Analysis of epitranscriptome for grading of glioma - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Conference Papers Year : 2022

Analysis of epitranscriptome for grading of glioma

Analyse de l'épitranscriptome pour la détermination du grade des gliomes

Abstract

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.
Fichier principal
Vignette du fichier
rivals_glioma_pres_salamanca_e.pdf (3.56 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

lirmm-04287393 , version 1 (15-11-2023)

Licence

Identifiers

  • HAL Id : lirmm-04287393 , version 1

Cite

Eric Rivals. Analysis of epitranscriptome for grading of glioma. Symposium on Cancer genomics and epitranscriptomics: from the bench to the clinic, Comprehensive Cancer Center (CIC), University of Salamanca, Nov 2022, Salamanca, Spain. ⟨lirmm-04287393⟩
16 View
6 Download

Share

More