposures. In this study, an miRNAome next-generation sequencing (NGS) approach was utilized to identify novel radiation-induced miRNA gene changes within the CGL1 human cell line. Here, irradiations of 10, 100, and 1000 mGy were performed and the samples were collected 1, 6, and 24 h post-irradiation. Corroboration of the miRNAome results with RT-qPCR verification confirmed the identification of numerous radiation-induced miRNA expression changes at all doses assessed. Further evaluation of select radiation-induced miRNAs, including miR-1228-3p and miR-758-5p, as well as their downstream mRNA targets, Ube2d2, Ppp2r2d, and Id2, demonstrated significantly dysregulated reciprocal expression patterns. Further evaluation is needed to determine whether the candidate miRNA biomarkers identified in this study can serve as suitable targets for radiation biodosimetry applications.