Quantitative Analysis of Isoform Switching in Cancer

Abstract

Over the past 8 years, multiple studies examined the phenomenon of isoform switching in human cancers and discovered that isoform switching is widespread, with hundreds to thousands of such events per cancer type. Although all of these studies used slightly different definitions of isoform switching, which in part led to a rather poor overlap of their results, they all leveraged transcript usage, a proportion of the transcript’s expression in the total expression level of the parent gene, to detect isoform switching. However, how changes in transcript usage correlate with changes in transcript expression is not sufficiently explored. In this article, we adopt the most common definition of isoform switching and use a state-of-the-art tool for the analysis of differential transcript usage, SatuRn, to detect isoform switching events in 12 cancer types. We analyze the detected events in terms of changes in transcript usage and the relationship between transcript usage and transcript expression on a global scale. The results of our analysis suggest that the relationship between changes in transcript usage and changes in transcript expression is far from straightforward, and that such quantitative information can be effectively used for prioritizing isoform switching events for downstream analyses.

Deciphering the Genetic Orchestra: Unveiling Splice Form Dynamics in Human Cell Lines

“…The definition of isoform switching lacks universal consensus as different studies introduce different methodologies tailored to answering specific biological inquiries [5]. Generally, the concept of "isoform switching" entails a comparison of transcriptomic profiles between normal and abnormal types of the same tissue to discern differential transcript usage and expression patterns.…”

“…Detecting switching isoforms necessitates analyzing a substantial number of samples due to inherent heterogeneity even within identical tissue types. While cell lines offer molecular stability, the correlation coefficient between samples from the same cell line in transcript expression analyses is quite moderate, despite similar gene expression profiles [5]. This circumstance obliges the analysis of a larger number of samples of the same cell line.…”

“…Cumulative evidence from large-scale inquiries has delineated numerous splice variants correlating with the pathogenesis of oncological conditions [2,5,6]. The complexity of biological processes is maintained by the variability of expressed genes, as well as by the full or partial duplication of molecular pathways, which prevents any definitive claims about the oncogenicity of a particular splice form.…”

Transcript-Level Analysis of Molecular Alterations Associated With the Development of Premalignant Bronchial Lesions

“…While aforementioned studies have primarily relied on gene expression analysis, an increasing volume of literature underscores the biomedical significance of protein isoforms, as these variants, originating from the same gene, can exhibit diverse biological functions and contribute differently to cellular processes [7][8][9][10], including oncogenesis [11,12]. In this study, we conducted a higherresolution analysis of the transcriptomic profiles of both normal and PML samples, with a specific focus on protein-coding transcripts rather than genes.…”

Transcript-Level Biomarkers of Early Lung Carcinogenesis in Bronchial Lesions

“…While the aforementioned studies have primarily relied on gene expression analysis, an increasing volume of literature underscores the biomedical significance of protein isoforms, as these variants, originating from the same gene, can exhibit diverse biological functions and contribute differently to cellular processes [ 8 , 9 , 10 , 11 ], including oncogenesis [ 12 , 13 ]. In this study, in addition to the traditional gene-based analysis, we conducted a higher-resolution exploration of the transcriptomic profiles of both normal and PML samples, with a specific focus on protein-coding transcripts.…”