A wide range of approaches can be used to detect micro RNA (miRNA)–target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

Arrhythmogenic cardiomyopathy is an inherited entity characterized by irregular cell–cell adhesion, cardiomyocyte death and fibro-fatty replacement of ventricular myocytes, leading to malignant ventricular arrythmias, contractile dysfunction and sudden cardiac death. Pathogenic variants in genes that encode desmosome are the predominant cause of arrhythmogenic cardiomyopathy. Moreover, signalling pathways such as Wnt/ß-catenin and transforming growth factor-β have been involved in the disease progression. However, still little is known about the molecular pathophysiological mechanisms that underlie arrhythmogenic cardiomyopathy pathogenesis. We used mRNA and small RNA sequencing to analyse the transcriptome of health and arrhythmogenic cardiomyopathy of autopsied human hearts. Our results showed 697 differentially expressed genes and eight differentially expressed miRNAs. Functional enrichment revealed mitochondrial respiratory-related pathways, impaired response to oxidative stress, apoptotic signalling pathways and inflammatory response-related and extracellular matrix response pathways. Furthermore, analysis of the miRNA–mRNA interactome identified eleven negatively correlated miRNA-target pairs for arrhythmogenic cardiomyopathy. Our finding revealed novel arrhythmogenic cardiomyopathy-related miRNAs with important regulatory function in disease pathogenesis, highlighting their value as potential key targets for therapeutic approaches.

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.