Abstract

The identification of colorectal cancer (CRC) molecular subtypes has prognostic and potentially diagnostic value for patients, yet reliable subtyping remains unavailable in the clinic. The current consensus molecular subtype (CMS) classification in colorectal cancers is based on complex RNA expression patterns quantified at gene level. The clinical application of these methods, however, is challenging due to high uncertainty of single sample classification and associated costs. Alternative splicing (AS), which strongly contributes to transcriptome diversity, has rarely been utilized for tissue type classification. Here, we present an AS-based CRC subtyping framework sensitive to differential exon usage that can be adapted for clinical application.