One of the main challenges in cancer management relates
to the
discovery of reliable biomarkers, which could guide decision-making
and predict treatment outcome. In particular, the rise and democratization
of high-throughput molecular profiling technologies bolstered the
discovery of “biomarker signatures” that could maximize
the prediction performance. Such an approach was largely employed
from diverse OMICs data (i.e., genomics, transcriptomics, proteomics,
metabolomics) but not from epitranscriptomics, which encompasses more
than 100 biochemical modifications driving the post-transcriptional
fate of RNA: stability, splicing, storage, and translation. We and
others have studied chemical marks in isolation and associated them
with cancer evolution, adaptation, as well as the response to conventional
therapy. In this study, we have designed a unique pipeline combining
multiplex analysis of the epitranscriptomic landscape by high-performance
liquid chromatography coupled to tandem mass spectrometry with statistical
multivariate analysis and machine learning approaches in order to
identify biomarker signatures that could guide precision medicine
and improve disease diagnosis. We applied this approach to analyze
a cohort of adult diffuse glioma patients and demonstrate the existence
of an “epitranscriptomics-based signature” that permits
glioma grades to be discriminated and predicted with unmet accuracy.
This study demonstrates that epitranscriptomics (co)evolves along
cancer progression and opens new prospects in the field of omics molecular
profiling and personalized medicine.