RNA Sequencing in Biomarker Identification: Introduction, Methods, and Advantages

Introduction

Transcriptomics is a sector of bioinformatics that is constantly evolving and growing in terms of biomarker discovery. RNA-sequencing (RNA-seq) is a branch of transcriptomics that allows for the discovery of new genes. From voluminous transcriptomic data, this method aids in the identification of differentially expressed genes or transcripts (DEGs) affiliated with a trait of interest. Biological and biomedical researchers had previously used microarray technology to find candidate genes and differentially expressed markers between two or more groups of interest. In recent years, high-throughput next-generation sequencing (NGS) of cDNA (RNAseq) technology has been used to generate massive amounts of transcriptomic data, resulting in RNA-seq count data for further analysis. This strategy also contains the identification of disease biomarkers that may be useful in the diagnosis of various types and subtypes of diseases, with implications for prognosis and treatment. By facilitating a huge spectrum of novel applications, such as identifying alternative splicing isoforms, identifying novel genes, gene promoters, isoforms, and allele-specific expression, this sequence-based technology has expanded the scope of transcriptome research.

Figure 1. Identification of potential secretory circRNA biomarker candidates using RNA-Seq. (Luo, 2020)

Methods

Traditionally, quantitative polymerase chain reaction (qPCR) and gene expression (GEX) arrays have been used to discover and profile RNA-based drug response biomarkers. For focused assessment, qPCR is a highly sensitive, reliable, and simple-to-use platform. Individual targets of interest or, in many cases, multi-assay panels are frequently queried with it. The latter frequently concentrate on drug metabolism functional pathways and/or the mechanism of action of the compound class under investigation.

The scope of information that qPCR can offer is its primary limitation as a biomarker discovery tool. This is due in part to practical constraints in the number of assays that can be run in parallel, and in part to the fact that assays must be pre-designed against particular targets, limiting the number of assays that can be run simultaneously. The project leader is compelled to prioritize which potential gene sets and functional pathways to focus on when designing a biomarker discovery study, and thus risks limiting the potential paths to a successful outcome.

The GEX arrays have supplied an additional platform for transcriptome-scale analysis. The use of GEX arrays expands biomarker discovery beyond a limited set of functional pathways, allowing researchers to gain a better comprehension of how compound administration impacts the transcriptome. The extensive collection of datasets accessible in the public domain reflects the widespread use and applicability of GEX arrays.

Advantages

RNA-Seq has quickly gained popularity as a tool for discovering and profiling compound response biomarkers. It casts a wider net as a discovery tool than other methods available. This is due to two complementary characteristics: high detection sensitivity at both ends of the expression spectrum and the ability to discern both known and novel features in a single assay, such as gene fusions and alternative transcripts.

In comparison to the three logs typical of GEX arrays, RNA-Seq has been demonstrated to offer five logs of dynamic range. The resulting 100-fold increase in sensitivity aids in the capture of candidate biomarkers at the extremes of the abundance spectrum and allows for accurate differential expression measurement.

References:

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