Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease.

Jennifer L Major,Julie Pires Da Silva,Rushita A Bagchi

doi:10.3390/mps4010005

Jennifer L Major, Julie Pires Da Silva + Show 1 more

Open Access

https://doi.org/10.3390/mps4010005

Copy DOI

Abstract

Over the past two decades, it has become increasingly evident that microRNAs (miRNA) play a major role in human diseases such as cancer and cardiovascular diseases. Moreover, their easy detection in circulation has made them a tantalizing target for biomarkers of disease. This surge in interest has led to the accumulation of a vast amount of miRNA expression data, prediction tools, and repositories. We used the Human microRNA Disease Database (HMDD) to discover miRNAs which shared expression patterns in the related diseases of ischemia/reperfusion injury, coronary artery disease, stroke, and obesity as a model to identify miRNA candidates for biomarker and/or therapeutic intervention in complex human diseases. Our analysis identified a single miRNA, hsa-miR-21, which was casually linked to all four pathologies, and numerous others which have been detected in the circulation in more than one of the diseases. Target analysis revealed that hsa-miR-21 can regulate a number of genes related to inflammation and cell growth/death which are major underlying mechanisms of these related diseases. Our study demonstrates a model for researchers to use HMDD in combination with gene analysis tools to identify miRNAs which could serve as biomarkers and/or therapeutic targets of complex human diseases.

Highlights

The majority of the genome is transcribed, only a small percentage of it is translated into protein-coding RNA, the remaining being generally classified as non-coding RNAs
It has become evident that miRNAs and the RNA inducing silencing complex (RISC) complex can be detected in the nucleus where they can regulate transcriptional activation, RNA processing events such as alternative splicing, and ribosome biogenesis [7]
Due to a surge in new data, the authors have made over 30 updates to the database in the last twelve years including a second version (HMDD v2.0) in 2014 [14] and the latest version (HMDD v3.0) was released in 2019 with double Methods Protoc. 2021, 4, x https://doi.othrge/1a0m.33o9u0/nxxtxoxfxmiRNAs classified into six main categories based on experimental evid4enofc1e1 [13]

Summary

Introduction

The majority of the genome is transcribed, only a small percentage of it is translated into protein-coding RNA, the remaining being generally classified as non-coding RNAs. Based on size, noncoding RNA can be divided into short noncoding RNA (including microRNA, siRNA, snoRNA, piRNA, and pRNAs) and long non-coding RNA (reviewed in [1]) The discovery of these novel molecules in the early 1990s and their biological activity in the early 2000s shattered the central dogma and led to an explosion of research revealing their importance in numerous disease pathophysiologies [2,3,4,5]. MiRNAs are ~22 nt long RNA molecules which act as a guide for regulatory proteins to bind to messenger RNAs (mRNA) and other non-coding RNAs via a complementary seed sequence (~6–8 nt) [6]. MiRNAs can be secreted from the cell and act as endocrine and paracrine signaling molecules, making them a tantalizing target for disease biomarkers [8]

Methods

Results

Conclusion