Multivariate Analysis of RNA Chemistry Marks Uncovers Epitranscriptomics-Based Biomarker Signature for Adult Diffuse...

Area of Science:

• Oncology
• Molecular Biology
• Biochemistry

Background:

• Cancer management requires reliable biomarkers for decision-making and outcome prediction.
• High-throughput molecular profiling has identified biomarker signatures from genomics, transcriptomics, proteomics, and metabolomics.
• Epitranscriptomics, the study of over 100 RNA biochemical modifications, has been underutilized for biomarker discovery despite its role in RNA fate and cancer progression.

Purpose of the Study:

• To develop a novel pipeline for analyzing the epitranscriptomic landscape.
• To identify epitranscriptomics-based biomarker signatures for precision medicine and improved cancer diagnosis.
• To investigate the role of epitranscriptomics in cancer progression and its potential for guiding treatment decisions.

Main Methods:

• Developed a unique pipeline combining multiplex epitranscriptomic analysis using high-performance liquid chromatography-tandem mass spectrometry.
• Integrated statistical multivariate analysis and machine learning approaches.
• Applied the pipeline to a cohort of adult diffuse glioma patients.

Main Results:

• Identified a novel "epitranscriptomics-based signature" in diffuse glioma patients.
• Demonstrated that this signature can discriminate and predict glioma grades with high accuracy.
• Showcased the co-evolution of epitranscriptomics with cancer progression.

Conclusions:

• Epitranscriptomics represents a promising, yet underexplored, source for cancer biomarker discovery.
• The developed pipeline and identified signature offer new prospects for precision medicine and personalized oncology.
• This study highlights the potential of epitranscriptomics to significantly improve disease diagnosis and treatment outcome prediction.